The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index; 2 of the 89 sources its claims cite were checked for support.
Exploiting machine learning for end-to-end drug discovery and development
Open accessAbstract
A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting.This Perspective describes the application of machine learning models in the design, synthesis and characterisation of molecules at different stages in the drug discovery and development process.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This review argues that machine learning can be integrated end-to-end across drug discovery and development, leveraging large public datasets and models for bioactivity, ADME/Tox, repurposing, and nanomedicine. It highlights that deep neural networks often excel in cross-validation but do not consistently outperform other methods on external testing, while data readiness, accessibility, and interpretability remain barriers.
Key findings
- The paper proposes an end-to-end (E2E) vision in which machine learning models span the R&D pathway to predict clinical viability, but notes that linear combinations of models may accumulate errors depending on each model’s accuracy.
- In cited comparisons, DNNs ranked higher than all other machine learning methods across all datasets after 4-fold cross validation using FCFP6 fingerprints; for 18,886 compounds screened against *Mycobacterium tuberculosis*, DNNs and support vector machines appeared superior for training and 5-fold cross validation, but external testing of DNN models with a large test set did not perform as well as other methods. For estrogen receptor datasets, DNNs had higher accuracy within the training set in 5-fold cross validation, while external test predictions were similar between DNN and most classic machine learning models.
- Public chemical and biological data in PubChem, ChEMBL, and other databases now include millions of molecules and bioactivities; tuberculosis and malaria have high-throughput screening datasets of >300,000 compounds, and rare diseases are defined as affecting less than 200,000 people in the United States.
- Applications cited include repurposing topiramate for inflammatory bowel disease, imipramine, bepridil, promethazine, and cimetidine as non-small cell lung cancer inhibitors, and CDRscan identifying 14 oncology and 23 non-oncology drugs with new potential cancer indications; a random forest classifier identified category C drugs likely harmful or safe for fetal loss or congenital anomalies.
Methods
- Design: This is a review/perspective article, not a primary experimental study; it synthesizes literature and the authors’ prior cheminformatics efforts.
- Data or sample: It cites public databases such as PubChem and ChEMBL, high-throughput screening datasets, and prior datasets including 18,886 *M. tuberculosis* compounds, estrogen receptor datasets, and >300,000 compounds for tuberculosis and malaria.
- Main analysis: Narrative synthesis and discussion of machine learning algorithms, molecular descriptors, validation strategies, and drug discovery applications; no systematic review or meta-analysis methodology is stated.
Limitations
- The paper states that many databases are not “model-ready” or machine-readable and require expert curation; DNNs are not “plug and play” and take significant time to optimize; there is no agreement on the best algorithm; DNNs have rarely been used for prospective prediction; generative DNN proof-of-concept studies often have not synthesized molecules to validate predictions; nanoparticle datasets are small (10–100s of molecules), limiting deep learning; and linear model combinations may accumulate errors.
- Evident from the text: as a review, it reports no new primary data or prospective validation of its own, and its conclusions depend on the cited studies rather than a systematic search or quantitative synthesis.
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Claims the paper makes
66 Not checked yet · 23 Own interpretation · 4 No basis given · 1 Supported · 1 Partly supported · 1 Own result · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“we subscribe to the concept that the limits of machine learning are likely to be exposed by experimental data inconsistency and dataset size, rather than the flaws of any individual modeling framework12.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Learning from history
What the sources say
- Partly supportedcites [12] Chen, H., Engkvist, O., Wang, Y., Olivecrona, M. & Blaschke, T. The rise of deep learning in drug discovery. Drug Discov. Today. 23, 1241–1250 (2018). sourceExperimental — accuracy not yet measured
The Concluding remarks say that experimental uncertainty and dataset size could limit machine-learning performance rather than the specific method used. But the paper makes this point conditionally—if different methods achieve roughly the same accuracy—and says “could,” whereas the citing sentence presents it as likely without that condition. No page number is available in our copy. (LLM, conf=0.85)
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Machine learning can help to assess whether a drug can be repositioned for a novel indication73”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine Learning repurposing
What the sources say
- Supportedcites [73] Schadt, E. E., Buchanan, S., Brennand, K. J. & Merchant, K. M. Evolving toward a human-cell based and multiscale approach to drug discovery for CNS disorders. Front. Pharmacol. 5, 252 (2014). source
In the Introduction’s multiscale biology section (page unavailable), the paper describes a computational pattern-matching approach used to identify existing drugs for novel indications, including topiramate for inflammatory bowel disease. It also describes predictive network models used to assess which drug effects might counteract disease-related molecular networks. These examples support the claim that computational learning approaches can help assess repositioning possibilities. (LLM, conf=0.85)
plainconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- Supported
None of the sources cited for this claim have been checked yet.
In the paper
“Several of our own recent cheminformatics prospective testing efforts have identified compounds active in vitro and in vivo against Chagas disease13 and the Ebola virus41 using Bayesian algorithms.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine learning models in action
What the sources say
- Not checked yetcites [13] Ekins, S. et al. Machine learning models and pathway genome data base for trypanosoma cruzi drug discovery. PLoS Negl. Trop. Dis. 9, e0003878 (2015). source
- Not checked yetcites [41] Ekins, S. et al. Machine learning models identify molecules active against Ebola virus in vitro. F1000Research 4, 1091 (2015). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“After 4-fold cross validation and ranked normalized scores of metrics, DNNs ranked higher than all the other machine learning methods across all datasets48.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine learning models in action
What the sources say
- Not checked yetcites [48] Stratton, T. P. et al. Addressing the metabolic stability of antituberculars through machine learning. ACS Med. Chem. Lett. 8, 1099–1104 (2017). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“For external test set predictions DNN and most classic machine learning models perform similarly regardless of dataset or molecular descriptors46.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine learning models in action
What the sources say
- Not checked yetcites [46] Russo, D. P., Zorn, K. M., Clark, A. M., Zhu, H. & Ekins, S. Comparing multiple machine learning algorithms and metrics for estrogen receptor binding prediction. Mol. Pharm. 15, 4361–4370 (2018). source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“For predicting compounds within the training set, DNNs had higher accuracy than other methods in 5-fold cross validation.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine learning models in action
What the sources say
- Not checked yetcites [46] Russo, D. P., Zorn, K. M., Clark, A. M., Zhu, H. & Ekins, S. Comparing multiple machine learning algorithms and metrics for estrogen receptor binding prediction. Mol. Pharm. 15, 4361–4370 (2018). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Conversely, external testing of DNN models with a large test set did not perform as well as other machine learning methods.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine learning models in action
What the sources say
- Not checked yetcites [51] Koutsoukas, A., Monaghan, K. J., Li, X. & Huan, J. Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data. J. Cheminform. 9, 42 (2017). source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“This comparison demonstrated that DNNs and support vector machines appear to be superior methods regardless of the descriptor type for training and 5-fold cross validation.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine learning models in action
What the sources say
- Not checked yetcites [51] Koutsoukas, A., Monaghan, K. J., Li, X. & Huan, J. Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data. J. Cheminform. 9, 42 (2017). source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A variety of machine learning methods such as Naïve Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Abstract
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“as now the costs of development are upwards of $2.8 billion4.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Learning from history
What the sources say
- Not checked yetcites [4] DiMasi, J. A., Grabowski, H. G. & Hansen, R. W. Innovation in the pharmaceutical industry: new estimates of R&D costs. J. Health Econ. 47, 20–33 (2016). source
plain- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 33
- Probably not a substantial claim. 36
None of the sources cited for this claim have been checked yet.
In the paper
“We have also used many different machine learning algorithms and descriptors in parallel to identify the optimum combination46 and address complex problems facing the pharmaceutical industry related to the challenges of improving solubility or metabolic stability47 while retaining bioactivity.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine learning models in action
What the sources say
- Not checked yetcites [46] Russo, D. P., Zorn, K. M., Clark, A. M., Zhu, H. & Ekins, S. Comparing multiple machine learning algorithms and metrics for estrogen receptor binding prediction. Mol. Pharm. 15, 4361–4370 (2018). source
- Not checked yetcites [47] Lusci, A., Pollastri, G. & Baldi, P. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J. Chem. Inf. Model. 53, 1563–1575 (2013). source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our own efforts continue to reflect this pattern, namely that while DNN excels at cross validation assessments it is generally no better than other machine learning methods for external testing.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine learning models in action
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These leverage the generally bigger data sets created from high throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Less than 20 years ago, developing a drug took ~12 years, cost under a billion dollars, and the biggest challenges were failures due to efficacy or toxicity-induced attrition2.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Learning from history
What the sources say
- Not checked yetcites [2] Kola, I. & Landis, J. Can the pharmaceutical industry reduce attrition rates. Nat. Rev. Drug. Discov. 3, 711–715 (2004). source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“2D structures of molecules have seen use as an input to predict toxicity using the Tox21 benchmark set34, which is also part of a platform called MoleculeNet35.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The tipping point for machine learning
What the sources say
- Not checked yetcites [34] Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018). source
- Not checked yetcites [35] Rifaioglu, A. S. et al. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief Bioinform. https://doi.org/10.1093/bib/bby061 (2018). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“These efforts build on the recent proposal that machine learning will impact the future of design, synthesis, characterization and application of molecules and materials10.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Learning from history
What the sources say
- Not checked yetcites [10] Davies, D. W., Butler, K. T., Isayev, O. & Walsh, A. Materials discovery by chemical analogy: role of oxidation states in structure prediction. Faraday Discuss. 211, 553–568 (2018). source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When you can download data in electronic formats, expert curation is always required to ensure compatibility of data and domain expertise is important to build and use machine learning models.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The tipping point for machine learning
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“DNNs have been used in pattern recognition and machine learning25, sparking their use in pharmacology and drug discovery26 and becoming a source for numerous recent reviews12.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The tipping point for machine learning
What the sources say
- Not checked yetcites [25] Schmidhuber, J. Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015). source
- Not checked yetcites [26] Aliper, A. et al. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol. Pharm. 13, 2524–2530 (2016). source
- Not checked yetcites [12] Chen, H., Engkvist, O., Wang, Y., Olivecrona, M. & Blaschke, T. The rise of deep learning in drug discovery. Drug Discov. Today. 23, 1241–1250 (2018). source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“but to date this algorithm has rarely been used for prospective prediction and in some respects this is a limitation of many of the machine learning drug discovery studies published.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The tipping point for machine learning
What the sources say
- Not checked yetcites [32] Altae-Tran, H., Ramsundar, B., Pappu, A. S. & Pande, V. Low data drug discovery with one-shot learning. ACS Cent. Sci. 3, 283–293 (2017). source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“an approach called computational phenotyping has emerged to embrace the complexity inherent in disease mechanisms with machine learning to define accurate phenotypes and has been used to predict antibiotic resistance phenotypes in a variety of bacterial species11.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Learning from history
What the sources say
- Not checked yetcites [11] Drouin, A. et al. Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons. BMC Genom. 17, 754 (2016). source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“We would suggest for many of the targets and diseases we now have plentiful data as indicated by the wide use of ChEMBL and the accuracy of the models generated18.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The tipping point for machine learning
What the sources say
- Not checked yetcites [18] Mayr, A. et al. Large-scale comparison of machine learning methods for drug target prediction on ChEMBL. Chem. Sci. 9, 5441–5451 (2018). source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“While DNN algorithms are increasingly available9, they are not ‘plug and play’ and their use takes significant time to optimize.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The tipping point for machine learning
What the sources say
- Not checked yetcites [9] Zhavoronkov, A. Artificial intelligence for drug discovery, biomarker development, and generation of novel chemistry. Mol. Pharm. 15, 4311–4313 (2018). source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Another way to possibly improve productivity in this complex environment is to implement machine learning across all areas of drug discovery and development9 for which there is sufficient data to train models.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Learning from history
What the sources say
- Not checked yetcites [9] Zhavoronkov, A. Artificial intelligence for drug discovery, biomarker development, and generation of novel chemistry. Mol. Pharm. 15, 4311–4313 (2018). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Models for multiple targets already assist in the prediction of off-target effects14 as well as predicting the most potent compound-target interactions15.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Learning from history
What the sources say
- Not checked yetcites [14] Lampa, S. et al. Predicting off-target binding profiles with confidence using conformal prediction. Front. Pharmacol. 9, 1256 (2018). source
- Not checked yetcites [15] Reker, D., Rodrigues, T., Schneider, P. & Schneider, G. Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus. Proc. Natl Acad. Sci. USA 111, 4067–4072 (2014). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The use of DNNs is still in its relative infancy and has limited applications for cheminformatics as compared with other methods30.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The tipping point for machine learning
What the sources say
- Not checked yetcites [30] Ekins, S. The next era: Deep learning in pharmaceutical research. Pharm. Res. 33, 2594–2603 (2016). source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Potential limitations of a linear combination of models might appear as errors could accumulate depending on the accuracy of each model, which may then influence the overall utility and prediction.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Learning from history
hedgedconditional- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“DNNs have been used in various pharmaceutical applications from docking to virtual screening and beyond (Table 1), but the rise in prominence is linked to increased computational power and the availability of larger datasets.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The tipping point for machine learning
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“much of the interest in them is centered around the flexibility of their architecture27 which allows the generation of models for single task or multitask machine learning28 as well as predicting drug-target interactions29.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The tipping point for machine learning
What the sources say
- Not checked yetcites [27] Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E. & Svetnik, V. Deep neural nets as a method for quantitative structure-activity relationships. J. Chem. Inf. Model. 55, 263–274 (2015). source
- Not checked yetcites [28] Wu, K., Zhao, Z., Wang, R. & Wei, G.-W. TopP–S: Persistent homology-based multi-task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility. J. Comput. Chem. 39, 1444–1454 (2018). source
- Not checked yetcites [29] Wen, M. et al. Deep-learning-based drug-target interaction prediction. J. Proteome Res. 16, 1401–1409 (2017). source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“but the rise in prominence is linked to increased computational power and the availability of larger datasets.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The tipping point for machine learning
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The impact of this viewpoint is that machine learning can be applied for the same cost to identify treatments for a variety of diseases and may level the drug development field for smaller companies and researchers.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Learning from history
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, recent interest in deep learning or deep neural networks (DNNs) for drug discovery has catalyzed interest in machine learning in this field more broadly.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The tipping point for machine learning
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Machine learning models such as support vector machines20, k-Nearest Neighbors21, Naïve Bayes22, Random Forest23 and many other methods24, have long been utilized for drug discovery.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The tipping point for machine learning
What the sources say
- Not checked yetcites [20] Christianini, N. & Shawe-Taylor, J. Support Vector Machines and Other Kernel-Based Learning Methods (Cambridge Univ. Press, 2000). source
- Not checked yetcites [21] Shen, M., Xiao, Y., Golbraikh, A., Gombar, V. K. & Tropsha, A. Development and validation of K-nearest neighbour QSPR models of metabolic stability of drug candidates. J. Med. Chem. 46, 3013–3020 (2003). source
- Not checked yetcites [22] Bender, A. et al. Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure. ChemMedChem 2, 861–873 (2007). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These challenges still persist partly because the datasets may not cover sufficient chemical space, and the test molecules could be outside the applicability of the training set of the models.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine learning models in action
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Machine learning methods in the pharmaceutical industry are most commonly used for virtual screening of compounds, reducing the need to generate more high-throughput screening data by cherry-picking compounds and performing low to medium-throughput screening38.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine learning models in action
What the sources say
- Not checked yetcites [38] McGaughey, G. B. et al. Comparison of topological, shape, and docking methods in virtual screening. J. Chem. Inf. Model. 47, 1504–1519 (2007). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The same machine learning algorithms have been used widely in both pharmaceutical and toxicological research30 (Table 1).”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine learning models in action
What the sources say
- Not checked yetcites [30] Ekins, S. The next era: Deep learning in pharmaceutical research. Pharm. Res. 33, 2594–2603 (2016). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The requirement for expert users in many ways has been the Achilles’ heel of cheminformatics, whereas computational tools for bioinformatics have found broader use due to their accessibility.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The tipping point for machine learning
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Generative DNNs have also been described for the generation virtual libraries of molecules and these enable de novo drug design with optimized properties36.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The tipping point for machine learning
What the sources say
- Not checked yetcites [36] Popova, M., Isayev, O. & Tropsha, A. Deep reinforcement learning for de novo drug design. Sci. Adv. 4, eaap7885 (2018). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We are at a clear tipping point for machine learning and deep learning in particular, but it has taken decades to reach this point and yet full integration of these models is still likely to be a work in progress.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The tipping point for machine learning
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“There are neglected and tropical diseases (e.g. malaria, tuberculosis and others) and rare diseases (defined as affecting less than 200,000 people in the United States) in which interest has increased as the FDA priority voucher52 has provided an incentive for companies to develop new treatments.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Models for all diseases
What the sources say
- Not checked yetcites [52] Lane, T. et al. Comparing and validating machine learning models for mycobacterium tuberculosis drug discovery. Mol. Pharm. 15, 4346–4360 (2018). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While there are several machine learning frameworks and tools available today aimed at using small molecules and related data, they are therefore not at the point where they are universally accessible to all scientists.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The tipping point for machine learning
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“While NIH funding has gone into the high throughput screening54, until recently there have not been comparable efforts on data mining or machine learning with this data16.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Models for all diseases
What the sources say
- Not checked yetcites [54] Ekins, S. et al. Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery. Chem. Biol. 20, 370–378 (2013). source
- Not checked yetcites [16] Kim, S. et al. PubChem substance and compound databases. Nucleic Acids Res. 44, D1202–1213 (2016). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“For example, machine learning methods have been applied to electronic health records to accurately predict multiple medical events from different centers without site-specific data harmonization, with recent data suggesting that deep learning was comparable to regularized logistic regression in this case40.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine learning models in action
What the sources say
- Not checked yetcites [40] Rajkomar, A. et al. Scalable and accurate deep learning with electronic health records. npj Digit. Med. 1, 18 (2018). source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“We have also proposed that machine learning could be used to find treatments for rare diseases using an iterative approach56.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Models for all diseases
What the sources say
- Not checked yetcites [56] Ekins, S. Industrializing rare disease therapy discovery and development. Nat. Biotechnol. 35, 117–118 (2017). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: To date there are no efforts specifically using machine learning to identify drugs for rare diseases that leverage the relevant datasets for targets that are in the public domain.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The fact that DNN does not always outperform other methods for external testing and in several cases is not the best, is important to consider due to the computational cost of DNN.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine learning models in action
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Others have initiated different approaches to combat rare diseases through the development of a comprehensive global genotype-phenotype database58, sharing genomics data59 or other aspects of rare diseases60, as well as assist patients and caregivers61.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Models for all diseases
What the sources say
- Not checked yetcites [58] Chen, B. & Altman, R. B. Opportunities for developing therapies for rare genetic diseases: focus on gain-of-function and allostery. Orphanet. J. Rare Dis. 12, 61 (2017). source
- Not checked yetcites [59] Trujillano, D. et al. A comprehensive global genotype-phenotype database for rare diseases. Mol. Genet. Genomic Med. 5, 66–75 (2017). source
- Not checked yetcites [60] Thompson, R. et al. RD-Connect: an integrated platform connecting databases, registries, biobanks and clinical bioinformatics for rare disease research. J. Gen. Intern. Med. 29, 780–787 (2014). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The closest example to this ideal scenario has purchased close analogs to the molecules generated with generative DNNs and tested them against different kinases at 10μM, identifying several hits37.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The tipping point for machine learning
What the sources say
- Not checked yetcites [37] Putin, E. et al. Adversarial threshold neural computer for molecular de novo design. Mol. Pharm. 15, 4386–4397 (2018). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Other researchers have also compared several machine learning approaches with different datasets from ChEMBL using random split and temporal cross validation to show the superiority of DNNs49, or 5-fold cross validation and leave out 40% as a validation set50.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine learning models in action
What the sources say
- Not checked yetcites [49] Korotcov, A., Tkachenko, V., Russo, D. P. & Ekins, S. Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery datasets. Mol. Pharm. 14, 4462–4475 (2018). source
- Not checked yetcites [50] Lenselink, E. B. et al. Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set. J. Cheminform. 9, 45 (2017). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There is now increasing focus on machine learning in drug discovery which suggests the utility of such approaches is increasing.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Making models more accessible and interpretable
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“These approaches could learn from the work performed on neglected and tropical diseases which has used public datasets to identify new compounds54.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Models for all diseases
What the sources say
- Not checked yetcites [54] Ekins, S. et al. Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery. Chem. Biol. 20, 370–378 (2013). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This is exemplified by the number of deals between start-up machine learning-based drug discovery companies and big pharma, biotechs, or VC investors62.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Making models more accessible and interpretable
What the sources say
- Not checked yetcites [62] Rare Disease InfoHub https://rarediseases.oscar.ncsu.edu (2018).
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“For example, in a classic paradigm inflammatory bowel disease (IBD) signatures were derived from surgical specimens and intersected with Connectivity Map70 data representing transcriptional readouts across a number of cell lines in response to treatment with many hundreds of drugs using a novel pattern-matching algorithm71. From this research the anticonvulsant drug topiramate was identified and”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine Learning repurposing
What the sources say
- Not checked yetcites [70] Baker, N. C., Ekins, S., Williams, A. J. & Tropsha, A. A bibliometric review of drug repurposing. Drug Discov. Today 23, 661–672 (2018). source
- Not checked yetcites [71] Lamb, J. et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As most of the published examples use small datasets (10–100s of molecules), deep learning has limited value and has rarely been applied.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Nanoparticles and nanomedicines
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If we can combine high-quality curated screening data with cutting edge machine learning algorithms and molecular descriptors, there is the opportunity to build models that can be used to reliably predict new molecules for most areas of drug discovery.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Making models more accessible and interpretable
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using machine learning more broadly across the industry could allow us to move beyond the limitations defined by researcher specialty and data silos but it will be important to perform prospective validation of the models to demonstrate progress.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The complete E2E model
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“In this context, we can apply machine learning to the whole pathway to evaluate how a network of protein interactions will react to a perturbation in the system such as the drug that is acting on a particular target and this in turn could lead to more personalized medicine75.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The complete E2E model
What the sources say
- Not checked yetcites [75] Cruz, S. et al. In silico HCT116 human colon cancer cell-based models en route to the discovery of lead-like anticancer drugs. Biomolecules 8, 56 (2018). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Proteins have isoforms and redundancy, so inhibition of one protein might not be enough to elicit the desired response.
- Rests on an assumption: Inhibition of one pathway might not be enough to achieve the response because cells have other pathway mechanisms that would be activated to circumvent the inhibited pathway.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These efforts can enable the quantitative prediction of desirable molecules before synthesis and focus research on experiments with the most promising candidates.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Nanoparticles and nanomedicines
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our thinking need not be limited to discovering just new molecular entities as machine learning can help explore the patterns of known drugs and their interactions with drug targets and potentially repurpose already approved molecules.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine Learning repurposing
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“CRISPR-Cas9, has the potential to edit and renovate the harmful genes for personalized therapy and machine learning methods have been applied to predict the off-targets of CRISPR-Cas9 gene editing77.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The complete E2E model
What the sources say
- Not checked yetcites [77] Chen, R., Liu, X., Jin, S., Lin, J. & Liu, J. Machine learning for drug-target interaction prediction. Molecules 23, 2208 (2018). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Often a major concern about making predictions with computational models is the vastness of chemical space and the potential for selecting compounds with unfavorable properties. These limitations would be mitigated to some extent if previously approved drugs that have extensive ADMET data (or better still have reached phase I trials) could be repurposed.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine Learning repurposing
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: A major concern about making predictions with computational models is the vastness of chemical space and the potential for selecting compounds with unfavorable properties.
None of the sources cited for this claim have been checked yet.
In the paper
“We and others have taken different routes to improve this aspect including tools to highlight contributions of models to test molecules64, identifying training compounds in the same neighborhood as test molecules and scores of model applicability or overlap46, 63.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Making models more accessible and interpretable
What the sources say
- Not checked yetcites [64] Chuang, K. V. & Keiser, M. J. Adversarial controls for scientific machine learning. ACS Chem. Biol. 13, 2819–2821 (2018). source
- Not checked yetcites [46] Russo, D. P., Zorn, K. M., Clark, A. M., Zhu, H. & Ekins, S. Comparing multiple machine learning algorithms and metrics for estrogen receptor binding prediction. Mol. Pharm. 15, 4361–4370 (2018). source
- Not checked yetcites [63] Fleming, N. How artificial intelligence is changing drug discovery. Nature 557, 55–57 (2018). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This leads us to selecting the best targets and may ultimately help us to predict efficacy.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The complete E2E model
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Some approaches using machine learning methods have been developed to detect drug-target interactions76, which is fundamental to both new drug discovery and drug repositioning.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > The complete E2E model
What the sources say
- Not checked yetcites [76] Fröhlich, H. et al. From hype to reality: data science enabling personalized medicine. BMC Med. 16, 150 (2018). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The field of nanomedicine has led to the development of nanoinformatics and the use of data mining and machine learning to develop nano-QSARs to predict functional and structural properties of nanoparticles.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Nanoparticles and nanomedicines
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“There are likely many examples of approved drugs finding additional uses for various diseases54”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Machine Learning repurposing
What the sources say
- Not checked yetcites [54] Ekins, S. et al. Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery. Chem. Biol. 20, 370–378 (2013). source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Quantitative structure-nanoparticle assembly prediction models have been used to generate predictions of nano-assembly which were found to encapsulate drugs with high loadings and have then also been validated in cancer models66.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Nanoparticles and nanomedicines
What the sources say
- Not checked yetcites [66] Jones, D. E., Ghandehari, H. & Facelli, J. C. A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles. Comput. Methods Programs Biomed. 132, 93–103 (2016). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Interestingly, a machine learning method has also been described to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov67.”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > Nanoparticles and nanomedicines
What the sources say
- Not checked yetcites [67] Shamay, Y. et al. Quantitative self-assembly prediction yields targeted nanomedicines. Nat. Mater. 17, 361–368 (2018). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
The biggest cost and most time-consuming component associated with drug development is conducting clinical trials.
“The biggest cost and most time-consuming component associated with drug development is conducting clinical trials.”
The paper relies on this without support.
Currently available chemical and biological data relevant for rare disease drug discovery is available but diffuse, existing in an array of public or private databases.
“Currently available chemical and biological data relevant for rare disease drug discovery is available but diffuse, existing in an array of public or private databases.”
The paper relies on this without support.
To date there are no efforts specifically using machine learning to identify drugs for rare diseases that leverage the relevant datasets for targets that are in the public domain.
“To date there are no efforts specifically using machine learning to identify drugs for rare diseases that leverage the relevant datasets for targets that are in the public domain.”
The paper relies on this without support.
Commercially available tools that could enable scientists to develop nanomedicines using these models have yet to be developed to date.
“Commercially available tools that could enable scientists to develop nanomedicines using these models have yet to be developed to date.”
The paper relies on this without support.
Proteins have isoforms and redundancy, so inhibition of one protein might not be enough to elicit the desired response.
“Proteins have isoforms and redundancy, thus, inhibition of one might not be enough to illicit the desired response.”
The paper relies on this without support.
Inhibition of one pathway might not be enough to achieve the response because cells have other pathway mechanisms that would be activated to circumvent the inhibited pathway.
“In the same way, inhibition of one pathway might not be enough to achieve the response, since the cell has other pathway mechanisms that would be activated to circumvent the one that has been inhibited.”
The paper relies on this without support.
There is currently an abundance of data, powerful and plentiful computing, public and private efforts to develop databases and models, and accessible fee-for-service ways to test compounds.
“We now have an abundance of data, powerful and plentiful computing, public and private efforts to develop databases and models and accessible ways to test compounds on a fee-for-service basis.”
The paper relies on this without support.
A major concern about making predictions with computational models is the vastness of chemical space and the potential for selecting compounds with unfavorable properties.
“Often a major concern about making predictions with computational models is the vastness of chemical space and the potential for selecting compounds with unfavorable properties.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“However, mining of the large-scale chemistry data is needed to efficiently classify potential drug compounds and machine learning techniques have shown great potential [15].”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [15]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > References
“48. Korotcov A, Tkachenko V, Russo DP& Ekins S Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Datasets. Mol Pharm, 14, 4462– 4475 (2018). PMC5741413 29096442”
§ Exploiting Machine Learning for End-To-End Drug Discovery and Development > References
“83. Korotcov A, Tkachenko V, Russo DP& Ekins S Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets. Mol Pharm, 14, 4462– 4475 (2017). 29096442 PMC5741413”
Citations
Cites
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PubChem Substance and Compound databases
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Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr777 · Cited by 4,382
Can the pharmaceutical industry reduce attrition rates?
Kola, I., & Landis, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1470 · Cited by 4,167
How to improve R&D productivity: the pharmaceutical industry's grand challenge
Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd3078 · Cited by 3,566
Innovation in the pharmaceutical industry: New estimates of R&D costs
DiMasi, J. A., Grabowski, H. G., & Hansen, R. W. (2016) · Journal of Health Economics · doi:10.1016/j.jhealeco.2016.01.012 · Cited by 3,359
MoleculeNet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018) · Chemical Science · doi:10.1039/c7sc02664a · Cited by 3,107
Research works printed but not linked: 1
- Korotcov, A., Tkachenko, V., Russo, D. P. & Ekins, S. Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets. Mol. Pharm. 14, 4462–4475 (2017). · DOI
Cited by
Sorted by citations
The rise of artificial intelligence in healthcare applications
Bohr, A., & Memarzadeh, K. (2020) · Artificial Intelligence in Healthcare · doi:10.1016/b978-0-12-818438-7.00002-2 · Cited by 1,636
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473
“ML models like SVM, RF, DNNs, and many others have been used for drug discovery for analyzing the pharmaceuticals applications from docking to VS [437].”
MethodGraph neural networks for materials science and chemistry
Reiser, P., Neubert, M., Eberhard, A., Torresi, L., Zhou, C., Shao, C., Metni, H., van Hoesel, C., Schopmans, H., Sommer, T., et al. (2022) · Communications Materials · doi:10.1038/s43246-022-00315-6 · Cited by 976
Li, Q., Wen, C., Yang, J., Zhou, X., Zhu, Y., Zheng, J., Cheng, G., Bai, J., Xu, T., Ji, J., et al. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00344 · Cited by 814
Green Chemistry in the Synthesis of Pharmaceuticals
Kar, S., Sanderson, H., Roy, K., Benfenati, E., & Leszczyński, J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00631 · Cited by 601
Metadata
- Authors
- Ekins, S., Puhl, A. C., Zorn, K. M., Lane, T. R., Russo, D. P., Klein, J. J., Hickey, A. J., & Clark, A. M.
- Affiliations
- Collaborations Pharmaceuticals (United States) (US)
- Rutgers, The State University of New Jersey (US)
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Ekins, Sean
- ORCID
- Venue
- Nature Materials
- Published
- 2019-04-18
- Volume
- 18
- Issue
- 5
- Pages
- 435-441
- ISSN
- 1476-1122 (ISSN-L)
- 1476-4660
- PMC
- PMC6594828
- OpenAlex
- W2940242941
- Semantic Scholar
- daf468f001c3a5c6f9e667417becb94fa83efb2f
- Cited by
- 578
- References
- 95
- Citations by year
- 2026: 79 · 2025: 85 · 2024: 85 · 2023: 63 · 2022: 80 · 2021: 95 · 2020: 72 · 2019: 19
- FWCI
- 45.24
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Materials (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- Funders
- National Institutes of Health (R44TR000942, UH2TR002084)
- National Institute of General Medical Sciences (R44GM122196)
- National Institute of Allergy and Infectious Diseases (U19AI098461)
- National Institute of Neurological Disorders and Stroke (R43NS107079)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Keywords
- machine learning, drug discovery, drug development, high-throughput screening data, bioactivity prediction, molecular property prediction, drug repurposing, deep neural network, support vector machine, naive Bayesian classifier, molecular design
- MeSH terms
- Machine Learning, Drug Development, Algorithms, Bayes Theorem, Computer Simulation, Humans, Technology, Pharmaceutical, Drug Design, Neural Networks, Computer, Computational Biology, Nanomedicine, Drug Discovery, Drug Repositioning, Support Vector Machine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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