Cited by 760Computational Drug Discovery MethodsMachine Learning in Materials ScienceProtein Degradation and Inhibitors

The DOI matches its Crossref record; 59 of the 60 references it cites were found in a registry or an index.

From machine learning to deep learning: progress in machine intelligence for rational drug discovery

Zhang, Lu, Tan, Jianjun, Han, Dan, Zhu, Hao·2017·Drug Discovery Today

DOI 10.1016/j.drudis.2017.08.010

Open this paper in CoChat — free account

Abstract

Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era.

Integrity signals

Registry

Crossref · 10.1016/j.drudis.2017.08.010

Cites

59 of 59 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Random Forests
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Deep learning
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Statistical Learning Theory
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598228 · Cited by 11,798
An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
Altman, N. (1992) · The American Statistician · doi:10.1080/00031305.1992.10475879 · Cited by 5,657
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3300 · Cited by 2,873
Bioisosterism: A Rational Approach in Drug Design
Patani, G. A., & LaVoie, E. J. (1996) · Chemical Reviews · doi:10.1021/cr950066q · Cited by 1,344
Big data deep learning: Challenges and perspectives
Chen, X.-W., & Lin, X. (2014) · IEEE Access · doi:10.1109/access.2014.2325029 · Cited by 1,255

Flagged first, then by citations

Show all 60 references

1 of the 60 references couldn’t be matched to a known paper.

Research works printed but not linked: 3

Hinton, 2006, 8, 1527
Jain, 2015, In silico de novo design of novel NNRTIs: a bio-molecular modelling approach, RSC Adv., 5, 14814, 10.1039/C4RA15478A · DOI
Unterthiner, 2014, Deep learning as an opportunity in virtual screening, Proc. Deep Learning Workshop NIPS

Sorted by citations

Items 101–200 of 758

Along the allostery stream: Recent advances in computational methods for allosteric drug discovery
Ni, D., Chai, Z., Wang, Y., Li, M., Yu, Z., Liu, Y., Lu, S., & Zhang, J. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1585 · Cited by 68
Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?
Hasanzadeh, A., Hamblin, M. R., Kiani, J., Noori, H., Hardie, J. M., Karimi, M., & Shafiee, H. (2022) · Nano Today · doi:10.1016/j.nantod.2022.101665 · Cited by 73
Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods
Mostofian, B., Martin, H., Razavi, A. M., Patel, S., Allen, B. K., Sherman, W., & Izaguirre, J. A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00603 · Cited by 70
Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products
Ancajas, C. M. F., Oyedele, A. S., Butt, C. M., & Walker, A. S. (2024) · Natural Product Reports · doi:10.1039/d4np00009a · Cited by 75
The emerging role of artificial intelligence in multiple sclerosis imaging
Afzal, H. M. R., Luo, S., Ramadan, S. A., & Lechner‐Scott, J. (2020) · Multiple Sclerosis Journal · doi:10.1177/1352458520966298 · Cited by 69
Prediction of bitterant and sweetener using structure-taste relationship models based on an artificial neural network
Bo, W., Qin, D., Zheng, X., Wang, Y., Ding, B., Li, Y., & Liang, G. (2022) · Food Research International · doi:10.1016/j.foodres.2022.110974 · Cited by 66
Sigma profiles in deep learning: towards a universal molecular descriptor
Abranches, D. O., Zhang, Y., Maginn, E. J., & Colón, Y. J. (2022) · Chemical Communications · doi:10.1039/d2cc01549h · Cited by 70
When Machine Learning and Deep Learning Come to the Big Data in Food Chemistry
Tseng, Y. J., Chuang, P.-J., & Appell, M. (2023) · ACS Omega · doi:10.1021/acsomega.2c07722 · Cited by 73
Deep learning for mining protein data
Shi, Q., Chen, W., Huang, S., Wang, Y., & Xue, Z. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz156 · Cited by 68
Deep Learning in Diverse Intelligent Sensor Based Systems
Zhu, Y., Wang, M., Yin, X., Zhang, J., Meijering, E. H. W., & Hu, J. (2022) · Sensors · doi:10.3390/s23010062 · Cited by 73
Predicting Drug-Induced Liver Injury Using Convolutional Neural Network and Molecular Fingerprint-Embedded Features
Nguyen‐Vo, T., Nguyen, L., Do, N., Le, P. H., Nguyen, T.-N., Nguyen, B. P., & Le, L. (2020) · ACS Omega · doi:10.1021/acsomega.0c03866 · Cited by 68
Machine learning for molecular thermodynamics
Ding, J., Xu, N., Nguyen, M. T., Qiao, Q., Shi, Y., He, Y., & Shao, Q. (2021) · Chinese Journal of Chemical Engineering · doi:10.1016/j.cjche.2020.10.044 · Cited by 66
Requirements for Animal Experiments: Problems and Challenges
Fontana, F., Figueiredo, P., Martins, J. P., & Santos, H. A. (2020) · Small · doi:10.1002/smll.202004182 · Cited by 67
Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach
Ciallella, H. L., Russo, D. P., Aleksunes, L. M., Grimm, F. A., & Zhu, H. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.1c02656 · Cited by 63
Self-citation
Deep Learning Prediction of Adverse Drug Reactions in Drug Discovery Using Open TG–GATEs and FAERS Databases
Mohsen, A. S., Tripathi, L. P., & Mizuguchi, K. (2021) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2021.768792 · Cited by 72
Barriers, drivers, and social considerations for AI adoption in supply chain management: a tertiary study
Hangl, J., Behrens, V. J., & Krause, S. (2022) · Logistics · doi:10.3390/logistics6030063 · Cited by 63
Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data
Πεππές, Ν., Alexakis, T., Adamopoulou, E. F., & Demestichas, K. P. (2021) · Sensors · doi:10.3390/s21144704 · Cited by 62
Multiphysical graph neural network (MP-GNN) for COVID-19 drug design
Li, X., Liu, X., Lü, L., Hua, X., Chi, Y., & Xia, K. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac231 · Cited by 65
Machine Learning for Child and Adolescent Health: A Systematic Review
Hoodbhoy, Z. A., Jeelani, S. M., Aziz, A., Habib, M. I., Iqbal, B., Akmal, W., Siddiqui, K., Hasan, B. S., Leeflang, M., & Das, J. K. (2020) · PEDIATRICS · doi:10.1542/peds.2020-011833 · Cited by 61
Research on a handwritten character recognition algorithm based on an extended nonlinear kernel residual network
Rao, Z., Zeng, C., Wu, M., Wang, Z., Zhao, N., & Wan, M. L. X. (2018) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2018.01.020 · Cited by 62
Using acoustic emission technique for structural health monitoring of laminate composite: A novel CNN-LSTM framework
Du, J., Zeng, J., Wang, H., Ding, H., Wang, H., & Bi, Y. (2024) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2024.110447 · Cited by 60
Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches
Ciallella, H. L., Russo, D. P., Aleksunes, L. M., Grimm, F. A., & Zhu, H. (2020) · Laboratory Investigation · doi:10.1038/s41374-020-00477-2 · Cited by 62
Self-citation
Artificial intelligence in the pharmaceutical sector: current scene and future prospect
Kalyane, D., Sanap, G., Paul, D., Shenoy, S., Anup, N., Polaka, S., Tambe, V., & Tekade, R. K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-814455-8.00003-7 · Cited by 62
Computer-Aided Drug Design
Bharatam, P. V. (2021) · Springer eBooks · doi:10.1007/978-981-15-5534-3_6 · Cited by 58
Dissecting Machine-Learning Prediction of Molecular Activity: Is an Applicability Domain Needed for Quantitative Structure–Activity Relationship Models Based on Deep Neural Networks?
Liu, R., Wang, H., Glover, K., Feasel, M. G., & Wallqvist, A. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00348 · Cited by 61
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 65
Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein–Ligand Binding Affinity Prediction
Wee, J., & Xia, K. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01415 · Cited by 60
Predicting Short-term Survival after Liver Transplantation using Machine Learning
Liu, C., Soong, R., Lee, W., Jiang, G.-W., & Lin, Y.-C. (2020) · Scientific Reports · doi:10.1038/s41598-020-62387-z · Cited by 58
Recent Advances in Quantum Computing for Drug Discovery and Development
Wang, P.-H., Chen, J.-H., Yang, Y.-Y., Lee, C., & Tseng, Y. J. (2023) · IEEE Nanotechnology Magazine · doi:10.1109/mnano.2023.3249499 · Cited by 58
A comprehensive review of computational cell cycle models in guiding cancer treatment strategies
Ma, C., & Gürkan-Çavusoglu, E. (2024) · npj Systems Biology and Applications · doi:10.1038/s41540-024-00397-7 · Cited by 63
ProGAN: Protein solubility generative adversarial nets for data augmentation in DNN framework
Xi, H., Zhang, L., Zhou, K., & Wang, X. (2019) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2019.106533 · Cited by 54
Self-citation
Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics
Lemus, E. H., & Martínez-García, M. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.605680 · Cited by 57
Comparison of Machine Learning Techniques Applied to Traffic Prediction of Real Wireless Network
Alekseeva, D., Stepanov, N., Veprev, A., Sharapova, A., Lohan, E. S., & Ometov, A. (2021) · IEEE Access · doi:10.1109/access.2021.3129850 · Cited by 66
Systematic Modeling of log D7.4 Based on Ensemble Machine Learning, Group Contribution, and Matched Molecular Pair Analysis
Fu, L., Liu, L., Yang, Z., Li, P., Ding, J., Yun, Y., Lü, A., Hou, T., & Cao, D. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00718 · Cited by 52
Serverless Prediction of Peptide Properties with Recurrent Neural Networks
Ansari, M., & White, A. D. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01317 · Cited by 54
Applying molecular docking to pesticides
Hou, Y., Bai, Y., Lu, C., Wang, Q., Wang, Z., Gao, J., & Xu, H. (2023) · Pest Management Science · doi:10.1002/ps.7700 · Cited by 52
Success stories of AI in drug discovery - where do things stand?
Mak, K.-K., Balijepalli, M. K., & Pichika, M. R. (2022) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2022.1985108 · Cited by 51
Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs
Periwal, V., Bassler, S. O., Andrejev, S., Gabrielli, N., Patil, K. R., Typas, A., & Patil, K. R. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010029 · Cited by 53
Recent advances from computer-aided drug design to artificial intelligence drug design
Wang, K., Huang, Y., Wang, Y., You, Q., & Wang, L. (2024) · RSC Medicinal Chemistry · doi:10.1039/d4md00522h · Cited by 58
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 55
Applications of conceptual density functional theory in reference to quantitative structure–activity / property relationship
Sharma, P., Ranjan, P., & Chakraborty, T. (2024) · Molecular Physics · doi:10.1080/00268976.2024.2331620 · Cited by 54
Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives
Mohapatra, M., Sahu, C., & Mohapatra, S. (2024) · Current Drug Targets · doi:10.2174/0113894501322734241008163304 · Cited by 52
Harnessing the power of machine learning into tissue engineering: current progress and future prospects
Wu, Y., Ding, X., Wang, Y., & Ouyang, D. (2024) · Burns & Trauma · doi:10.1093/burnst/tkae053 · Cited by 52
iCYP-MFE: Identifying Human Cytochrome P450 Inhibitors Using Multitask Learning and Molecular Fingerprint-Embedded Encoding
Nguyen‐Vo, T., Trinh, Q. H., Nguyen, L., Nguyen-Hoang, P.-U., Nguyen, T.-N., Nguyen, D. T., Nguyen, B. P., & Le, L. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00628 · Cited by 50
Big (Bio)Chemical Data Mining Using Chemometric Methods: A Need for Chemists
Parastar, H., & Tauler, R. (2018) · Angewandte Chemie International Edition · doi:10.1002/anie.201801134 · Cited by 51
Real-world application, challenges and implication of artificial intelligence in healthcare: an essay.
Mudgal, S. K., Agarwal, R. K., Chaturvedi, J., Gaur, R., & Ranjan, N. (2022) · PubMed · doi:10.11604/pamj.2022.43.3.33384 · Cited by 65
Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure–Activity Relationship (QSAR) Analysis
Matsuzaka, Y., & Uesawa, Y. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00065 · Cited by 48
A machine learning model to estimate myocardial stiffness from EDPVR
Babaei, H., Mendiola, E. A., Neelakantan, S., Xiang, Q., Vang, A., Dixon, R. A. F., Shah, D. J., Vanderslice, P., Choudhary, G., & Avazmohammadi, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-09128-6 · Cited by 44
Artificial intelligence as a tool in drug discovery and development
Kokudeva, M., Vichev, M., Naseva, E., Miteva, D., & Velikova, T. V. (2024) · World Journal of Experimental Medicine · doi:10.5493/wjem.v14.i3.96042 · Cited by 58
Integrating continuous flow synthesis with in-line analysis and data generation
Baumann, M. (2018) · Organic & Biomolecular Chemistry · doi:10.1039/c8ob01437j · Cited by 48
Quantitative structure–activity relationship analysis using deep learning based on a novel molecular image input technique
Uesawa, Y. (2018) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2018.08.032 · Cited by 44
Recent Advances of Graphene and Related Materials in Artificial Intelligence
Huang, M., Li, Z., & Zhu, H. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202200077 · Cited by 45
Self-citation
Deep-learning-based target screening and similarity search for the predicted inhibitors of the pathways in Parkinson's disease
Khan, A., Kaushik, A. C., Ali, S. S., Ahmad, N., & Wei, D. (2019) · RSC Advances · doi:10.1039/c9ra01007f · Cited by 50
Approaching Target Selectivity by De Novo Drug Design
Fischer, T., Gazzola, S., & Riedl, R. (2019) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2019.1615435 · Cited by 46
Deep Learning Techniques and COVID-19 Drug Discovery: Fundamentals, State-of-the-Art and Future Directions
Jamshidi, M. B., Lalbakhsh, A., Talla, J., Peroutka, Z., Roshani, S., Matousek, V., Roshani, S., Mirmozafari, M., Malek, Z., Spada, L. L., et al. (2021) · Studies in systems, decision and control · doi:10.1007/978-3-030-67716-9_2 · Cited by 46
High-Throughput Screening for Drug Discovery
Saxena, S. K. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.91075 · Cited by 46
Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches
Bhat, V., Sornberger, P., Pokuri, B. S. S., Duke, R., Ganapathysubramanian, B., & Risko, C. M. (2022) · Chemical Science · doi:10.1039/d2sc04676h · Cited by 48
Could deep learning in neural networks improve the QSAR models?
Gini, G. C., Zanoli, F., Gamba, A., Raitano, G., & Benfenati, E. (2019) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2019.1650827 · Cited by 46
Developing an artificial intelligence method for screening hepatotoxic compounds in traditional Chinese medicine and Western medicine combination
Zhao, C. X., Zhao, M., You, L., Zheng, R., Jiang, Y., Zhang, X., Qiu, R., Sun, Y., Pan, H., He, T., et al. (2022) · Chinese Medicine · doi:10.1186/s13020-022-00617-4 · Cited by 47
In silico models for genotoxicity and drug regulation
Benigni, R., Bassan, A., & Pavan, M. (2020) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1080/17425255.2020.1785428 · Cited by 43
Computational and artificial intelligence-based approaches for drug metabolism and transport prediction
Dudás, B., & Miteva, M. A. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2023.11.001 · Cited by 42
How Clinical Practice Research Datalink data are used to support pharmacovigilance
Ghosh, R. E., Crellin, E., Beatty, S., Donegan, K. L., Myles, P. R., & Williams, R. (2019) · Therapeutic Advances in Drug Safety · doi:10.1177/2042098619854010 · Cited by 46
Deep learning enables discovery of highly potent anti-osteoporosis natural products
Liu, Z., Huang, D., Zheng, S., Song, Y., Liu, B., Sun, J., Niu, Z., Gu, Q., Xu, J., & Xie, L. (2020) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2020.112982 · Cited by 42
AI-enabled FinTech for innovative sustainability: promoting organizational sustainability practices in digital accounting and finance
Nair, A. J., Manohar, S., & Mittal, A. (2024) · International Journal of Accounting and Information Management · doi:10.1108/ijaim-05-2024-0172 · Cited by 60
Breast cancer detection using deep learning techniques: challenges and future directions
Shahid, M. S., & Imran, A. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20606-7 · Cited by 43
Augmenting Hit Identification by Virtual Screening Techniques in Small Molecule Drug Discovery
Yan, X. C., Sanders, J. M., Gao, Y.-D., Tudor, M., Haidle, A. M., Klein, D. J., Converso, A., Lesburg, C. A., Zang, Y., & Wood, H. B. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00113 · Cited by 42
Towards the quantum-enabled technologies for development of drugs or delivery systems
Hassanzadeh, P. (2020) · Journal of Controlled Release · doi:10.1016/j.jconrel.2020.04.050 · Cited by 42
Next Generation Sequencing and Machine Learning Technologies Are Painting the Epigenetic Portrait of Glioblastoma
Jovčevska, I. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00798 · Cited by 41
Artificial intelligence and the future of life sciences
Leite, M. L., Costa, L. S. D. L., Cunha, V. A., Kreniski, V., Filho, M. D. O. B., da Cunha, N. B., & Costa, F. F. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.07.002 · Cited by 48
Emerging therapeutic strategies in glioblastsoma: drug repurposing, mechanisms of resistance, precision medicine, and technological innovations
Anwer, M. S., Abdel-Rasol, M. A., & El-Sayed, W. M. (2025) · Clinical and Experimental Medicine · doi:10.1007/s10238-025-01631-0 · Cited by 45
Self-supervised learning in drug discovery
Chen, Y., Wang, Z., Wang, J., Chu, Y., Zhang, Q., Li, Z. A., & Zeng, X. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4453-4 · Cited by 40
Heterogeneous Strategies to Eliminate Intracellular Bacterial Pathogens
Liu, Y., Jia, Y., Yang, K., & Wang, Z. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.00563 · Cited by 41
BitterMatch: recommendation systems for matching molecules with bitter taste receptors
Margulis, E., Slavutsky, Y., Lang, T., Behrens, M., Benjamini, Y., & Niv, M. Y. (2022) · Journal of Cheminformatics · doi:10.1186/s13321-022-00612-9 · Cited by 40
A new wave of innovation in Semantic web tools for drug discovery
Kanza, S., & Frey, J. G. (2019) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2019.1586880 · Cited by 39
Applying Machine Learning to LTE Traffic Prediction: Comparison of Bagging, Random Forest, and SVM
Stepanov, N., Alekseeva, D., Ometov, A., & Lohan, E. S. (2020) · International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) · doi:10.1109/icumt51630.2020.9222418 · Cited by 44
Prediction of collector flotation performance based on machine learning and quantum chemistry: A case of sulfide minerals
Zhang, W., Sun, W., Zheng, M., Xu, S., Zheng, R., Cao, J., Jin, X., Gao, Z., & Feng, Z. (2024) · Separation and Purification Technology · doi:10.1016/j.seppur.2024.126954 · Cited by 39
Combining Ensemble Learning with a Fragment-Based Topological Approach To Generate New Molecular Diversity in Drug Discovery: In Silico Design of Hsp90 Inhibitors
Speck‐Planche, A. (2018) · ACS Omega · doi:10.1021/acsomega.8b02419 · Cited by 39
Machine learning concepts and its applications for prediction of diseases based on drug behaviour: An extensive review
Singh, D. P., & Kaushik, B. (2022) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2022.104637 · Cited by 37
Prediction of activity and selectivity profiles of human Carbonic Anhydrase inhibitors using machine learning classification models
Tinivella, A., Pinzi, L., & Rastelli, G. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00499-y · Cited by 40
Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade
Serafim, M. S. M., dos Santos, V. S., Gertrudes, J. C., Maltarollo, V. G., & Honório, K. M. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1918098 · Cited by 38
Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network
Saadi, S. B., Sarshar, N. T., Sadeghi, S., Ranjbarzadeh, R., Kooshki, M., & Bendechache, M. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4703682 · Cited by 36
Review on the Artificial Intelligence-based Nanorobotics Targeted Drug Delivery System for Brain-specific Targeting
Rai, A., Shah, K., & Dewangan, H. K. (2023) · Current Pharmaceutical Design · doi:10.2174/0113816128279248231210172053 · Cited by 38
Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives
Shahzadi, M., Rafique, H., Waheed, A., Naz, H., Waheed, A., Zokirova, F. R., & Khan, H. (2024) · Therapeutic Advances in Vaccines and Immunotherapy · doi:10.1177/25151355241305856 · Cited by 44
A Machine Learning-Based Prediction Platform for P-Glycoprotein Modulators and Its Validation by Molecular Docking
Kadioglu, O., & Efferth, T. (2019) · Cells · doi:10.3390/cells8101286 · Cited by 36
Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry
Polański, J. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23052797 · Cited by 40
Ethical Challenges of Artificial Intelligence in Medicine
Boudi, A. L., Boudi, M., Chan, C. C. C., & Boudi, F. B. (2024) · Cureus · doi:10.7759/cureus.74495 · Cited by 35
BraggNet: integrating Bragg peaks using neural networks
Sullivan, B., Archibald, R. K., Azadmanesh, J., Vandavasi, V. G., Langan, P. S., Coates, L., Lynch, V. E., & Langan, P. A. (2019) · Journal of Applied Crystallography · doi:10.1107/s1600576719008665 · Cited by 34
Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach
Wang, X., Liu, M., Zhang, L., Wang, Y., Li, Y., & Lu, T. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00568 · Cited by 34
Self-citation
Molecular geometric deep learning
Shen, C., Luo, J., & Xia, K. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100621 · Cited by 35
Advances in machine intelligence‐driven virtual screening approaches for big‐data
Kumar, N., & Acharya, V. (2023) · Medicinal Research Reviews · doi:10.1002/med.21995 · Cited by 36
Quality by digital design to accelerate sustainable medicines development
Mustoe, C. L., Turner, A., Urwin, S. J., Houson, I., Feilden, H., Markl, D., Qaraghuli, M. M. A., Chong, M. W. S., Robertson, M. N., Nordon, A., et al. (2025) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2025.125625 · Cited by 36
Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview
Castañé, H., Baiges‐Gayà, G., Hernández‐Aguilera, A., Rodríguez‐Tomàs, E., Fernández‐Arroyo, S., Herrero, P., Delpino-Rius, A., Canela-Canela, N., Menéndez, J. A., Camps, J., et al. (2021) · Biomolecules · doi:10.3390/biom11030473 · Cited by 34
Triphasic DeepBRCA-A Deep Learning-Based Framework for Identification of Biomarkers for Breast Cancer Stratification
Rajpal, S., Agarwal, M., Kumar, V., Gupta, A., & Kumar, N., V (2021) · IEEE Access · doi:10.1109/access.2021.3093616 · Cited by 34
Natural Product-Based Studies for the Management of Castration-Resistant Prostate Cancer: Computational to Clinical Studies
Singla, R. K., Sharma, P., Dubey, A. K., Gundamaraju, R., Kumar, D., Kumar, S., Madaan, R., Shri, R., Tsagkaris, C., Parisi, S., et al. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.732266 · Cited by 35
Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning
Kim, Y., Jung, Y.-S., Park, J.-H., Kim, S.-J., & Cho, Y. (2022) · Biomolecules · doi:10.3390/biom12101497 · Cited by 35
MDF-DTA: A Multi-Dimensional Fusion Approach for Drug-Target Binding Affinity Prediction
Ranjan, A., Bess, A., Alvin, C., & Mukhopadhyay, S. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00310 · Cited by 32
An LCZ-based machine learning framework for revealing spatial heterogeneity of thermal comfort in high-density areas: Enhancing explainability and fine-grid scale resolution
Zhou, S., Geng, X., Zhao, J., Hei, J., Wu, T., Chen, Z., & Wu, Z. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106873 · Cited by 32
Artificial Intelligence (AI) in Pharmaceutical Formulation and Dosage Calculations
Joshi, S., & Sheth, S. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17111440 · Cited by 39
Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein–ligand binding affinities
Yasuda, I., Endo, K., Yamamoto, E., Hirano, Y., & Yasuoka, K. (2022) · Communications Biology · doi:10.1038/s42003-022-03416-7 · Cited by 36
Interplay between Artificial Intelligence and Biomechanics Modeling in the Cardiovascular Disease Prediction
Li, X., Liu, X., Deng, X., & Fan, Y. (2022) · Biomedicines · doi:10.3390/biomedicines10092157 · Cited by 32

Authors: Zhang, L., Tan, J., Han, D., & Zhu, H.
Affiliations: Beijing University of Technology (CN)
Rutgers, The State University of New Jersey (US)
Corresponding author: Tan, Jianjun
Zhu, Hao
ORCID: Zhang, Lu
Tan, Jianjun
Han, Dan
Zhu, Hao
Venue: Drug Discovery Today
Publisher: Elsevier BV
Published: 2017-11
Volume: 22
Issue: 11
Pages: 1680-1685
ISSN: 1359-6446 (ISSN-L)
DOI: 10.1016/j.drudis.2017.08.010
OpenAlex: W2753588101
Cited by: 760
References: 60
Citations by year: 2026: 42 · 2025: 90 · 2024: 105 · 2023: 134 · 2022: 112 · 2021: 96 · 2020: 79 · 2019: 61 · 2018: 39 · 2017: 2
FWCI: 4.98
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Drug Discovery Today (publishedVersion)
PubMed (publishedVersion)
Funders: Chinese Natural Science Foundation Project (21173014)
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: rational drug discovery, deep learning, machine learning, QSAR, big data, history of machine learning, computer-aided drug design
MeSH terms: Machine Learning, Artificial Intelligence, Humans, Models, Theoretical, Drug Design, Quantitative Structure-Activity Relationship, Drug Discovery

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.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

From machine learning to deep learning: progress in machine intelligence for rational drug discovery

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks