A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials
Open accessAbstract
Over the past decade, the rate of drug attrition due to clinical trial failures has risen substantially. Unfortunately it is difficult to identify compounds that have unfavorable toxicity properties before conducting clinical trials. Inspired by the effective use of sabermetrics in predicting successful baseball players, we sought to use a similar "moneyball" approach that analyzes overlooked features to predict clinical toxicity. We introduce a new data-driven approach (PrOCTOR) that directly predicts the likelihood of toxicity in clinical trials. PrOCTOR integrates the properties of a compound's targets and its structure to provide a new measure, the PrOCTOR score. Drug target network connectivity and expression levels, along with molecular weight, were identified as important indicators of adverse clinical events. Our method provides a data-driven, broadly applicable strategy to identify drugs likely to possess manageable toxicity in clinical trials and will help drive the design of therapeutic agents with less toxicity.
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Efficacy is as complex to predict as idiosyncratic toxicity.
“while efficacy is as complex to predict6.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [6]
Gayvert et al. performed classified toxicity prediction on FDA-approved drugs and failed drugs with RF, using chemical structure, median expression of drug targeted genes, and functional mutations, obtaining AUC about 0.8263.
“Gayvert et al. performed classified toxicity prediction on FDA-approved drugs and drugs that had failed to pass toxicity-tests, with the RF supervised learning algorithm. The learning was from multiple sources: chemical structure characterizations, the median value of the expression of the drug targeted genes from the transcriptome of various tissues, the frequency or possibility of functional”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [157]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464 · see citing works
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139 · see citing works
The igraph software package for complex network research
Csárdi, G., & Nepusz, T. (2006) · Medical Entomology and Zoology · Cited by 10,078 · see citing works
Molecular properties that influence the oral bioavailability of drug candidates
Veber, D. F., Johnson, S. R., Cheng, H.-Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm020017n · Cited by 8,420 · see citing works
Lead- and drug-like compounds: the rule-of-five revolution
Lipinski, C. A. (2004) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2004.11.007 · Cited by 6,339 · see citing works
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062 · see citing works
Effect of Rosiglitazone on the Risk of Myocardial Infarction and Death from Cardiovascular Causes
Nissen, S. E., & Wolski, K. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa072761 · Cited by 4,675 · see citing works
Ghose, A. K., Viswanadhan, V. N., & Wendoloski, J. J. (1998) · Journal of Combinatorial Chemistry · doi:10.1021/cc9800071 · Cited by 3,095 · see citing works
Clinical development success rates for investigational drugs
Hay, M., Thomas, D. W., Craighead, J. L., Economides, C., & Rosenthal, J. (2014) · Nature Biotechnology · doi:10.1038/nbt.2786 · Cited by 2,536 · see citing works
The influence of drug-like concepts on decision-making in medicinal chemistry
Leeson, P. D., & Springthorpe, B. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2445 · Cited by 2,403 · see citing works
Quantifying the chemical beauty of drugs
Bickerton, G. R., Paolini, G. V., Besnard, J., Mureşan, S., & Hopkins, A. L. (2012) · Nature Chemistry · doi:10.1038/nchem.1243 · Cited by 2,301 · see citing works
DrugBank 4.0: shedding new light on drug metabolism
Law, V., Knox, C., Djoumbou-Feunang, Y., Jewison, T., Guo, A. C., Liu, Y., Maciejewski, A., Arndt, D., Wilson, M., Neveu, V., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1068 · Cited by 1,995 · see citing works
Diagnosing the decline in pharmaceutical R&D efficiency
Scannell, J. W., Blanckley, A., Boldon, H., & Warrington, B. (2012) · Nature Reviews Drug Discovery · doi:10.1038/nrd3681 · Cited by 1,981 · see citing works
DrugBank 3.0: a comprehensive resource for 'Omics' research on drugs
Knox, C., Law, V., Jewison, T., Liu, P., Ly, S., Frolkis, A., Pon, A., Banco, K., Mak, C., Neveu, V., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1126 · Cited by 1,832 · see citing works
A side effect resource to capture phenotypic effects of drugs
Kuhn, M., Campillos, M., Letunic, I., Jensen, L. J., & Bork, P. (2010) · Molecular Systems Biology · doi:10.1038/msb.2009.98 · Cited by 899 · see citing works
Moneyball: the art of winning an unfair game
(2004) · Choice Reviews Online · doi:10.5860/choice.41-4733 · Cited by 782 · see citing works
HINT: High-quality protein interactomes and their applications in understanding human disease
Das, J., & Yu, H. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-92 · Cited by 462 · see citing works
Arastu‐Kapur, S., Anderl, J. L., Kraus, M., Parlati, F., Shenk, K. D., Lee, S. H., Muchamuel, T., Bennett, M. K., Driessen, C., Ball, A. J., et al. (2011) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-10-1950 · Cited by 411 · see citing works
Translational research: 4 ways to fix the clinical trial
Ledford, H. K. (2011) · Nature · doi:10.1038/477526a · Cited by 188 · see citing works
Interpretation of Genomic Variants Using a Unified Biological Network Approach
Khurana, E., Fu, Y., Chen, J., & Gerstein, M. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002886 · Cited by 184 · see citing works
Patlewicz, G. Y., & Fitzpatrick, J. M. (2015) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.5b00388 · Cited by 105 · see citing works
Prediction of human population responses to toxic compounds by a collaborative competition
Eduati, F., Mangravite, L. M., Wang, T., Tang, H., Bare, J. C., Huang, R., Norman, T., Kellen, M., Menden, M. P., Yang, J., et al. (2015) · Nature Biotechnology · doi:10.1038/nbt.3299 · Cited by 95 · see citing works
Prediction of Human Population Responses to Toxic Compounds by a Collaborative Competition
Bynagari, N. B. (2017) · Asian Journal of Humanity Art and Literature · doi:10.18034/ajhal.v4i2.577 · Cited by 91 · see citing works
AMBIT RESTful web services: an implementation of the OpenTox application programming interface
Jeliazkova, N., & Jeliazkov, V. (2011) · Journal of Cheminformatics · doi:10.1186/1758-2946-3-18 · Cited by 83 · see citing works
Rosiglitazone: a European regulatory perspective
BLIND, E., Dunder, K., de Graeff, P. A., & Abadie, E. (2010) · Diabetologia · doi:10.1007/s00125-010-1992-5 · Cited by 69 · see citing works
Clinical pharmacodynamic factors in docetaxel toxicity
Puisset, F., Alexandre, J., Tréluyer, J., Raoul, V., Roche, H. H., Goldwasser, F., & Chatelut, É. (2007) · British Journal of Cancer · doi:10.1038/sj.bjc.6603872 · Cited by 42 · see citing works
Pharmacology-based toxicity assessment: towards quantitative risk prediction in humans
Sahota, T., Danhof, M., & Pasqua, O. E. D. (2016) · Mutagenesis · doi:10.1093/mutage/gev081 · Cited by 33 · see citing works
PiHelper: an open source framework for drug-target and antibody-target data
Aksoy, B. A., Gao, J., Dresdner, G., Wang, W., Root, A., Jing, X., Cerami, E. G., & Sander, C. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt345 · Cited by 14 · see citing works
Factors Predicting Docetaxel-Related Toxicity: Experience at a Single Institution
Massacesi, C., Marcucci, F., Rocchi, M., Mazzanti, P., Pilone, A., & Bonsignori, M. (2004) · Journal of Chemotherapy · doi:10.1179/joc.2004.16.1.85 · Cited by 9 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Exome Aggregation Consortium (ExAC) (Cambridge, MA)
Cited by
Sorted by citations
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 · see citing works
A Deep Learning Approach to Antibiotic Discovery
Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.021 · Cited by 2,463 · see citing works
Artificial intelligence in drug discovery and development
Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.10.010 · Cited by 1,695 · see citing works
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 · see citing works
“DeepTox (http:// bioinf. jku. at/ resea rch/ DeepT ox/ tox21. html) [334] and PrOCTOR (https:// github. com/ kgayv ert/ PrOCT OR) [335], are used for prediction of toxicity of new compounds and prediction of the toxicity probability in clinical trials, respectively.”
MethodArtificial intelligence in drug development: Present status and future prospects
Mak, K.-K., & Pichika, M. R. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2018.11.014 · Cited by 1,018 · see citing works
Artificial Intelligence in Cancer Research and Precision Medicine
Bhinder, B., Gilvary, C., Madhukar, N. S., & Elemento, O. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0090 · Cited by 781 · see citing works
Self-citationGeometry-enhanced molecular representation learning for property prediction
Fang, X., Liu, L., Lei, J., He, D., Zhang, S., Zhou, J., Wang, F., Wu, H. R., & Wang, H. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-021-00438-4 · Cited by 606 · see citing works
Exploiting machine learning for end-to-end drug discovery and development
Ekins, S., Puhl, A. C., Zorn, K. M., Lane, T. R., Russo, D. P., Klein, J. J., Hickey, A. J., & Clark, A. M. (2019) · Nature Materials · doi:10.1038/s41563-019-0338-z · Cited by 578 · see citing works
Self-Supervised Graph Transformer on Large-Scale Molecular DataPreprint
Rong, Y., Bian, Y., Xu, T., Xie, W., Wei, Y., Huang, W., & Huang, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.02835 · Cited by 515 · see citing works
Artificial Intelligence for Drug Toxicity and Safety
Basile, A. O., Yahi, A., & Tatonetti, N. P. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.07.005 · Cited by 385 · see citing works
A Comprehensive Survey on Deep Graph Representation Learning
Ju, W., Fang, Z., Gu, Y., Liu, Z., Long, Q., Qiao, Z., Qin, Y., Shen, J., Sun, F., Xiao, Z., et al. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106207 · Cited by 359 · see citing works
Uni-Mol: A Universal 3D Molecular Representation Learning FrameworkPreprint
Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., & Ke, G. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2022-jjm0j-v4 · Cited by 339 · see citing works
Artificial Intelligence in Pharmaceutical and Healthcare Research
Bhattamisra, S. K., Banerjee, P., Gupta, P., Mayuren, J., Patra, S., & Candasamy, M. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7010010 · Cited by 283 · see citing works
Big data in basic and translational cancer research
Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C., & Ruppin, E. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00502-0 · Cited by 254 · see citing works
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction
Cai, H., Zhang, H., Zhao, D., Wu, J., & Wang, L. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac408 · Cited by 257 · see citing works
Wu, Y., & Wang, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19082358 · Cited by 193 · see citing works
A novel molecular representation with BiGRU neural networks for learning atom
Lin, X., Quan, Z., Wang, Z.-J., Huang, H., & Zeng, X. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz125 · Cited by 166 · see citing works
Xu, Z., Ren, F., Wang, P., Cao, J., Tan, C., Ma, D., Zhao, L., Dai, J., Ding, Y., Fang, H., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03743-2 · Cited by 180 · see citing works
“…have demonstrated proof-of-concept innovation and potential to accelerate and decrease costs across all stages of pharmaceutical research and development by facilitating disease-associated target prioritization 3–5 , compound design and optimization 6–10 , and clinical trial performance 11–15 .”
Huanbutta, K., Burapapadh, K., Kraisit, P., Sriamornsak, P., Ganokratanaa, T., Suwanpitak, K., & Sangnim, T. (2024) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2024.106938 · Cited by 185 · see citing works
Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?
Zhavoronkov, A., Vanhaelen, Q., & Oprea, T. I. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1795 · Cited by 159 · see citing works
Toxicity prediction based on artificial intelligence: A multidisciplinary overview
Pérez Santín, E., Rodríguez Solana, R., González García, M., García Suárez, M. D. M., Blanco Díaz, G. D., Cima Cabal, M. D., Moreno Rojas, J. M., & López Sánchez, J. I. (2021) · WIREs Computational Molecular Science · doi:10.1002/wcms.1516 · Cited by 153 · see citing works
Pre-training Molecular Graph Representation with 3D GeometryPreprint
Liu, S., Wang, H., Li, Z., Lasenby, J., Guo, H., & Tang, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07728 · Cited by 221 · see citing works
Advances in Artificial Intelligence (AI)-assisted approaches in drug screening
Singh, S., Gupta, H., Sharma, P., & Sahi, S. (2023) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2023.100039 · Cited by 144 · see citing works
Dzau, V. J., & Hodgkinson, C. P. (2023) · Hypertension · doi:10.1161/hypertensionaha.123.21710 · Cited by 150 · see citing works
Wang, M. W. H., Goodman, J. M., & Allen, T. E. H. (2020) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.0c00316 · Cited by 126 · see citing works
Sun, M., Xing, J., Wang, H., Chen, B., & Zhou, J. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467186 · Cited by 121 · see citing works
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural NetworksPreprint
He, C., Balasubramanian, K., Ceyani, E., Yang, C., Xie, H., Sun, L., He, L., Yang, L., Yu, P. S., Rong, Y., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.07145 · Cited by 171 · see citing works
Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects
Nguyen, P. A., Born, D. A., Deaton, A. M., Nioi, P., & Ward, L. D. (2019) · Nature Communications · doi:10.1038/s41467-019-09407-3 · Cited by 104 · see citing works
Domingo‐Fernándéz, D., Gadiya, Y., Preto, A. J., Krettler, C. A., Mubeen, S., Allen, A., Healey, D., & Colluru, V. (2024) · Journal of Natural Products · doi:10.1021/acs.jnatprod.4c00581 · Cited by 110 · see citing works
Machine learning-integrated omics for the risk and safety assessment of nanomaterials
Ahmad, F., Mahmood, A., & Muhmood, T. (2020) · Biomaterials Science · doi:10.1039/d0bm01672a · Cited by 102 · see citing works
Graph Neural Networks in Modern AI-Aided Drug Discovery
Zhang, O., Lin, H., Zhang, X., Wang, X., Wu, Z., Ye, Q., Zhao, W., Wang, J., Ying, K., Kang, Y., et al. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00461 · Cited by 103 · see citing works
Graph Neural Networks for Molecules
Wang, Y., Li, Z., & Farimani, A. B. (2023) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-031-37196-7_2 · Cited by 100 · see citing works
Aliper, A., Kudrin, R., Polykovskiy, D. A., Kamya, P. R. N., Tutubalina, E. V., Chen, S., Ren, F., & Zhavoronkov, A. A. (2023) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.3008 · Cited by 90 · see citing works
Machine Learning in Drug Discovery and Development Part 1: A Primer
Talevi, A., Morales, J. F., Hather, G. J., Podichetty, J. T., Kim, S., Bloomingdale, P., Kim, S., Burton, J. K., Brown, J. D., Winterstein, A. G., et al. (2020) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12491 · Cited by 86 · see citing works
An integrative machine learning approach for prediction of toxicity-related drug safety
Lysenko, A., Sharma, A., Boroevich, K. A., & Tsunoda, T. (2018) · Life Science Alliance · doi:10.26508/lsa.201800098 · Cited by 88 · see citing works
Next generation radiotheranostics promoting precision medicine
Pomykala, K. L., Hadaschik, B. A., Sartor, O. A., Gillessen, S., Sweeney, C. J., Maughan, T., Hofman, M. S., & Herrmann, K. (2023) · Annals of Oncology · doi:10.1016/j.annonc.2023.03.001 · Cited by 82 · see citing works
Uni-Mol: A Universal 3D Molecular Representation Learning Framework
Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., & Ke, G. (2022) · doi:10.26434/chemrxiv-2022-jjm0j · Cited by 81 · see citing works
Li, H., Zhao, D. Y., & Zeng, J. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539426 · Cited by 78 · see citing works
The Missing Pieces of Artificial Intelligence in Medicine
Gilvary, C. M., Madhukar, N. S., Elkhader, J. A., & Elemento, O. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.06.001 · Cited by 76 · see citing works
Self-citationAI in drug development: a multidisciplinary perspective
Gallego, V., Naveiro, R., Roca, C., Insua, D. R., & Campillo, N. E. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10266-8 · Cited by 86 · see citing works
Qiao, J., Jin, J., Wang, D., Teng, S., Zhang, J., Yang, X., Liu, Y., Wang, Y., Cui, L., Zou, Q., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59634-0 · Cited by 76 · see citing works
Wang, Y., Magar, R., Chen, L., & Farimani, A. B. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00495 · Cited by 74 · see citing works
Chakraborty, C., Bhattacharya, M., Lee, S., Wen, Z., & Lo, Y.-H. (2024) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2024.102295 · Cited by 79 · see citing works
Opportunities and challenges in application of artificial intelligence in pharmacology
Kumar, M., Nguyen, T. P. N., Kaur, J., Singh, T. G., Soni, D., Singh, R., & Kumar, P. (2023) · Pharmacological Reports · doi:10.1007/s43440-022-00445-1 · Cited by 76 · see citing works
Molformer: Motif-Based Transformer on 3D Heterogeneous Molecular Graphs
Wu, F., Radev, D., & Li, S. Z. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25662 · Cited by 77 · see citing works
Recent advances in AI-based toxicity prediction for drug discovery
Lee, H., Kim, J., Kim, J.-W., & Lee, Y. (2025) · Frontiers in Chemistry · doi:10.3389/fchem.2025.1632046 · Cited by 76 · see citing works
Recent applications of quantitative systems pharmacology and machine learning models across diseases
Aghamiri, S. S., Amin, R., & Helikar, T. (2021) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-021-09790-9 · Cited by 66 · see citing works
Revealing cytotoxic substructures in molecules using deep learning
Webel, H. E., Kimber, T. B., Radetzki, S., Neuenschwander, M., Nazaré, M., & Volkamer, A. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00310-4 · Cited by 64 · see citing works
Artificial intelligence in the early stages of drug discovery
Cavasotto, C. N., & Filippo, J. I. D. (2020) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2020.108730 · Cited by 67 · see citing works
Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years
Sultan, A., Sieg, J., Mathea, M., & Volkamer, A. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00747 · Cited by 67 · see citing works
Machine learning prediction of oncology drug targets based on protein and network properties
Dezső, Z., & Ceccarelli, M. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3442-9 · Cited by 64 · see citing works
HINT: Hierarchical interaction network for clinical-trial-outcome predictions
Fu, T., Huang, K., Xiao, C., Glass, L. M., & Sun, J. (2022) · Patterns · doi:10.1016/j.patter.2022.100445 · Cited by 70 · see citing works
Extracting Predictive Representations from Hundreds of Millions of Molecules
Chen, D., Zheng, J., Wei, G., & Pan, F. M. (2021) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.1c03058 · Cited by 60 · see citing works
Predictive modeling of clinical trial terminations using feature engineering and embedding learning
Elkin, M. E., & Xingquan Zhu, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-82840-x · Cited by 58 · see citing works
Key indicators of phase transition for clinical trials through machine learning
Feijoo, F., Palopoli, M., Bernstein, J., Siddiqui, S., & Albright, T. E. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2019.12.014 · Cited by 61 · see citing works
Liu, C., Sun, Y., Davis, R. L., Cardona, S. T., & Hu, P. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00698-9 · Cited by 55 · see citing works
Artificial Intelligence in Drug Formulation and Development: Applications and Future Prospects
Noorain, Srivastava, V., Parveen, B., & Parveen, R. (2023) · Current Drug Metabolism · doi:10.2174/0113892002265786230921062205 · Cited by 55 · see citing works
Cai, C., Fang, J., Guo, P., Wang, Q., Hong, H., Moslehi, J. J., & Cheng, F. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00641 · Cited by 52 · see citing works
Chemical representation learning for toxicity prediction
Born, J., Markert, G., Janakarajan, N., Kimber, T. B., Volkamer, A., Martínez, M. R., & Manica, M. (2023) · Digital Discovery · doi:10.1039/d2dd00099g · Cited by 53 · see citing works
Artificial intelligence for dementia drug discovery and trials optimization
Doherty, T., Yao, Z., Khleifat, A. A. L., Tantiangco, H., Tamburin, S., Albertyn, C., Thakur, L., Llewellyn, D. J., Oxtoby, N. P., Lourida, I., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13428 · Cited by 48 · see citing works
Roughness of Molecular Property Landscapes and Its Impact on Modellability
Aldeghi, M., Graff, D., Frey, N. C., Morrone, J. A., Pyzer‐Knapp, E. O., Jordan, K. E., & Coley, C. W. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00903 · Cited by 48 · see citing works
Kavalci, E., & Hartshorn, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-27416-7 · Cited by 47 · see citing works
Jung, W. J., Goo, S., Hwang, T., Lee, H., Kim, Y., Chae, J., Yun, H., & Jung, S. (2024) · Pharmaceuticals · doi:10.3390/ph17030382 · Cited by 51 · see citing works
Robledo‐Cadena, D. X., Gallardo‐Pérez, J. C., Dávila‐Borja, V. M., Pacheco-Velázquez, S. C., Belmont-Díaz, J., Ralph, S. J., Blanco-Carpintero, B. A., Moreno‐Sánchez, R., & Rodríguez‐Enríquez, S. (2020) · Pharmaceuticals · doi:10.3390/ph13120463 · Cited by 47 · see citing works
A scoping review of artificial intelligence applications in clinical trial risk assessment
Teodoro, D., Naderi, N., Yazdani, A., Zhang, B., & Bornet, A. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01886-7 · Cited by 51 · see citing works
Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS
Tom, G., Hickman, R. J., Zinzuwadia, A. N., Mohajeri, A., Sánchez-Lengeling, B., & Aspuru‐Guzik, A. (2023) · Digital Discovery · doi:10.1039/d2dd00146b · Cited by 47 · see citing works
Li, B., Lin, M., Chen, T., & Wang, L. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad398 · Cited by 45 · see citing works
Seo, S., Kim, Y., Han, H., Son, W., Hong, Z.-Y., Sohn, I., Shim, J., & Hwang, C. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.670670 · Cited by 44 · see citing works
Driving success in personalized medicine through AI-enabled computational modeling
Chakravarty, K., Antontsev, V. G., Bundey, Y., & Varshney, J. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.02.007 · Cited by 43 · see citing works
Machine learning small molecule properties in drug discovery
Schapin, N., Majewski, M., Varela‐Rial, A., Arróniz, C., & Fabritiis, G. D. (2023) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2023.100020 · Cited by 44 · see citing works
In silico off-target profiling for enhanced drug safety assessment
Liu, J., Gui, Y., Rao, J., Sun, J., Wang, G., Ren, Q., Qu, N., Niu, B., Chen, Z., Sheng, X., et al. (2024) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2024.03.002 · Cited by 41 · see citing works
Timpe, C., Stegemann, S., Barrett, A., & Mujumdar, S. K. (2020) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14388 · Cited by 40 · see citing works
Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomesPreprint
Artemov, A. V., Lane, E., Vanhaelen, Q., Aliper, A. M., Ozerov, I. V., & Zhavoronkov, A. A. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/095653 · Cited by 41 · see citing works
Uni-Mol: A Universal 3D Molecular Representation Learning FrameworkPreprint
Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., & Ke, G. (2022) · ChemRxiv · doi:10.26434/chemrxiv-2022-jjm0j-v3 · Cited by 43 · see citing works
Fraser, K., Bruckner, D. M., & Dordick, J. S. (2018) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.8b00054 · Cited by 36 · see citing works
Multi-View Graph Neural Networks for Molecular Property PredictionPreprint
Ma, H., Bian, Y., Rong, Y., Huang, W., Xu, T., Xie, W., Ye, G., & Huang, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.13607 · Cited by 36 · see citing works
State of the Art and Potentialities of Graph-level Learning
Yang, Z., Zhang, G., Wu, J. X., Yang, J. Y., Sheng, Q. Z., Xue, S., Zhou, C. D., Aggarwal, C. C., Peng, H., Hu, W., et al. (2024) · ACM Computing Surveys · doi:10.1145/3695863 · Cited by 42 · see citing works
Yang, Q., Liu, Y., Cheng, J., Li, Y., Liu, S., Duan, Y., Zhang, L., & Luo, S. (2022) · ChemPhysChem · doi:10.1002/cphc.202200255 · Cited by 35 · see citing works
In silico methods for the prediction of drug toxicity
Rajpoot, K., Desai, N., Koppisetti, H., Tekade, M., Sharma, M. C., Behera, S. K., & Tekade, R. K. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-98367-9.00012-3 · Cited by 35 · see citing works
Molecular geometric deep learning
Shen, C., Luo, J., & Xia, K. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100621 · Cited by 33 · see citing works
Big Data Approaches for Modeling Response and Resistance to Cancer Drugs
Jiang, P., Sellers, W. R., & Liu, X. S. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013350 · Cited by 32 · see citing works
Molecular Joint Representation Learning via Multi-Modal Information of SMILES and Graphs
Wu, T., Tang, Y. S., Sun, Q., & Xiong, L. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3253862 · Cited by 36 · see citing works
Large-Scale Distributed Training of Transformers for Chemical Fingerprinting
Aty, H. A., & Gould, I. R. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00715 · Cited by 31 · see citing works
PanGu Drug Model: learn a molecule like a human
Lin, X., Xu, C., Xiong, Z., Zhang, X., Ni, N., Ni, B., Chang, J., Pan, R., Wang, Z., Yu, F., et al. (2022) · Science China Life Sciences · doi:10.1007/s11427-022-2239-y · Cited by 31 · see citing works
Goel, I., Bhaskar, Y., Kumar, N., Singh, S., Amanullah, M., Dhar, R., & Karmakar, S. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1550407 · Cited by 31 · see citing works
DGCL: dual-graph neural networks contrastive learning for molecular property prediction
Jiang, X., Tan, L., & Zou, Q. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae474 · Cited by 30 · see citing works
Pretraining graph transformer for molecular representation with fusion of multimodal information
Chen, R., Li, C., Wang, L., Liu, M., Chen, S., Yang, J., & Zeng, X. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102784 · Cited by 30 · see citing works
Ulm, J. W., & Nelson, S. F. (2020) · Transboundary and Emerging Diseases · doi:10.1111/tbed.13710 · Cited by 30 · see citing works
Medical concept normalization in clinical trials with drug and disease representation learning
Miftahutdinov, Z., Kadurin, A., Kudrin, R., & Tutubalina, E. V. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab474 · Cited by 30 · see citing works
Demarque, D. P., & Espíndola, L. S. (2021) · Frontiers in Chemistry · doi:10.3389/fchem.2021.779049 · Cited by 30 · see citing works
Revolutionizing Medical Practice: The Impact of Artificial Intelligence (AI) on Healthcare
(2024) · Open Access Journal of Applied Science and Technology · doi:10.33140/oajast.02.01.07 · Cited by 29 · see citing works
Formatting biological big data for modern machine learning in drug discovery
Duran‐Frigola, M., Fernández‐Torras, A., Bertoni, M., & Aloy, P. (2019) · WIREs Computational Molecular Science · doi:10.1002/wcms.1408 · Cited by 25 · see citing works
Advanced deep learning methods for molecular property prediction
Pang, C., Tong, H. H. Y., & Wei, L. (2023) · Quantitative Biology · doi:10.1002/qub2.23 · Cited by 27 · see citing works
Matsuzaka, Y., & Uesawa, Y. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00485 · Cited by 25 · see citing works
Towards Pharma 4.0 in clinical trials: A future-orientated perspective
Hariry, R. E., Barenji, R. V., & Paradkar, A. R. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.09.002 · Cited by 29 · see citing works
Molecular pretraining models towards molecular property prediction
Qiao, J., Gao, W., Jin, J., Wang, D., Xu, G., Manavalan, B., & Wei, L. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4457-2 · Cited by 27 · see citing works
Wiercioch, M., & Kirchmair, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119055 · Cited by 25 · see citing works
Federated Learning in Computational Toxicology: An Industrial Perspective on the Effiris Hackathon
Bassani, D., Brigo, A., & Morger, A. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00137 · Cited by 24 · see citing works
Deep learning algorithms applied to computational chemistry
Pando, A. G., Ramírez-Alonso, G. M. D. J., Arzate-Quintana, C., & Camarillo‐Cisneros, J. (2023) · Molecular Diversity · doi:10.1007/s11030-023-10771-y · Cited by 26 · see citing works
Oyejide, A. J., Adekunle, Y. A., Abodunrin, O. D., & Atoyebi, E. O. (2025) · Intelligent Pharmacy · doi:10.1016/j.ipha.2025.01.001 · Cited by 28 · see citing works
- Authors
- Gayvert, K. M., Madhukar, N. S., & Elemento, O.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Weill Cornell Medicine (US)
- Corresponding author
- Elemento, Olivier
- Venue
- Cell chemical biology
- Publisher
- Elsevier BV
- Published
- 2016-10
- Volume
- 23
- Issue
- 10
- Pages
- 1294-1301
- ISSN
- 2451-9448 (ISSN-L)
- 2451-9456
- PMC
- PMC5074862
- OpenAlex
- W2461620095
- Cited by
- 310
- References
- 30
- Citations by year
- 2026: 46 · 2025: 48 · 2024: 49 · 2023: 52 · 2022: 33 · 2021: 30 · 2020: 29 · 2019: 13 · 2018: 8 · 2017: 1 · 2016: 1
- FWCI
- 7.05
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell chemical biology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- CAREER grant from National Science Foundation (DB1054964)
- NIH (R01CA194547)
- Starr Cancer Foundation
- Institute for Computational Biomedicine
- PhRMA Foundation Pre Doctoral Informatics Fellowship
- Tri-Institutional Training Program in Computational Biology and Medicine
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- drug attrition, molecular weight, adverse clinical events, structure-property relationships, drug toxicity
- MeSH terms
- Clinical Trials as Topic, Humans, Models, Biological, Models, Molecular, Software, Likelihood Functions, Computational Biology, Drug Discovery, Drug-Related Side Effects and Adverse Reactions
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.
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.
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