Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?
Open accessAbstract
As the field of artificial intelligence and machine learning (AI/ML) for drug discovery is rapidly advancing, we address the question “What is the impact of recent AI/ML trends in the area of Clinical Pharmacology?” We address difficulties and AI/ML developments for target identification, their use in generative chemistry for small molecule drug discovery, and the potential role of AI/ML in clinical trial outcome evaluation. We briefly discuss current trends in the use of AI/ML in health care and the impact of AI/ML context of the daily practice of clinical pharmacologists.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Companies like Insilico Medicine are using generative AI to discover novel compounds for treating metabolic disorders like diabetes.
“For example, companies like Insilico Medicine are using generative AI to discover novel compounds for treating metabolic disorders like diabetes (61).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [61]
The Conflict of Interest section (page unavailable) identifies Insilico Medicine as developing an AI-based drug-discovery pipeline, and the Generative Chemistry section describes AI-generated compounds. The copy does not link Insilico Medicine’s pipeline specifically to generative compound discovery or say that its compounds are intended to treat metabolic disorders such as diabetes. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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 · see citing works
Self-citationArtificial intelligence for clinical trial design
Harrer, S., Shah, P., Antony, B., & Hu, J. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.05.005 · Cited by 672 · see citing works
Unexplored therapeutic opportunities in the human genome
Oprea, T. I., Bologa, C. G., Brunak, S., Campbell, A., Gan, G. N., Gaulton, A., Gomez, S. M., Guha, R., Hersey, A., Holmes, J., et al. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2018.14 · Cited by 378 · cited 2× · see citing works
“, their function and role in human biology and disease is not understood).(3) The fact that in-depth biological insights are limited to a small number of proteins(4) is (for the most part) due to a causality dilemma: The study of specific genes and proteins requires specific biomolecular tools (e.”
BackgroundSelf-citationA Data-Driven Approach to Predicting Successes and Failures of Clinical Trials
Gayvert, K. M., Madhukar, N. S., & Elemento, O. (2016) · Cell chemical biology · doi:10.1016/j.chembiol.2016.07.023 · Cited by 310 · see citing works
Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery
Polykovskiy, D., Zhebrak, A., Vetrov, D., Ivanenkov, Y., Aladinskiy, V., Mamoshina, P., Bozdaganyan, M., Aliper, A., Zhavoronkov, A., & Kadurin, A. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00839 · Cited by 278 · see citing works
Self-citationUrsu, O., Rayan, A., Goldblum, A., & Oprea, T. I. (2011) · WIREs Computational Molecular Science · doi:10.1002/wcms.52 · Cited by 276 · see citing works
Self-citationAssessing the impact of generative AI on medicinal chemistry
Walters, W. P., & Murcko, M. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0418-2 · Cited by 262 · see citing works
Flagged first, then by citations
Research works printed but not linked: 18
- [1]Aronson, J.K. A manifesto for clinical pharmacology from principles to practice. Br. J. Clin. Pharmacol. 70, 3– 13 (2010). 20642541 PMC2909801
- [2]Santos, R. et al A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19– 34 (2017). 27910877 PMC6314433
- [4]Edwards, A.M. et al Too many roads not taken. Nature 470, 163– 165 (2011). 21307913
- [5]Sun, Y., Barber, R., Gupta, M., Aggarwal, C.C. & Han, J. Co‐author relationship prediction in heterogeneous bibliographic networks. International Conference on Advances in Social Networks Analysis and Mining, Kaohsiung, Taiwan, July 25, 2011–July 27, 2011.
- [6]Papadimitriou, S. et al Predicting disease‐causing variant combinations. Proc. Natl. Acad. Sci. USA 116, 11878– 11887 (2019). 31127050 PMC6575632
- [7]Bang, S., Kim, J.‐H. & Shin, H. Causality modeling for directed disease network. Bioinformatics 32, i437– i444 (2016). 27587660
- [9]Artemov, A.V., Putin, E., Vanhaelen, Q., Aliper, A., Ozerov, I.V., & Zhavoronkov, A. Integrated deep learned transcriptomic and structure‐based predictor of clinical trials outcomes. bioRxiv (2016). 10.1101/095653 · DOI
- [11]Viceconti, M., Henney, A. & Morley‐Fletcher, E. In silico clinical trials: how computer simulation will transform the biomedical industry. Int. J. Clin. Trials 3, 37 (2016).
- [13]Ursu, O. & Oprea, T.I. Model‐free drug‐likeness from fragments. J. Chem. Inf. Model. 50, 1387– 1394 (2010). 20726597
- [14]Goodfellow, J. et al Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27, 2672– 2680 (2014).
- [15]Kulkarni, P. Reverse Hypothesis Machine Learning. (Springer, Cham, Switzerland, 2017).
- [16]Kadurin, A. et al The cornucopia of meaningful leads: applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget 8, 10883– 10890 (2017). 28029644 PMC5355231
- [17]Durant, J.L., Leland, B.A., Henry, D.R. & Nourse, J.G. Reoptimization of MDL keys for use in drug discovery. J. Chem. Inf. Comput. Sci. 42, 1273– 1280 (2002). 12444722
- [19]Akutsu, T. & Nagamochi, H. Comparison and enumeration of chemical graphs. Comput. Struct. Biotechnol. J. 5, e201302004 (2013). 24688697 PMC3962186
- [20]Putin, E. et al Adversarial threshold neural computer for molecular de novo design. Mol. Pharm. 15, 4386– 4397 (2018). 29569445
- [21]Kuzminykh, D. et al 3D Molecular representations based on the wave transform for convolutional neural networks. Mol. Pharm. 15, 4378– 4385 (2018). 29473756
- [22]Polykovskiy, D. et al Molecular sets (MOSES): a benchmarking platform for molecular generation models. arXiv [cs.LG] (2018) < http://arxiv.org/abs/1811.12823>. PMC7775580 33390943 · arXiv
- [25]Kheshti, R., Aalipour, M. & Namazi, S. A comparison of five common drug‐drug interaction software programs regarding accuracy and comprehensiveness. Am. J. Pharmacogenomics 5, 257– 263 (2016). PMC5084483 27843962
Cited by
Sorted by citations
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473 · see citing works
“…organic compounds with the help of computer programs, designing new compounds, developing assays, and predicting the 3D structure of target molecules are some time-consuming and tiresome tasks which with the help of AI can be reduced and can speed up the process of drug discovery [94, 95].”
BackgroundArtificial intelligence applied to clinical trials: opportunities and challenges
Askin, S., Burkhalter, D., Calado, G., & El Dakrouni, S. (2023) · Health and Technology · doi:10.1007/s12553-023-00738-2 · Cited by 285 · see citing works
Artificial Intelligence for Drug Discovery: Are We There Yet?
Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236 · see citing works
Self-citationThe Potential Applications of Artificial Intelligence in Drug Discovery and Development
Farghali, H., Kutinová Canová, N., & Arora, M. (2021) · Physiological Research · doi:10.33549/physiolres.934765 · Cited by 72 · see citing works
Osonuga, A., Olawade, D. B., Gore, M., Rotifa, O. B., Egbon, E., Chinwah, V., & Boussios, S. (2025) · Annals of Translational Medicine · doi:10.21037/atm-25-62 · Cited by 4 · see citing works
- Authors
- Zhavoronkov, A., Vanhaelen, Q., & Oprea, T. I.
- Affiliations
- Insilico Medicine (United States) (US)
- University of Copenhagen (DK)
- Corresponding author
- Oprea, Tudor I.
- Venue
- Clinical Pharmacology & Therapeutics
- Publisher
- Wiley
- Published
- 2020-03-03
- Volume
- 107
- Issue
- 4
- Pages
- 780-785
- ISSN
- 0009-9236 (ISSN-L)
- 1532-6535
- PMC
- PMC7158211
- OpenAlex
- W3001449808
- Cited by
- 159
- References
- 7
- Citations by year
- 2026: 22 · 2025: 14 · 2024: 34 · 2023: 35 · 2022: 15 · 2021: 29 · 2020: 10
- FWCI
- 11.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Clinical Pharmacology & Therapeutics (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by-nc)
- Funders
- National Institutes of Health (P30 CA118100, U24 CA224370, U01 CA239108, U24 TR002278)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- artificial intelligence, machine learning, drug discovery, clinical pharmacology, target identification, generative chemistry, small molecule drug discovery, artificial intelligence in healthcare
- MeSH terms
- Animals, Artificial Intelligence, Clinical Trials as Topic, Humans, Pharmacology, Clinical, 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 ›
Are you an author, or do you see a problem with this page? Request a review
