Machine Learning in Drug Discovery: A Review

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

The Elements of Statistical Learning

Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162

Auto-Encoding Variational Bayes

Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067

Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness

Nishita, M., Park, S.-Y., Nishio, T., Kamizaki, K., Wang, Z., Tamada, K., Takumi, T., Hashimoto, R., Otani, H., Pazour, G. J., et al. (2017) · Scientific Reports · doi:10.1038/s41598-016-0028-x · Cited by 15,947

Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy

Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., et al. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0306430 · Cited by 10,057

In situ click chemistry generation of cyclooxygenase-2 inhibitors

Bhardwaj, A., Kaur, J., Wuest, M., & Wuest, F. (2017) · Nature Communications · doi:10.1038/s41467-016-0009-6 · Cited by 7,448

Structural absorption by barbule microstructures of super black bird of paradise feathers

McCoy, D. E., Feo, T., Harvey, T. A., & Prum, R. O. (2018) · Nature Communications · doi:10.1038/s41467-017-02088-w · Cited by 7,424

Greedy layer-wise training of deep networks

Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841

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

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

Drug repositioning: identifying and developing new uses for existing drugs

Ashburn, T. T., & Thor, K. B. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1468 · Cited by 3,237

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106

Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations

Khera, A. V., Chaffin, M., Aragam, K. G., Haas, M. E., Roselli, C., Choi, S. H., Natarajan, P., Lander, E. S., Lubitz, S. A., Ellinor, P. T., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0183-z · Cited by 2,953

Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930

An Introduction to Variational Autoencoders

Kingma, D. P., & Welling, M. (2019) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000056 · Cited by 2,609

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573

Deep learning for computational biology

Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473

Metformin improves healthspan and lifespan in mice

Martin-Montalvo, A., Mercken, E. M., Mitchell, S. J., Palacios, H. H., Mote, P. L., Scheibye-Knudsen, M., Gomes, A. P., Ward, T. M., Minor, R. K., Blouin, M.-J., et al. (2013) · Nature Communications · doi:10.1038/ncomms3192 · Cited by 1,445

ZDOCK: An initial‐stage protein‐docking algorithm

Chen, R., Li, L., & Weng, Z. (2003) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10389 · Cited by 1,393

Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships

Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., & Svetnik, V. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500747n · Cited by 1,159

Small molecules, big targets: drug discovery faces the protein–protein interaction challenge

Scott, D. E., Bayly, A. R., Abell, C., & Skidmore, J. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.29 · Cited by 1,137

DeepTox: toxicity prediction using deep learning

Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00080 · Cited by 1,081

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Wang, S., Sun, S., Li, Z., Zhang, R., & Xu, J. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005324 · Cited by 931

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

MAQC Consortium (2010) · Nature Biotechnology · doi:10.1038/nbt.1665 · Cited by 889

The target landscape of clinical kinase drugs

Klaeger, S., Heinzlmeir, S., Wilhelm, M., Polzer, H., Vick, B., Koenig, P.-A., Reinecke, M., Ruprecht, B., Petzoldt, S., Meng, C., et al. (2017) · Science · doi:10.1126/science.aan4368 · Cited by 854

Similarity-based virtual screening using 2D fingerprints

Willett, P. (2006) · Drug Discovery Today · doi:10.1016/j.drudis.2006.10.005 · Cited by 849

Deep learning based tissue analysis predicts outcome in colorectal cancer

Bychkov, D., Linder, N., Turkki, R., Nordling, S., Kovanen, P. E., Verrill, C., Walliander, M., Lundin, M., Haglund, C., & Lundin, J. (2018) · Scientific Reports · doi:10.1038/s41598-018-21758-3 · Cited by 644

Network-based in silico drug efficacy screening

Guney, E., Menche, J., Vidal, M., & Barábasi, A.-L. (2016) · Nature Communications · doi:10.1038/ncomms10331 · Cited by 625

Convolutional embedding of attributed molecular graphs for physical property prediction

Coley, C. W., Barzilay, R., Green, W. H., Jaakkola, T. S., & Jensen, K. F. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00601 · Cited by 470

Deep learning of the tissue-regulated splicing code

Leung, M. K. K., Xiong, H. Y., Lee, L. J., & Frey, B. J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu277 · Cited by 459

Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening

Ain, Q. U., Aleksandrova, A., Roessler, F. D., & Ballester, P. J. (2015) · WIREs Computational Molecular Science · doi:10.1002/wcms.1225 · Cited by 357

Target identification among known drugs by deep learning from heterogeneous networks

Zeng, X., Zhu, S., Lu, W., Liu, Z., Huang, J., Zhou, Y., Fang, J., Huang, Y., Guo, H., Li, L., et al. (2020) · Chemical Science · doi:10.1039/c9sc04336e · Cited by 357

Metformin promotes lifespan through mitohormesis via the peroxiredoxin PRDX-2

De Haes, W., Frooninckx, L., Van Assche, R., Smolders, A., Depuydt, G., Billen, J., Braeckman, B. P., Schoofs, L., & Temmerman, L. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1321776111 · Cited by 355

Deep learning for classification of colorectal polyps on whole-slide images

Korbar, B., Olofson, A. M., Miraflor, A. P., Nicka, C. M., Suriawinata, M. A., Torresani, L., Suriawinata, A. A., & Hassanpour, S. (2017) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_34_17 · Cited by 349

Interpretable dimensionality reduction of single cell transcriptome data with deep generative models

Ding, J., Condon, A., & Shah, S. P. (2018) · Nature Communications · doi:10.1038/s41467-018-04368-5 · Cited by 337

A review of network-based approaches to drug repositioning

Lotfi Shahreza, M., Ghadiri, N., Mousavi, S. R., Varshosaz, J., & Green, J. R. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx017 · Cited by 330

Protein-Protein Docking: From Interaction to Interactome

Vakser, I. A. (2014) · Biophysical Journal · doi:10.1016/j.bpj.2014.08.033 · Cited by 318 · cited 1×

Is Multitask Deep Learning Practical for Pharma?

Ramsundar, B., Liu, B., Wu, Z., Verras, A., Tudor, M., Sheridan, R. P., & Pande, V. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00146 · Cited by 263

Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction

Xu, Y., Pei, J., & Lai, L. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00244 · Cited by 250

Comprehensive prediction of drug-protein interactions and side effects for the human proteome

Zhou, H., Gao, M., & Skolnick, J. (2015) · Scientific Reports · doi:10.1038/srep11090 · Cited by 138

Targeting Protein–Protein Interactions and Fragment-Based Drug Discovery

Valkov, E., Sharpe, T., Marsh, M., Greive, S., & Hyvönen, M. (2011) · Topics in Current Chemistry · doi:10.1007/128_2011_265 · Cited by 107

A machine-learning heuristic to improve gene score prediction of polygenic traits

Paré, G., Mao, S., & Deng, W. Q. (2017) · Scientific Reports · doi:10.1038/s41598-017-13056-1 · Cited by 85

Self-Driving Laboratories for Chemistry and Materials Science

Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707

Hepatic fibrosis 2022: Unmet needs and a blueprint for the future

Friedman, S. L., & Pinzani, M. (2022) · Hepatology · doi:10.1002/hep.32285 · Cited by 430

AI in drug discovery and its clinical relevance

Qureshi, R., Irfan, M., Gondal, T. M., Khan, S., Wu, J., Hadi, M. U., Heymach, J., Le, X., Yan, H., & Alam, T. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17575 · Cited by 319

Federated learning for the healthcare metaverse: Concepts, applications, challenges, and future directions

Bashir, A. K., Victor, N., Bhattacharya, S., Huynh-The, T., Chengoden, R., Yenduri, G., Maddikunta, P. K. R., Pham, Q.-V., Gadekallu, T. R., & Liyanage, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3304790 · Cited by 183

Generative machine learning for de novo drug discovery: A systematic review

Martinelli, D. D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105403 · Cited by 177