Cited by 546Computational Drug Discovery MethodsMachine Learning in Materials ScienceArtificial Intelligence in Healthcare and Education

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How artificial intelligence is changing drug discovery

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Fleming, Nic·2018·Nature

DOI 10.1038/d41586-018-05267-x

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“Collectively, these advances project potential reductions in total development time from 10–15 years to 3–6 years and costs from $2.6 B to $1.0–1.5 B per approved drug [27,28], though long-term validation of these estimates awaits maturation of current AI-discovered candidates.”
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Background
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Background
Integrating Transformer-based Language Model for Drug Discovery
Kotkondawar, R. R., Sutar, S. R., Kiwelekar, A. W., & Kadam, V. J. (2024) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom61295.2024.10498263 · Cited by 6
“The adoption of Transformer-based Generative LMs, in Drug Discovery has risen owing to continuous advancements in computing power, the accessibility of extensive biochemical data, and sophisticated algorithms [12] [13] [14].”
Method
Development of scoring-assisted generative exploration (SAGE) and its application to dual inhibitor design for acetylcholinesterase and monoamine oxidase B
Lim, H. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00845-w · Cited by 6
“To overcome this challenge, de novo molecular design has been developed as a solution [1–3], which aims to create new molecules with the desired properties from scratch.”
Method
Extension of scoring-assisted generative exploration for ionic liquids (SAGE-IL) and its application to ionic liquid design for CO2 capture
Lim, H. (2024) · Materials Today Advances · doi:10.1016/j.mtadv.2024.100529 · Cited by 5
Machine Learning Methods In Drug Discovery: A Selective Review
Abdelkrim, A., Bouramoul, A., & Zenbout, I. (2021) · International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS) · doi:10.1109/ictaacs53298.2021.9715218 · Cited by 5
Future Directions of AI In Pharma : Innovation In Pharmaceutical Industry
-, P. B. W., -, S. P. M., & -, V. L. G. (2023) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2023.v05i03.3098 · Cited by 5
Predicting inhibitors for SARS-CoV-2 RNA-dependent RNA polymerase using machine learning and virtual screeningPreprint
Cozac, R., Medzhidov, N., & Yuki, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06523 · Cited by 4
Editorial: Computational Approaches in Drug Discovery and Precision Medicine
Huang, Z., Xiao, Y., & Gu, R. (2021) · Frontiers in Chemistry · doi:10.3389/fchem.2020.639449 · Cited by 5
“These methods, including both structure-based molecular modeling techniques (Kalyaanamoorthy and Chen, 2011) and artificial intelligence (Fleming, 2018; Williams et al., 2018; Chan et al., 2019), accelerate the discovery of drug candidates, guide the repurposing of existing drugs, improve our…”
Method
TECH-DRIVEN TRUST: THE ROLE OF AI AND EMERGING TECHNOLOGIES IN PHARMACEUTICAL QUALITY ASSURANCE
KRISHNAN, P., Krishnan, N. J. S., DEY, A., SIVAKUMAR, S., RAVICHANDRAN, S., & BHARATHI, M. (2025) · International Journal of Applied Pharmaceutics · doi:10.22159/ijap.2025v17i5.54474 · Cited by 7
Integrative machine learning-guided in silico and in vitro approach reveals selective small molecule inhibitors targeting mutant IDH1
Bajaj, M., Kumar, R., Pandey, V., Parvez, S., Tanneru, H. K., Aparoy, P., & Karnati, R. (2025) · RSC Advances · doi:10.1039/d5ra06290j · Cited by 5
“36 Only a few studies have reported methodologies for drug inhibitory activity prediction, suggesting an extensive gap to be  lled with an e ﬀ ective computational prediction model for MT-IDH1 speci  c inhibitor response.”
From Algorithms to Assets: A Comprehensive Review of AI’s Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation
Cai, M., & Liu, T. (2026) · Pharmaceuticals · doi:10.3390/ph19050696 · Cited by 8
“90% of clinical trials fail, largely because a new drug is ineffective or extremely toxic [1–3].”
Enhancement of drug discovery with machine learning clustering algorithms
Damarla, R. (2022) · Journal of High School Science · doi:10.64336/001c.35568 · Cited by 4
Computer‐aided evaluation of targets and biological activity spectra for new piperidine derivatives
Khaiitova, M. (2023) · Journal of Clinical Medicine of Kazakhstan · doi:10.23950/jcmk/13541 · Cited by 4
Fragment-pair based drug molecule solubility prediction through attention mechanism
Liu, J., Lei, X., Ji, C., et al. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1255181 · Cited by 5
Maximising the Opportunities of Artificial Intelligence for People Living With Cancer
Fenech, M. E. (2019) · Clinical Oncology · doi:10.1016/j.clon.2019.09.053 · Cited by 4
Artificial Intelligence (AI) Joins the Fight Against COVID-19
Chawki, M. (2020) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-15-9682-7_1 · Cited by 4
A Deep Learning-Based Method for Uncovering GPCR Ligand-Induced Conformational States Using Interpretability Techniques
Gutiérrez-Mondragón, M. A., König, C., & Vellido, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-07802-6_23 · Cited by 4
Recent artificial intelligence methods and coronaviruses
Rehman, S. U., Shafqat, F., & Niaz, K. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-95047-3.00009-5 · Cited by 5
Calibrating a Transformer-Based Model’s Confidence on Community-Engaged Research Studies: Decision Support Evaluation Study
Ferrell, B., Raskin, S. E., & Zimmerman, E. B. (2023) · JMIR Formative Research · doi:10.2196/41516 · Cited by 5
“It is said that AI will transform how life on this planet is currently shaped, whether that be with regard to stock trading [14], diagnosing patients [15], manufacturing [16], drug discovery [17], or even poetry [18].”
Background
Artificial Intelligence in Pharmaceutical Field: Applications, Challenges and Future Scope
Joshi, P., Nainwal, N., Rautela, J., Rajpoot, N., & Saha, P. (2022) · International Conference on Fourth Industrial Revolution Based Technology and Practices (ICFIRTP) · doi:10.1109/icfirtp56122.2022.10059451 · Cited by 4
“Combination Synergy Estimation is a tool that was designed to estimate the likelihood of a combination of antimalarial drugs having a synergistic effect [16-17].”
Method
Notice of Violation of IEEE Publication Principles: Research on Urban Land Price Assessment Based on Artificial Neural Network Model
Shousong, C., Gu, X., Wang, X., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2958978 · Cited by 4
“Fleming selected the benchmark land price coefficient correction method and income reduction method to evaluate the value of block A, and compared the differences between the two methods [9].”
Method
Retracted

Authors: Fleming, N.
Corresponding author: Fleming, Nic
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2018-05-30
Volume: 557
Issue: 7707
Pages: S55-S57
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/d41586-018-05267-x
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: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature (publishedVersion)
PubMed (publishedVersion)
RePEc: Research Papers in Economics (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
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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