Artificial intelligence foundation for therapeutic science
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Initiatives like DREAM Challenges, MoleculeNet, and Therapeutic Data Commons offer benchmark data sets across a wide spectrum of prediction tasks for pharmacology.
“Initiatives like the DREAM Challenges,284 MoleculeNet,110 and Therapeutic Data Commons285 offer benchmark data sets across a wide spectrum of prediction tasks for pharmacology.”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [285]
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MethodSelf-citationBehavioral health and generative AI: a perspective on future of therapies and patient care
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Protein language models are performant in structure-free virtual screening
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Application of artificial intelligence and machine learning in drug repurposing
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Building trust in deep learning-based immune response predictors with interpretable explanations
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“The MHC-Bench dataset is curated by combining multiple existing datasets, namely – Therapeutics Data Commons (TDC) [24, 25], the external and independent dataset from TransPHLA [11], monoallelic benchmark dataset from MHCflurry [38] and benchmark dataset from NetMHCpan [43].”
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Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for TherapeuticsPreprint
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“TDC-2 drastically expands the modalities previously available on TDC [11, 12].”
InfluentialSelf-citationMolFCL: predicting molecular properties through chemistry-guided contrastive and prompt learning
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“In the fine-tuning phase of molecular property prediction, we used nine datasets from MolecularNet (Wu et al. 2018) and 14 smaller datasets from Therapeutics Data Commons (TDC) (Huang et al. 2022).”
Impact of Generative Artificial Intelligence on Footwear Design Concept and Ideation
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Computer‐aided evaluation and exploration of chemical spaces constrained by reaction pathways
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Self-citationKnowledge-augmented Graph Machine Learning for Drug Discovery: A Survey
Zhong, Z., Barkova, A., & Mottin, D. (2025) · ACM Computing Surveys · doi:10.1145/3744237 · Cited by 19 · see citing works
“To reduce the financial burden and increase the success rate, researchers have been working on accelerating drug discovery by taking advantage of Artificial Intelligence (AI) techniques [7, 8, 9, 10, 11].”
Kamuntavičius, G., Paquet, T., Bastas, O., Šalkauskas, D., Prat, A., Aty, H. A., Pabrinkis, A., Norvaišas, P., & Tal, R. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01041-0 · Cited by 14 · see citing works
GP-MoLFormer: a foundation model for molecular generation
Ross, J., Belgodere, B. M., Hoffman, S. C., Chenthamarakshan, V., Navratil, J., Mroueh, Y., & Das, P. (2025) · Digital Discovery · doi:10.1039/d5dd00122f · Cited by 31 · see citing works
Graff, D., Pyzer‐Knapp, E. O., Jordan, K. E., Shakhnovich, E. I., & Coley, C. W. (2023) · Digital Discovery · doi:10.1039/d3dd00088e · Cited by 13 · see citing works
Self-citationHe, S., Ye, X., & Sakurai, T. (2024) · Medinformatics · doi:10.47852/bonviewmedin42022049 · Cited by 13 · see citing works
Shkil, D. O., Muhamedzhanova, A. A., Petrov, P. I., Скорб, Е. В., Aliev, T. A., Steshin, I., Tumanov, A. V., Kislinskiy, A. S., & Fedorov, M. V. (2024) · Molecules · doi:10.3390/molecules29081826 · Cited by 13 · see citing works
Fallani, A., Ирекович, Н. Р., Arjona-Medina, J. A., Wegner, J. K., Tkatchenko, A., & Chernichenko, K. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-00970-0 · Cited by 13 · see citing works
“In this section we will describe in detail the model, the datasets, the methods used for pretraining and fine-tuning as well as each of the analyses done on the fine-tuned models.”
Quantum-machine-assisted drug discovery
Zhou, Y., Chen, J., Cheng, J., Cao, X., Zhang, Y., Karemore, G., Žitnik, M., Chong, F. T., Liu, J., Fu, T., et al. (2026) · npj Drug Discovery. · doi:10.1038/s44386-025-00033-2 · Cited by 19 · see citing works
“Drug discovery and development is a highly complex and costly endeavor, typically requiring over a decade and billions of dollars to bring a single therapeutic to market [1, 2].”
Self-citationQuan, L., Wu, J., Jiang, Y., Pan, D., & Lyu, Q. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168843 · Cited by 12 · see citing works
Li, R., Lu, J., Liu, Z., Yi, D., Wan, M., Zhang, Y., Zan, P., He, S., & Bo, X. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00923-6 · Cited by 11 · see citing works
Wang, Y.-F., Li, Y., Liu, L., Hong, P., & Xu, H. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00430 · Cited by 16 · see citing works
DataSAIL: Data Splitting Against Information LeakagePreprint
Joeres, R., Blumenthal, D. B., & Kalinina, O. V. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.15.566305 · Cited by 11 · see citing works
“Some tools, such as TDCommons[15], RDKit[28], or DeepChem[16], offer complex data splits as a part of a broader framework.”
MethodAΙ-Driven Drug Repurposing: Applications and Challenges
Keramida, P., Syrigos, N. K., Kouvela, M., Poulakou, G., Charpidou, A. G., & Fiste, O. (2025) · Medicines · doi:10.3390/medicines12040028 · Cited by 12 · see citing works
“By integrating diverse biomedical data, they can identify non-obvious drug–disease associations and accelerate the discovery of novel applications for the existing drugs, while simultaneously reducing time and costs compared with traditional drug development methods [9,10].”
Zhai, Y., Chu, L., Liu, Y., Wang, D., & Wu, Y. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1844 · Cited by 18 · see citing works
“Their findings indicated that automated AI could significantly improve the efficiency of drug development ( Huang et al., 2022 ).”
BackgroundDeep Batch Active Learning for Drug DiscoveryPreprint
Bailey, M., Moayedpour, S., Li, R., Corrochano-Navarro, A., Kötter, A., Kogler-Anele, L., Riahi, S., Grebner, C., Heßler, G., Matter, H., et al. (2024) · eLife · doi:10.7554/elife.89679.2 · Cited by 10 · see citing works
Ma, J., Li, C., Zhang, Y., Wang, Z., Li, S., Guo, Y., Zhang, L., Liu, H., Gao, X., & Song, J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad524 · Cited by 9 · see citing works
Gao, C., Bao, W., Wang, S., Zheng, J., Wang, L., Ren, Y., Jiao, L., Wang, J., & Wang, X. (2024) · Briefings in Functional Genomics · doi:10.1093/bfgp/elae011 · Cited by 9 · see citing works
Vidiyala, N., Sunkishala, P., Parupathi, P. R., & Nyavanandi, D. (2025) · AAPS PharmSciTech · doi:10.1208/s12249-025-03134-3 · Cited by 9 · see citing works
“…a huge database with all the historical products and their analytical methods with the physicochemical properties of the drug substance, and chemical structure using AI will provide the analytical test methods which can be validated and employed for characterizing the drug products [93, 94].”
Multiscale-Information-Embedded Universal Toxicity Prediction Framework
Wu, L., Wang, F., Zhang, Y., Li, R., Zhao, Y., Xu, H., Gao, Z., He, S., & Bo, X. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c04614 · Cited by 10 · see citing works
Self-citationGangwal, A., & Lavecchia, A. (2026) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70072 · Cited by 9 · see citing works
“Major public benchmarks include MoleculeNet [65], TDC [21], MOSES [66], and GuacaMol.”
Controlling risks of AI in chemical science with agents
He, J., Guan, H., Feng, W., Min, Y., Yi, J., Tang, K., Li, S. C., Zhang, J., Chen, K., Zhou, W., et al. (2025) · AI for Science · doi:10.1088/3050-287x/adfee5 · Cited by 18 · see citing works
Hickman, R. J., Parakh, P., Cheng, A., Ai, Q., Schrier, J., Aldeghi, M., & Aspuru‐Guzik, A. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-74w8d · Cited by 9 · see citing works
Digitization of molecular complexity with machine learning
Tyrin, A. S., Boiko, D. A., Kolomoets, N. I., & Ananikov, V. P. (2025) · Chemical Science · doi:10.1039/d4sc07320g · Cited by 11 · see citing works
Knowledge Enhanced Representation Learning for Drug Discovery
Hoang, T. L., Sbodio, M. L., Galindo, M. M., Zayats, M., Fernández-Díaz, R., Valls, V., Picco, G., Berrospi, C., & López, V. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i9.28924 · Cited by 9 · see citing works
Quantum-Machine-Assisted Drug Discovery: Survey and PerspectivePreprint
Zhou, Y., Karemore, G., Li, W., Liang, Z., Žitnik, M., Chong, F. T., Liu, J., & Fu, T. (2024) · Preprints.org · doi:10.20944/preprints202408.1950.v1 · Cited by 8 · see citing works
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Closing the Execution Gap in Generative AI for Chemicals and Materials: Freeways or Safeguards
Subramanian, A., Gao, W., Barzilay, R., Grossman, J. C., Jaakkola, T., Jegelka, S., Li, M., Li, J., Matusik, W., Olivetti, E., et al. (2024) · An MIT Exploration of Generative AI · doi:10.21428/e4baedd9.92e511e3 · Cited by 7 · see citing works
Self-citationBetow, J. Y., Turón, G., Metuge, C. S., Akame, S., Shu, V. A., Ebob, O. T., Duran‐Frigola, M., & Ntie‐Kang, F. (2024) · Molecular Informatics · doi:10.1002/minf.202400293 · Cited by 10 · see citing works
Current advancement in AI-integrated drug discovery: Methods and applications
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Heterogeneous Catalysis under Continuous Flow Conditions
Gupta, A., Gupta, R., Arora, G., Yadav, P., & Sharma, R. K. (2023) · Current Organic Chemistry · doi:10.2174/0113852728268688230921105908 · Cited by 7 · see citing works
Wang, S., Han, Q., Qin, W., Wang, L., Yuan, J., Zhao, Y., Ren, P., Zhang, Y., Tang, Y., Li, R., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.23.619752 · Cited by 7 · see citing works
Self-citationSelective conformal inference with false coverage-statement rate control
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Zonyfar, C., Njimbouom, S. N., Mosalla, S., & Kim, J. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00915-z · Cited by 6 · see citing works
“To validate the performance of our proposed model, we utilize the same dataset that was employed by Veith et al. [28] and collect from therapeutics data commons database [29] (https:// tdcom mons. ai, accessed January 2024).”
MethodScoping review of knowledge graph applications in biomedical and healthcare sciences
Budhdeo, S., Zhang, J., Abdulle, Y., Agapow, P., McKechnie, D. G., Archer, M., Shah, V., Forte, E., Noori, A., Žitnik, M., et al. (2025) · Wellcome Open Research · doi:10.12688/wellcomeopenres.23599.1 · Cited by 6 · see citing works
DeepProtein: deep learning library and benchmark for protein sequence learning
Xie, J., Li, Y., & Fu, T. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf165 · Cited by 10 · see citing works
“The library is implemented in Python 3.9, PyTorch 2.3.0, PyTDC 0.4.1 (Huang et al. 2021a,b), DeepPurpose 0.1.5 (Huang et al. 2021a,b), and RDKit 2023.9.6 (Bento et al .”
InfluentialSelf-citationChen, Y., Guo, X., & Xue, W. (2026) · Drug Discovery Today · doi:10.1016/j.drudis.2026.104634 · Cited by 7 · see citing works
Cai, M., & Liu, T. (2026) · Pharmaceuticals · doi:10.3390/ph19050696 · Cited by 7 · see citing works
Hybrid approach for drug-target interaction predictions in ischemic stroke models
Peng, J.-J., Zhang, Y., Li, R., Zhu, W., Liu, H., Li, H.-Y., Liu, B., Cao, D., Peng, J., & Luo, X. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103067 · Cited by 5 · see citing works
Sharma, S., Yang, P., Liu, Y., Rossi, K., Bai, P., Rigutto, M. S., Zuidema, E., Agarwal, U., Baur, R., Calero, S., et al. (2025) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.5c03868 · Cited by 5 · see citing works
Wang, S., Han, Q., Qin, W., Wang, L., Yuan, J., Cai, F., Zhao, Y., Ren, P., Zhang, Y., Tang, Y., et al. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.09.036 · Cited by 5 · see citing works
“TDC is a large-scale collection of chemical datasets specifically designed to advance the development of machine learning methods in drug chemistry, materials science, and biology.”
InfluentialSelf-citationMapping the avoid-ome: a systematic open-science approach to predictive ADMET
Fraser, J. S., Edgar, S., Handly, L. N., Kosuri, S., Chodera, J. D., Murcko, M. A., & Walters, W. P. (2026) · Nature Communications · doi:10.1038/s41467-026-73410-8 · Cited by 10 · see citing works
Gowri, B. S., Sharan, M., Gehlot, Y., & Varma, V. C. (2024) · doi:10.1109/iccds60734.2024.10560446 · Cited by 5 · see citing works
- Authors
- Huang, K., Fu, T., Gao, W., Zhao, Y., Roohani, Y., Leskovec, J., Coley, C. W., Xiao, C., Sun, J., & Zitnik, M.
- Affiliations
- Harvard University (US)
- Georgia Institute of Technology (US)
- Massachusetts Institute of Technology (US)
- Carnegie Mellon University (US)
- Stanford Medicine (US)
- Stanford University (US)
- IQVIA (United States) (US)
- University of Illinois Urbana-Champaign (US)
- ORCID
- Venue
- Nature Chemical Biology
- Published
- 2022-09-21
- Volume
- 18
- Issue
- 10
- Pages
- 1033-1036
- ISSN
- 1552-4450 (ISSN-L)
- 1552-4469
- PMC
- PMC9529840
- OpenAlex
- W4296551289
- Cited by
- 207
- References
- 23
- Citations by year
- 2026: 51 · 2025: 58 · 2024: 44 · 2023: 51 · 2022: 3
- FWCI
- 20.36
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Chemical Biology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Human Genome Research Institute (U54HG010426)
- National Institute of Neurological Disorders and Stroke (R01NS107291)
- 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 › Artificial Intelligence Applications
- Keywords
- artificial intelligence, drug discovery, therapeutic modalities
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