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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.”
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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
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310
The rise of deep learning in drug discovery
Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770
Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery
Thomford, N. E., Senthebane, D. A., Rowe, A., Munro, D., Seele, P. P., Maroyi, A., & Dzobo, K. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19061578 · Cited by 1,301
Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913
Self-citationSchneider, G. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.232 · Cited by 910
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
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 ›
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