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Automating drug discovery
Abstract
Small-molecule drug discovery can be viewed as a challenging multidimensional problem in which various characteristics of compounds - including efficacy, pharmacokinetics and safety - need to be optimized in parallel to provide drug candidates. Recent advances in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as artificial intelligence systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into aspects of this process. This could potentially accelerate time frames for compound discovery and optimization and enable more effective searches of chemical space. However, such approaches also raise considerable conceptual, technical and organizational challenges, as well as scepticism about the current hype around them. This article aims to identify the approaches and technologies that could be implemented robustly by medicinal chemists in the near future and to critically analyse the opportunities and challenges for their more widespread application.
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Davies, I. W. (2019) · Nature · doi:10.1038/s41586-019-1288-y · Cited by 127
Revolutionizing Drug Discovery: A Comprehensive Review of AI Applications
Dhudum, R., Ganeshpurkar, A., & Pawar, A. (2024) · Drugs and Drug Candidates · doi:10.3390/ddc3010009 · Cited by 131
CReM: chemically reasonable mutations framework for structure generation
Полищук, П. Г. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00431-w · Cited by 126
Mapping the Efficiency and Physicochemical Trajectories of Successful Optimizations
Young, R. J., & Leeson, P. D. (2018) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b00180 · Cited by 113
From Platform to Knowledge Graph: Evolution of Laboratory Automation
Bai, J., Cao, L., Mosbach, S., Akroyd, J., Lapkin, A. A., & Kraft, M. (2022) · JACS Au · doi:10.1021/jacsau.1c00438 · Cited by 124
“Since then, considerable advances have been made to expand the potentialities of such a tool, covering the field of chemical reactions [24, 25], drug discovery [130], and material discovery for clean energy [139].”
Using attribution to decode binding mechanism in neural network models for chemistry
McCloskey, K., Taly, A., Monti, F., Brenner, M. P., & Colwell, L. J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1820657116 · Cited by 105
“However, despite decades of computational effort to develop high-resolution simulations and other approaches, we are still not able to rely solely upon virtual screening to explore the vast space of possible protein–ligand binding interactions (3).”
BackgroundCortés‐Ciriano, I., & Bender, A. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00542 · Cited by 105
Autonomous Molecular Design: Then and Now
Dimitrov, T., Kreisbeck, C., Becker, J. S., Aspuru-Guzik, A., & Saikin, S. K. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b01226 · Cited by 103
Automation and data-driven design of polymer therapeutics
Upadhya, R., Kosuri, S., Tamasi, M., Meyer, T. A., Atta, S., Webb, M. A., & Gormley, A. J. (2020) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2020.11.009 · Cited by 109
Artificial intelligence: machine learning for chemical sciences
Karthikeyan, A., & Priyakumar, U. D. (2021) · Journal of Chemical Sciences · doi:10.1007/s12039-021-01995-2 · Cited by 113
“This can lead to automated laboratories.(178) This rapidly growing field in computational science,”
Accelerating Drug Discovery: Synthesis of Complex Chemotypes via Multicomponent Reactions
Buskes, M. J., Coffin, A. R., Troast, D. M., Stein, R., & Blanco, M.-J. (2023) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.3c00012 · Cited by 103
TrimNet: learning molecular representation from triplet messages for biomedicine
Li, P., Li, Y., Hsieh, C., Zhang, S., Liu, X., Liu, H., Song, S., & Yao, X. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa266 · Cited by 110
Anticancer drug discovery from Chinese medicinal herbs
Huang, M., Zhang, L., Ding, J., & Lu, J. (2018) · Chinese Medicine · doi:10.1186/s13020-018-0192-y · Cited by 102
Flexible automation accelerates materials discovery
MacLeod, B. P., Parlane, F. G. L., Brown, A. K., Hein, J. E., & Berlinguette, C. P. (2021) · Nature Materials · doi:10.1038/s41563-021-01156-3 · Cited by 99
Practical considerations for active machine learning in drug discovery
Reker, D. (2019) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2020.06.001 · Cited by 103
“With improving flexibility of automation technology and a re-discovered enthusiasm for the deployment of artificial intelligence across drug discovery pipelines, active learning is expected to see a surge of applications [3,17,18].”
BackgroundAutomated synthesis of prexasertib and derivatives enabled by continuous-flow solid-phase synthesis
Liu, C., Xie, J., Wu, W.-B., Wang, M., Chen, W., Idres, S. B., Rong, J., Deng, L., Khan, S. A., & Wu, J. (2021) · Nature Chemistry · doi:10.1038/s41557-021-00662-w · Cited by 94
The impact of chemoinformatics on drug discovery in the pharmaceutical industry
Martínez‐Mayorga, K., Madariaga‐Mazón, A., Medina‐Franco, J. L., & Maggiora, G. M. (2020) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2020.1696307 · Cited by 97
Design automation of microfluidic single and double emulsion droplets with machine learning
Lashkaripour, A., McIntyre, D., Calhoun, S. G. K., Krauth, K., Densmore, D. M., & Fordyce, P. M. (2024) · Nature Communications · doi:10.1038/s41467-023-44068-3 · Cited by 91
Xu, S., Wang, T.-J., Liu, G., Cao, Z., Frank, L. A., Jiang, S., Zhang, C., Li, Z., Krasitskaya, V. V., Li, Q., et al. (2020) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2020.128991 · Cited by 90
Zhou, J., Dong, J., Hou, H., Huang, L., & Li, J. (2024) · Lab on a Chip · doi:10.1039/d3lc01012k · Cited by 94
Influence of artificial intelligence in modern pharmaceutical formulation and drug development
Ali, K. A., Mohin, S., Mondal, P., Goswami, S., Ghosh, S., & Choudhuri, S. (2024) · Future Journal of Pharmaceutical Sciences · doi:10.1186/s43094-024-00625-1 · Cited by 106
“Drug repurposing [31], pharmacological features [32], protein characteristics and efficacy [33], drug combination, drug-target interaction [34], and prediction of potential synthetic methods for drug-like molecules [35] are other uses of AI in the pharmaceutical industry.”
BackgroundRadhakrishnan, J., Varadaraj, S., Dash, S. K., Sharma, A., & Verma, R. S. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.03.002 · Cited by 87
Microfluidic Synthesis of Luminescent and Plasmonic Nanoparticles: Fast, Efficient, and Data‐Rich
Nette, J., Howes, P. D., & deMello, A. J. (2020) · Advanced Materials Technologies · doi:10.1002/admt.202000060 · Cited by 88
Absorption matters: A closer look at popular oral bioavailability rules for drug approvals
Soares, A. C. G., Sousa, G. H. M., Calil, R. L., & Trossini, G. H. G. (2023) · Molecular Informatics · doi:10.1002/minf.202300115 · Cited by 106
Demystifying artificial intelligence in pharmacy
Nelson, S. D., Walsh, C. G., Olsen, C. A., McLaughlin, A. J., LeGrand, J., Schutz, N., & Lasko, T. A. (2020) · American Journal of Health-System Pharmacy · doi:10.1093/ajhp/zxaa218 · Cited by 98
Drug metabolic stability in early drug discovery to develop potential lead compounds
Gajula, S. N. R., Nadimpalli, N., & Sonti, R. (2021) · Drug Metabolism Reviews · doi:10.1080/03602532.2021.1970178 · Cited by 83
“The medicinal chemist’s efforts to optimize drugs’ structural scaffolds for finding the ideal molecule is a continuous, vigorous, and recursive process (Sliwoski et al. 2014; Schneider 2018).”
Background
Metadata
- Authors
- Schneider, G.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Schneider, Gisbert
- ORCID
- Schneider, Gisbert
- Venue
- Nature Reviews Drug Discovery
- Published
- 2017-12-15
- Volume
- 17
- Issue
- 2
- Pages
- 97-113
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- OpenAlex
- W2775714759
- Cited by
- 910
- References
- 252
- Citations by year
- 2026: 167 · 2025: 154 · 2024: 119 · 2023: 89 · 2022: 87 · 2021: 92 · 2020: 79 · 2019: 74 · 2018: 47 · 2017: 1
- FWCI
- 5.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Reviews Drug Discovery (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- AstraZeneca
- OPO-Stiftung
- European Commission (HORIZON2020, 675555, 676434)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (157190, 159737)
- Horizon 2020 (676434)
- 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 › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
- Keywords
- small molecule drug discovery, artificial intelligence in drug design, compound optimization, chemical space exploration, medicinal chemistry, pharmacokinetics, drug safety
- MeSH terms
- Animals, Drug Industry, Pharmaceutical Preparations, Humans, Drug Discovery
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