The DOI matches its Crossref record; 113 of the 118 references it cites were found in a registry or an index.
A renaissance of neural networks in drug discovery
Abstract
INTRODUCTION: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to choose the most appropriate approach. AREAS COVERED: In this review, the authors discuss traditional and newly emerging neural network approaches to drug discovery. Their focus is on backpropagation neural networks and their variants, self-organizing maps and associated methods, and a relatively new technique, deep learning. The most important technical issues are discussed including overfitting and its prevention through regularization, ensemble and multitask modeling, model interpretation, and estimation of applicability domain. Different aspects of using neural networks in drug discovery are considered: building structure-activity models with respect to various targets; predicting drug selectivity, toxicity profiles, ADMET and physicochemical properties; characteristics of drug-delivery systems and virtual screening. EXPERT OPINION: Neural networks continue to grow in importance for drug discovery. Recent developments in deep learning suggests further improvements may be gained in the analysis of large chemical data sets. It's anticipated that neural networks will be more widely used in drug discovery in the future, and applied in non-traditional areas such as drug delivery systems, biologically compatible materials, and regenerative medicine.
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“DNN methods also require substantially larger datasets to fully capitalize on their advantages76”
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“The development of new types of DNN76 has opened up new applications, allowing simpler molecular representations, such as SMILES strings or chemical graphs to be used to generate useful toxicity (and other property) models.”
From QSAR without borders (2020) · cites this paper as [76]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
(2014) · Cited by 33,419
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
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Neural networks for pattern recognition
Bishop, C. (1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,537
Flagged first, then by citations
5 of the 118 references couldn’t be matched to a known paper.
Research works printed but not linked: 9
- Baskin II, 2008, Methods Mol Biol (Clifton, NJ), 458, 137
- Burden F, 2008, Methods Mol Biol, 458, 25
- 10.1002/qsar.v28:10 · DOI
- Srivastava N, 2014, J Machine Learn Res, 15, 1929
- 10.1002/qsar.v28:6/7 · DOI
- Glorot X, 2011, Int Conf Artif Intelligence Stat, 2011, 315
- 10.1002/minf.v30.1 · DOI
- 10.1002/minf.v35.1 · DOI
- Tetko IV, 2016, Mol Inf
Cited by
Sorted by citations
Items 201–236 of 236
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Ratkova, E. L., Abramov, Y. A., Baskin, I. I., Livingstone, D. J., Xia, J., & Tetko, I. V. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00058-3 · Cited by 1
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Schuh, M. G., Daniluk, A., & Sieber, S. A. (2026) · Chemical Science · doi:10.1039/d6sc01799a · Cited by 3
Constructing Features Using a Hybrid Genetic Algorithm
Tsoulos, I. G. (2022) · Signals · doi:10.3390/signals3020012 · Cited by 1
Advances in drug development with the application of artificial intelligence
Leite, M. S., de Jesus, A. A., Araújo, P. J. L., & dos Santos, B. F. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822248-5.00014-0 · Cited by 1
Deep learning in pharmacology: opportunities and threats
Kocić, I., Kocić, M., Rusiecka, I., Kocić, A., & Kocić, E. (2022) · European Journal of Translational and Clinical Medicine · doi:10.31373/ejtcm/149217 · Cited by 1
Traoré, Y., Ehouman, J. M., Koné, M. G.-R., Diabaté, D., & Ziao, N. (2022) · Computational Chemistry · doi:10.4236/cc.2022.104009 · Cited by 1
Dlamini, Z., & Hull, R. (2023) · Springer eBooks · doi:10.1007/978-3-031-21506-3_1 · Cited by 2
Discovering Novel Pharmaceutical Molecules with Generative Adversarial Networks
Chandrashekar, L., Namratha, K., Mohan, P., J, R. B. P., & Kulkarni, A. (2023) · Proceeding of the Electrical Engineering Computer Science and Informatics · doi:10.1109/eecsi59885.2023.10295896 · Cited by 1
Demidova, A. V., & Tarasova, O. A. (2019) · Cited by 1
Advances in Multiobjective Optimization for Drug Discovery and Development
Honeycutt, J. D., Zorn, K. M., Clark, A. M., & Ekins, S. (2021) · Wiley eBooks · doi:10.1002/0471266949.bmc122.pub2 · Cited by 2
Jaleel, U., Vishnuprasad, C. N., Sathish, S., Thottappillil, A., R, J. R. K., Kukkupuni, S. K., Kulkarni, P., Rakhila, M., Safeeda, A., Manuel, A., et al. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4468365 · Cited by 1
Manufacturability of Regenerative Medicine Products for Commercial Scale
Orchard‐Webb, D., & Robu, I. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.65601-5
Živanović, D., Javorac, J., Javorac, T., & Kralj, M. (2019) · Zdravstvena zastita · doi:10.5937/zz1904043z
QSAR model of phenols generated by deep neural network
Douali, L. (2020) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset48871.2020.9092000
Tarasova, O. A., Biziukova, N. Y., Filimonov, D. A., & Poroikov, V. (2022) · Research Square · doi:10.21203/rs.3.rs-1521134/v1
Lane, T. R., Foil, D. H., Minerali, E., Urbina, F., Zorn, K. M., & Ekins, S. (2020) · ChemRxiv · doi:10.26434/chemrxiv.12781241
Шмырев, К. А., & Makarov, I. (2022) · IEEE International Symposium on Computational Intelligence and Informatics (CINTI) · doi:10.1109/cinti-macro57952.2022.10029553
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Mendon, A., Votavat, B. M., Hegde, V., & Patil, M. (2023) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-6684-6821-0.ch023
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Tsoulos, I. G., & Tzallas, A. T. (2023) · Applied Sciences · doi:10.3390/app13148124
Numerical Algorithms in III–V Semiconductor Heterostructures
Tsoulos, I. G., & Stavrou, V. N. (2024) · Algorithms · doi:10.3390/a17010044
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Senthil, P. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.59337
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Rajeswari, M. A. M. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.59304
Prediction of apoptosis signal-regulating kinase 1 (ASK1) inhibition with machine learning methods
Kuang, Z., Huang, Q., Pan, H., Duan, X., & Huang, L. (2024) · Chemical Papers · doi:10.1007/s11696-024-03499-y
Deep Learning of Electron Density for Predicting Energies: The Case of Boron Clusters
Saha, P., & Nguyen, M. T. (2024) · doi:10.1002/9781394217656.ch12
Analysis of Artificial Neural Networks and Their Application in Pharmacokinetics: A Narrative Review
Bezerra, T. D. S., Abreu, L. P. G. C., Abreu, R. G., & Luz, G. V. D. S. (2025) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-94921-0_57
PyaiVS unifies AI workflows to accelerate ligand discovery and yields ABCG2 inhibitors
Wang, M., Qu, B., Yang, L., Wang, L., Jiang, K., & Lin, J. (2025) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2025.118176 · Cited by 1
AI strategies for drug discovery and bioactivity prediction: opportunities and challenges
Azad, A. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34059-8.00013-0
Sahu, M. K., Sahu, B., Gupta, M., & Mishra, A. K. (2026) · Journal for Research in Applied Sciences and Biotechnology · doi:10.55544/jrasb.5.1.19
Protein and Peptide Design with Generative AI
Agustriawan, D. (2026) · Springer eBooks · doi:10.1007/978-3-032-20428-8_3
Saud, N. B., Abdulgawad, K. E. A., Mansour, M., Mahlous, A. R., & Azar, A. T. (2026) · IEEE Access · doi:10.1109/access.2026.3695138
Spintronic neuromorphic hardware using domain wall-based neurons and quantized synapses
Bandekar, S. K., Ganguly, A., Polley, D., & Das, D. (2026) · npj Spintronics · doi:10.1038/s44306-026-00160-0
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Gupta, J., Jain, S. D., Agrawal, A., & Gupta, A. K. (2026) · International Journal of Newgen Research in Pharmacy & Healthcare · doi:10.61554/ijnrph.v4i1.2026.235
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Kesavardhini, K., Saranraj, P., Lokeshwari, B., Gayathri, K. V., & Arthi, K. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8562-4.ch008
BindingDB Entry 50010960: Learning Molecular Representations for Medicinal Chemistry.
BindingDB (2021) · UC San Diego · doi:10.7270/q2765jxg
Metadata
- Authors
- Baskin, I. I., Winkler, D., & Tetko, I. V.
- Affiliations
- Lomonosov Moscow State University (RU)
- Flinders University (AU)
- Helmholtz Munich (DE)
- Corresponding author
- Tetko, Igor V.
- Venue
- Expert Opinion on Drug Discovery
- Publisher
- Informa UK Limited
- Published
- 2016-07-04
- Volume
- 11
- Issue
- 8
- Pages
- 785-795
- ISSN
- 1746-0441 (ISSN-L)
- 1746-045X
- OpenAlex
- W2436108096
- Cited by
- 235
- References
- 118
- Citations by year
- 2026: 12 · 2025: 18 · 2024: 25 · 2023: 24 · 2022: 41 · 2021: 26 · 2020: 26 · 2019: 21 · 2018: 27 · 2017: 8 · 2016: 7
- FWCI
- 1.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Opinion on Drug Discovery (publishedVersion)
- PubMed (publishedVersion)
- Site cant be reached (submittedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- artificial neural networks, drug discovery, BP neural network, self-organizing maps, deep learning, overfitting, regularization, ensemble learning, model interpretability, applicability domain, structure-activity relationship, toxicity prediction, ADMET prediction, physicochemical property prediction, drug delivery systems, virtual screening
- MeSH terms
- Pharmaceutical Preparations, Humans, Models, Biological, Structure-Activity Relationship, Drug Design, Drug Delivery Systems, Neural Networks, Computer, Computer-Aided Design, Drug Discovery
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