How artificial intelligence is changing drug discovery
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AI is a major driver behind the current trend toward personalized medicine, with treatments tailored to each patient's specific biological profile.
“AI is, for example, the major driver behind the current trend toward personalized medicine, with treatments being tailored to each patient's specific biological profile (Fleming, 2018;Lichfield, 2018).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Fleming, 2018
The Pattern Recognition passage says some expect AI to accelerate a trend toward treatments designed for patients with specific biological profiles. It presents this as a potential future contribution, not as evidence that AI is already the major driver of the trend or that such tailoring is currently occurring. (LLM, conf=0.87)
Experimental — accuracy not yet measuredAI-based methods can accelerate the drug discovery process and enable the development of more effective medications.
“Many more examples are available [3,32,33,34,35,36,37], showing that AI-based methods can accelerate the drug discovery process and enable the development of more effective medications.”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [37]
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Artificial Intelligence and Management: The Automation–Augmentation Paradox
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Artificial intelligence in drug discovery and development
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“Even then, nine out of ten therapeutic molecules fail Phase II clinical trials and regulatory approval [31,32].”
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Artificial intelligence to deep learning: machine intelligence approach for drug discovery
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The role of artificial intelligence in healthcare: a structured literature review
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The ethics of AI in health care: A mapping review
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“…in diagnosis (Arieno et al., 2019; De Fauw et al., 2018; Kunapuli et al., 2018), drug discovery (Álvarez-Machancoses and Fernández-Martínez, 2019; Fleming, 2018), epidemiology (Hay et al., 2013), personalised medicine (Barton et al., 2019; Cowie et al., 2018; Dudley et al., 2015) and operational…”
BackgroundArtificial intelligence in drug development: Present status and future prospects
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ADMET modeling approaches in drug discovery
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A Structure-Based Drug Discovery Paradigm
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“, a UK-based world class AI-driven drug design company [93] is collaborating with Sanofi to find a cure for metabolic disorders [94], and Clegene, another leading pharmaceutical company, aims to accelerate drug discovery in the areas of autoimmunity and oncology [95].”
BackgroundThe role of AI in drug discovery: challenges, opportunities, and strategies
Blanco-González, A., Cabezón, A., Seco-González, A., Conde-Torres, D., Antelo-Riveiro, P., Piñeiro, Á., & Garcia-Fandino, R. (2023) · Pharmaceuticals · doi:10.3390/ph16060891 · Cited by 690 · see citing works
“Many more examples are available 3,32–37 , showing that AI-based methods can accelerate the drug discovery process and enable the development of more effective medications.”
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Artificial intelligence in COVID-19 drug repurposing
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A drug-likeness toolbox facilitates ADMET study in drug discovery
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Big data and artificial intelligence modeling for drug discovery
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“Drug attritions that happen in clinical studies induce great resource loss, and currently, nine out of ten drug candidates fail between phase I clinical trials and regulatory approval (2).”
BackgroundArtificial intelligence vs COVID-19: limitations, constraints and pitfalls
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“Even long before the COVID-19 outbreak, AI was lauded for its potential to contribute to new drug discovery, see e.g. Coldeway (2019), Fleming (2018), Segler et al. (2018) and Smith (2018).”
BackgroundEthnobotany and the Role of Plant Natural Products in Antibiotic Drug Discovery
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The significance of artificial intelligence in drug delivery system design
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Deep generative molecular design reshapes drug discovery
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Human-machine collaboration in managerial decision making
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Combining Human and Artificial Intelligence: Hybrid Problem-Solving in Organizations
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“Pharmaceutical companies including Pfizer, Roche, and Sanofi use them to discover drugs when there are either no drugs for a disease or the existing drugs have limited efficacy (Deng, Yang, Ojima, Samaras, & Wang, 2022; Fleming, 2018).”
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“The initial MIC values (MIC 0 ) for CRRI3, CRRI4, CRRI7 and penicillin are listed in Supplementary Table 15.”
BackgroundInfluentialArtificial intelligence for drug discovery: Resources, methods, and applications
Chen, W., Liu, X., Zhang, S., & Chen, S. (2023) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2023.02.019 · Cited by 288 · see citing works
“8 billion and 15 years on average.(1) The lowefficacy and high-cost characteristics of conventional methods have become the hurdles of drug discovery.”
BackgroundOpen access in silico tools to predict the ADMET profiling of drug candidates
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“molecule drugs [2,4] and 59 NCE (comprised of 64% small molecule drugs) were approved in the years 2016 and 2018, respectively.”
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“These examples illustrate that AI technologies can be applied at di ff erent levels, with the goal of facilitating the development of new pharmaceuticals quicker, cheaper, and more e ff ectively (Fleming, 2018).”
Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling
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“…improvements in computational power coupled with improvements in artificial intelligence (Al) technology, including the development of various deep learning algorithms [117–119], positioned CADD studies to progress to a new stage and capitalize on the rich infrastructure of big data studies [120].”
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Humans as Creativity Gatekeepers: Are We Biased Against AI Creativity?
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“For instance, AI is currently being used to perform creative tasks in the scientific domain—e.g., to develop new compounds for medicines (Fleming, 2018; Popova et al., 2018).”
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“It already exhibits great potential in several health application fields, from clinical diagnosis (ÁlvarezMachancoses et al. 2019; Fleming 2018), high levels of precision in cancer prediction (Kuo et al.”
BackgroundDeep Learning Based Drug Screening for Novel Coronavirus 2019-nCov
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“The latter have been applied for e.g., in QSAR models and drug design (Kadurin et al., 2017; Chen et al., 2018; Fleming, 2018; Green et al., 2018; Gupta et al., 2018; Li et al., 2018b; Lo et al., 2018; Popova et al., 2018; Simões et al., 2018) aiming at identifying systems, molecules and materials…”
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Enhancing preclinical drug discovery with artificial intelligence
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“4 The use of AI technologies is driving new opportunities across the drug discovery and development continuum, starting from target identi fi cation through to preclinical development (Fig.”
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“Empirical Strategy and Identification The data used for our primary analysis cover the years between 2010 and 2019, which capture the period of rapid advances in AI technologies (Fleming 2018).”
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Deep learning tools for advancing drug discovery and development
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“Another such DL-based tool called FRnet-DTI, which is a CNN-based classifier for drug target interaction prediction and uses an auto-encoder-based feature manipulation (Rayhan et al. 2020, 2018).”
BackgroundMethodTribulations and future opportunities for artificial intelligence in precision medicine
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“Numerous other examples highlight the capacity of AI-based methods to expedite drug screening and discovery [92] and enhance the development of more effective therapies, drug combinations for drug synergies [93, 94], and drug repurposing [88, 95–103] for various other diseases [104–110].”
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“Examples range from new drug discoveries (Fleming 2018, Paul et al. 2020) to skin cancer detection (Esteva et al. 2017), automated screening of diabetic retinopathy (Gulshan et al. 2016, 2019), fracture detection in radiographs (Badgeley et al. 2019, Qi et al. 2020, Olczak et al. 2021), detecting…”
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You Can’t Have AI Both Ways: Balancing Health Data Privacy and Access Fairly
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“…personalized medicine, clinical research, drug development, administrative processes, and the mitigation of health disparities (Shibata & Wada, 2011; Fleming, 2018; Shortliffe & Sepúlveda, 2018; Davenport & Kalakota, 2019; Fiske, Henningsen, & Buyx, 2019; Liu et al., 2019; Schork, 2019; Woo, 2019).”
Current Strategies of Antiviral Drug Discovery for COVID-19
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“In the big data era, artificial intelligence (AI) algorithms are increasingly being applied for rapid and cost-effective drug discovery (Fleming, 2018).”
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Artificial Intelligence (AI) in Drugs and Pharmaceuticals
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“Moreover, AI can create value where it previously did not exist and can benefit from unstructured data, such as in drug discovery (Noorbakhsh-Sabet et al., 2019; Fleming, 2018; Jing et al., 2018).”
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Artificial Intelligence and Machine Learning Applied at the Point of Care
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Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade
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Challenges and Opportunities in Central Nervous System Drug Discovery
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“There is evidence that some of these systems have superior diagnostic and predictive capabilities when compared to human experts (Esteva et al. 2017; Fleming 2018; Rajpurkar et al. 2017; Tschandl et al. 2019).”
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The dark side of AI identity: Investigating when and why AI identity entitles unethical behavior
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Use of artificial intelligence to enhance phenotypic drug discovery
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Integration of AI and traditional medicine in drug discovery
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DeepMalaria: Artificial Intelligence Driven Discovery of Potent Antiplasmodials
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“The validation dataset consists of the results from previously performed HTS in University of Central Florida at the Chakrabarti Laboratory, consisting of natural-products, kinase inhibitors from commercial vendors Asinex (Winston-Salem, NC)and ChemDiv (San Diego, CA) libraries.”
BackgroundInfluentialRole of Artificial Intelligence in Kidney Disease
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“The continued rapid growth in computer-processing power over the past two decades, the availability of large data sets and the development of advanced algorithms have driven major improvements in machine learning[3].”
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Williams, M., & Braddock, M. (2019) · Studia Humana · doi:10.2478/sh-2019-0001 · Cited by 74 · see citing works
Advances in preclinical approaches to Chagas disease drug discovery
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Ekert, J. E., Deakyne, J., Pribul-Allen, P., Terry, R., Schofield, C., Jeong, C. G., Storey, J., Mohamet, L., Francis, J., Naidoo, A., et al. (2020) · SLAS DISCOVERY · doi:10.1177/2472555220923332 · Cited by 68 · see citing works
The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review
Morley, J., Machado, C., Burr, C., Cowls, J., Taddeo, M., & Floridi, L. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3486518 · Cited by 66 · see citing works
Kumar, S. A., Kumar, T. D. A., Beeraka, N. M., Pujar, G. V., Singh, M., Akshatha, H. S., & Bhagyalalitha, M. (2021) · Future Medicinal Chemistry · doi:10.4155/fmc-2021-0243 · Cited by 71 · see citing works
Li, X., Xu, Y., Yao, H., & Lin, K. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00446-3 · Cited by 68 · see citing works
Application of artificial intelligence in drug design: A review
Singh, S., Kaur, N., & Gehlot, A. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108810 · Cited by 75 · see citing works
Antibiotic discovery in the artificial intelligence era
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Artificial Intelligence in Pharmacy
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PharmSD: A novel AI-based computational platform for solid dispersion formulation design
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Overcoming Consumer Resistance to AI in General Health Care
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“…cases for AI in health care — including AI assistance in health maintenance (Chun 2020), precision medicine (Abramoff, Lavin, Birch, Shah, and Folk 2018), and drug discovery (e.g., P fi zer ’ s use of IBM Watson; Fleming 2018) — demonstrate how this new technology is already disrupting the industry.”
BackgroundJebreen, K., Radwan, E., Kammoun-Rebai, W., Alattar, E., Radwan, A., Safi, W., Radwan, W., & Alajez, M. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05465-4 · Cited by 63 · see citing works
“AI applications in medicine have the potential to support a variety of tasks such as clinical research [5], therapy [6], administrative procedures [7], and drug development [8] due to their capacity to learn from large sets of clinical data.”
BackgroundArtificial Intelligence for Biology
Hassoun, S., Jefferson, F., Shi, X., Stucky, B., Wang, J., & Rosa, E. (2022) · Integrative and Comparative Biology · doi:10.1093/icb/icab188 · Cited by 64 · see citing works
Enhanced Graph Isomorphism Network for Molecular ADMET Properties Prediction
Peng, Y., Lin, Y., Jing, X., Zhang, H., Yi-ran, H., & Luo, G. S. (2020) · IEEE Access · doi:10.1109/access.2020.3022850 · Cited by 63 · see citing works
“dollars in recent years [6], [7], respectively.”
Wang, B., Zhang, T., Liu, Q., Sutcharitchan, C., Zhou, Z., Zhang, D., & Li, S. (2024) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2024.101144 · Cited by 68 · see citing works
Niazi, S. K. (2025) · Pharmaceuticals · doi:10.3390/ph18091271 · Cited by 61 · see citing works
Liu, X., Feng, H., Wu, J., & Xia, K. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab127 · Cited by 63 · see citing works
“Currently, to develop one new market-approval prescription medicine takes more than 10 years and costs about $2.6 billion [1].”
BackgroundInfluentialAn adaptive graph learning method for automated molecular interactions and properties predictions
Li, Y., Hsieh, C., Lu, R., Gong, X., Wang, X., Li, P., Liu, S., Tian, Y., Jiang, D., Yan, J., et al. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00501-8 · Cited by 62 · see citing works
“Given the perturbed test set M′ with properties Q′, we predict the property set P′ by equation (5):”
MethodInfluentialMachine intelligence in cardiovascular medicine
Miller, D. D. (2020) · Cardiology in Review · doi:10.1097/crd.0000000000000294 · Cited by 58 · see citing works
“50 Taking advantage of ultrafast parallel processing computing for the query and extract-transform-load of large proprietary and public datasets, drug R&D operations can rapidly model the spatial relationships and dynamic coupling of very large numbers of candidate compounds (ie, small molecule…”
Background
- Authors
- Fleming, N.
- Corresponding author
- Fleming, Nic
- Venue
- Nature
- Published
- 2018-05-30
- Volume
- 557
- Issue
- 7707
- Pages
- S55-S57
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2806834952
- Cited by
- 546
- References
- 0
- Citations by year
- 2026: 41 · 2025: 49 · 2024: 81 · 2023: 95 · 2022: 97 · 2021: 81 · 2020: 56 · 2019: 38 · 2018: 7
- FWCI
- 34.22
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, drug discovery, machine learning, pharmaceuticals, computer-aided drug design
- MeSH terms
- Machine Learning, Algorithms, Amyotrophic Lateral Sclerosis, Animals, Artificial Intelligence, Automation, Drug Industry, Humans, Neoplasms, Pattern Recognition, Automated, Robotics, Efficiency, Organizational, Mice, Drug Discovery, Precision Medicine
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