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Artificial intelligence foundation for therapeutic science
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“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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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423
Drug repurposing: progress, challenges and recommendations
Pushpakom, S., Iorio, F., Eyers, P. A., Escott, K. J., Hopper, S., Wells, A., Doig, A., Guilliams, T., Latimer, J., McNamee, C., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2018.168 · Cited by 4,718
Applications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228
A Deep Learning Approach to Antibiotic Discovery
Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.021 · Cited by 2,463
Inverse molecular design using machine learning: Generative models for matter engineering
Sánchez-Lengeling, B., & Aspuru‐Guzik, A. (2018) · Science · doi:10.1126/science.aat2663 · Cited by 2,177
DeepDTA: deep drug–target binding affinity prediction
Öztürk, H., Özgür, A., & Özkırımlı, E. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty593 · Cited by 1,792
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
Impact of high-throughput screening in biomedical research
Macarron, R., Banks, M. N., Bojanic, D., Burns, D. J., Cirovic, D. A., Garyantes, T., Green, D. V. S., Hertzberg, R. P., Janzen, W. P., Paslay, J. W., et al. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3368 · Cited by 1,364
Research works printed but not linked: 3
- Gao, W., Raghavan, P. & Coley, C. W. Nat. Commun. 13, 1–4 (2022).
- Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. In Proc. 34th International Conference on Machine Learning (eds Precup, D. & Teh, Y. W.) 70, 1263–1272 (2017).
- Xie, Y. et al. In Proc. 9th International Conference on Learning Representations (Spotlight Proceedings) https://openreview.net/forum?id=kHSu4ebxFXY (2021).
Cited by
Sorted by citations
Scientific discovery in the age of artificial intelligence.
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823
Self-citationArtificial 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
“ai/) hosts multiple ML-ready datasets to advance AI4DD technologies systematically (125).”
BackgroundEvaluating explainability for graph neural networks
Agarwal, C., Queen, O., Lakkaraju, H., & Zitnik, M. (2023) · Scientific Data · doi:10.1038/s41597-023-01974-x · Cited by 186
“, Open Graph Benchmark (OGB) [17], GNNMark [18], GraphGT [19], MalNet [20], Graph Robustness Benchmark (GRB) [21], Therapeutics Data Commons [22, 23],”
A knowledge-guided pre-training framework for improving molecular representation learning
Li, H., Zhang, R., Min, Y., Ma, D., Zhao, D. Y., & Zeng, J. (2023) · Nature Communications · doi:10.1038/s41467-023-43214-1 · Cited by 173
Guiding questions to avoid data leakage in biological machine learning applications
Bernett, J., Blumenthal, D. B., Grimm, D. G., Haselbeck, F., Joeres, R., Kalinina, O. V., & List, M. (2024) · Nature Methods · doi:10.1038/s41592-024-02362-y · Cited by 184
“Various methods for computing such biochemically informed splits exist (for example, DeepChem 64 , TDCommons 65 , GraphPart 23 or the LoHi splitter 66 for one-dimensional data splitting along the drug or target axis and DataSAIL 22 for simultaneous splitting along both axes).”
Current and future directions in network biology
Žitnik, M., Li, M. M., Wells, A., Glass, K., Gysi, D. M., Krishnan, A., Murali, T. M., Predrag Radivojac, A., Roy, S., Baudot, A., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae099 · Cited by 128
“These advancements in graph representation learning have impacted areas like cancer biology, drug discovery, and disease diagnosis [95, 171, 173, 283, 390].”
BackgroundMachine Learning and Artificial Intelligence in the Multi-Omics Approach to Gut Microbiota
Rozera, T., Pasolli, E., Segata, N., & Ianiro, G. (2025) · Gastroenterology · doi:10.1053/j.gastro.2025.02.035 · Cited by 112
Foundation models for materials discovery – current state and future directions
Pyzer‐Knapp, E. O., Manica, M., Staar, P., Morin, L., Ruch, P., Laino, T., Smith, J. R., & Curioni, A. (2025) · npj Computational Materials · doi:10.1038/s41524-025-01538-0 · Cited by 144
“Thisislargelyduetothe releaseofopenbenchmarksandspecializedtoolkitsforgenerativemole-cular design, which encompass a wide range of methods, including evolutionary approaches and generative models, such as GuacaMol 90 , Moses 91 , TDC (Therapeutics Data Common) 92,93 , and GT4SD 94 .”
Data splitting to avoid information leakage with DataSAIL
Joeres, R., Blumenthal, D. B., & Kalinina, O. V. (2025) · Nature Communications · doi:10.1038/s41467-025-58606-8 · Cited by 159
TOXRIC: a comprehensive database of toxicological data and benchmarks
Wu, L., Yan, B., Han, J., Li, R., Xiao, J., He, S., & Bo, X. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1074 · Cited by 106
“There are two ML task-centric databases, TDC (17) and MoleculeNet (18), which are platforms to provide datasets, ML tasks, benchmarks, and other information for various fields of study, such as ADMET, drug combination, quantum mechanical-and biophysical-related studies.”
Computational drug repurposing: approaches, evaluation of in silico resources and case studies
Tanoli, Z., Fernández‐Torras, A., Özcan, U. O., Kushnir, A., Nader, K. M., Gadiya, Y., Fiorenza, L., Ianevski, A., Vähä-Koskela, M. J. V., Miihkinen, M., et al. (2025) · Nature Reviews Drug Discovery · doi:10.1038/s41573-025-01164-x · Cited by 104
“Two notable examples include Therapeutics Data Commons 157 and MoleculeNet 158 , which provide AI-ready datasets and benchmarks for multiple prediction tasks, including toxicity and ADR prediction, as community resources implementing various evaluation tools and leaderboards metrics.”
Tran, T. T. V., Tayara, H., & Chong, K. T. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15041260 · Cited by 105
“2 million compounds, 34,000 genes, and approximately 2 million reactions [115].”
BackgroundTurón, G., Hlozek, J., Woodland, J. G., Kumar, A., Chibale, K., & Duran‐Frigola, M. (2023) · Nature Communications · doi:10.1038/s41467-023-41512-2 · Cited by 99
Zhu, Z., Zheng, Y., Zheng, X., Qi, G., Li, Y., Mazur, N., Gao, X., Gong, Y., & Cong, B. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107621 · Cited by 94
Arnold, A., McLellan, S., & Stokes, J. M. (2025) · npj Antimicrobials and Resistance · doi:10.1038/s44259-025-00085-4 · Cited by 90
Machine Learning‐Enabled Drug‐Induced Toxicity Prediction
Bai, C., Wu, L., Li, R., Cao, Y., He, S., & Bo, X. (2025) · Advanced Science · doi:10.1002/advs.202413405 · Cited by 88
He, H., Chen, G., & Chen, C. Y. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad355 · Cited by 67
“Our data splitting was based on the TDC library (Huang et al. 2022”
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
MolE: a foundation model for molecular graphs using disentangled attention
Méndez‐Lucio, O., Nicolaou, C. A., & Earnshaw, B. A. (2024) · Nature Communications · doi:10.1038/s41467-024-53751-y · Cited by 96
“Using the ADMET tasks de fi ned in the Therapeutic Data Commons (TDC) 27 , we show that MolE is capable of being fi netuned on small datasets to achieve top performance.”
InfluentialEvaluating generalizability of artificial intelligence models for molecular datasets
Ektefaie, Y., Shen, A. N., Bykova, D., Marin, M. G., Žitnik, M., & Farhat, M. R. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00931-6 · Cited by 69
“https://doi.org/10.1038/s42256-024-00931-6 science 62–64 .”
InfluentialArtificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future Directions
Zhang, R., Wen, H., Lin, Z., Li, B., & Zhou, X. (2025) · Toxics · doi:10.3390/toxics13070525 · Cited by 56
Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry
Mroz, A. M., Basford, A. R., Hastedt, F., Jayasekera, I. S., Mosquera‐Lois, I., Sedgwick, R., Ballester, P. J., Bocarsly, J. D., del Rio‐Chanona, E. A., Evans, M. L., et al. (2025) · Chemical Society Reviews · doi:10.1039/d5cs00146c · Cited by 61
Generating Molecular Fragmentation Graphs with Autoregressive Neural Networks
Goldman, S., Li, J., & Coley, C. W. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.3c04654 · Cited by 64
“To characterize this, we analyzed the distributions for both the synthetic accessibility (SA) score [10, 18] (Fig.”
MethodSelf-citationAdvancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
Duran-Frigola, M., Cigler, M., & Winter, G. E. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11098 · Cited by 50
Behavioral health and generative AI: a perspective on future of therapies and patient care
Sezgın, E., & McKay, I. T. (2024) · npj Mental Health Research · doi:10.1038/s44184-024-00067-w · Cited by 55
Fooladi, H., Vu, T. N. L., Mathea, M., & Kirchmair, J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00475 · Cited by 59
Ma, C., Zhang, H., Rao, Y., Jiang, X., Liu, B., Sun, Z., Song, Z., Gao, Y., Cui, Y., Liu, X., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02198-6 · Cited by 61
Accelerating material design with the generative toolkit for scientific discovery
Manica, M., Born, J., Cadow, J., Christofidellis, D., Dave, A., Clarke, D., Teukam, Y. G. N., Giannone, G., Hoffman, S. C., Buchan, M. J., et al. (2023) · npj Computational Materials · doi:10.1038/s41524-023-01028-1 · Cited by 51
Generative AI for navigating synthesizable chemical space
Gao, W., Luo, S., & Coley, C. W. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2415665122 · Cited by 94
Self-citationMachine learning small molecule properties in drug discovery
Schapin, N., Majewski, M., Varela‐Rial, A., Arróniz, C., & Fabritiis, G. D. (2023) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2023.100020 · Cited by 44
Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction
Tran, T. T. V., Tayara, H., & Chong, K. T. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24031815 · Cited by 45
Autonomous biomedical research with an artificial intelligence agent
Huang, K., Zhang, S., Wang, H., Qu, Y., Lu, Y., Li, R., Roohani, Y., Qiu, L., Cao, S., Li, G. L., et al. (2026) · Science · doi:10.1126/science.adz4351 · Cited by 289
Self-citationCombinatorial prediction of therapeutic perturbations using causally inspired neural networks
Gonzalez, G., Lin, X., Herath, I. S., Veselkov, K., Bronstein, M. M., & Žitnik, M. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01481-x · Cited by 45
“Such integration would broaden the scope of PDGrapher, allowing it to capture wider mechanistic insights and support more effective therapeutic discovery 61,62 .”
Carlo, A. D., Ronchi, D., Piastra, M., Tosca, E. M., & Magni, P. (2024) · Pharmaceutics · doi:10.3390/pharmaceutics16060776 · Cited by 39
“…more comprehensive assessment of the novel features (i.e., the graph attention mechanism for both the entire molecule and its substructures), an ablation study of the newly proposed GNN architecture and the standard test procedure of the Therapeutics Data Commons (TDC) platform [28] are performed.”
MethodWognum, C., Ash, J. R., Aldeghi, M., Rodríguez-Pérez, R., Fang, C., Cheng, A. C., Price, D. J., Clevert, D.-A., Engkvist, O., & Walters, W. P. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00911-w · Cited by 42
Lam, H. Y. I., Pincket, R., Han, H., Ong, X. E., Wang, Z., Hinks, J., Wei, Y., Li, W., Zheng, L., & Mu, Y. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00683-9 · Cited by 29
“As a result, by overcoming these limitations, accurate and explainable models predicting datasets from the Tox21 18 , Therapeutic Data Commons (TDCommons) 19 and ChEMBL 20 databases could be achieved in tandem with target-specific score functions in virtual screening 21 .”
Protein language models are performant in structure-free virtual screening
Lam, H. Y. I., Guan, J. S., Ong, X. E., Pincket, R., & Mu, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae480 · Cited by 31
Progress of AI-Driven Drug–Target Interaction Prediction and Lead Optimization
Wang, Q., Sun, B., Yi, Y., Velkov, T., Shen, J., Dai, C., & Jiang, H. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms262010037 · Cited by 33
“In practice, many studies employ the same public datasets (such as MoleculeNet, TDC) to ensure fair model assessment and to compare results with prior work [85,86].”
Understanding predictions of drug profiles using explainable machine learning models
König, C., & Vellido, A. (2024) · BioData Mining · doi:10.1186/s13040-024-00378-w · Cited by 24
“The availability of publicly accessible experimental data is paramount for the advances in such ML-based QSAR prediction for ADME properties [6–9].”
MethodAdvancing predictive toxicology: overcoming hurdles and shaping the future
Masarone, S., Beckwith, K. V., Wilkinson, M. R., Tuli, S., Lane, A., Windsor, S., Lane, J. S., & Hosseini-Gerami, L. (2025) · Digital Discovery · doi:10.1039/d4dd00257a · Cited by 28
“48 TDC o ff ers a variety of cheminformatics benchmarks including toxicity, however the datasets included are limited in size and are not accompanied by relevant scien-ti c context regarding how they were generated.”
Precision cardiovascular medicine: shifting the innovation paradigm
Aikawa, M., Sonawane, A. R., Chelvanambi, S., Asano, T., Halu, A., Matamalas, J. T., Singh, S. A., Uchida, S., Aïkawa, E., Arenas, À., et al. (2025) · Frontiers in Science · doi:10.3389/fsci.2025.1474469 · Cited by 24
The rise of automated curiosity-driven discoveries in chemistry
Bustillo, L., Laino, T., & Rodrigues, T. (2023) · Chemical Science · doi:10.1039/d3sc03367h · Cited by 23
Cheng, A., Ser, C. T., Skreta, M., Guzmán-Cordero, A., Thiede, L., Burger, A., Aldossary, A., Leong, S. X., Pablo‐García, S., Strieth‐Kalthoff, F., et al. (2024) · Faraday Discussions · doi:10.1039/d4fd00153b · Cited by 22
Chemical complexity challenge: Is multi‐instance machine learning a solution?
Zankov, D. V., Madzhidov, T., Varnek, A. A., & Полищук, П. Г. (2023) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1698 · Cited by 19
Crusius, D., Cipcigan, F. S., & Biggin, P. C. (2024) · Faraday Discussions · doi:10.1039/d4fd00091a · Cited by 20
Park, J., Ahn, J., Choi, J., & Kim, J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01669 · Cited by 27
Chen, H., Liu, F., & Lin, Z. (2025) · Pharmacophore · doi:10.51847/bhyi6rjara · Cited by 18
Application of artificial intelligence and machine learning in drug repurposing
Ghandikota, S. K., & Jegga, A. G. (2024) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2024.03.030 · Cited by 24
Building trust in deep learning-based immune response predictors with interpretable explanations
Borole, P., & Rajan, A. (2024) · Communications Biology · doi:10.1038/s42003-024-05968-2 · Cited by 19
“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].”
MethodVerma, V., & Kumar, D. (2025) · Letters in Drug Design & Discovery · doi:10.1016/j.lddd.2026.100341 · Cited by 20
Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for TherapeuticsPreprint
Velez-Arce, A., Lin, X., Li, M. M., Huang, K., Gao, W., Fu, T., Pentelute, B. L., Kellis, M., & Žitnik, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.12.598655 · Cited by 20
“TDC-2 drastically expands the modalities previously available on TDC [11, 12].”
InfluentialSelf-citationMolFCL: predicting molecular properties through chemistry-guided contrastive and prompt learning
Tang, X., Zhao, Q., Wang, J., & Duan, G. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf061 · Cited by 16
“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
Cheng, S.-H. (2023) · IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) · doi:10.3390/engproc2023055032 · Cited by 16
DISGENET: Accelerating Data-Driven Discovery in Disease Genomics and Therapeutic DevelopmentPreprint
Piñero, J., Corvi, J., Rykova, N., Guillem, A., Martínez, A., Zufiaur, J. T., Rivetta, I., Holmes, S., Boccio, M. D., Shapoval, D., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.05.697749 · Cited by 16
Kalemati, M., Emani, M. Z., & Koohi, S. (2024) · BMC Genomics · doi:10.1186/s12864-024-10326-x · Cited by 15
TrialBench: Multi-Modal AI-Ready Datasets for Clinical Trial Prediction
Chen, J., Hu, Y., Cai, M., Lu, Y., Wang, Y., Cao, X., Lin, M., Xu, H., Wu, J., Cao, X., et al. (2025) · Scientific Data · doi:10.1038/s41597-025-05680-8 · Cited by 49
“Clinical trials are inherently risky as they explore “new” treatments, while artificial intelligence (AI) is particularly well-suited for making accurate estimates to reduce risk since AI excels at identifying patterns, including those not previously known to humans 6 .”
Self-citationGangwal, A., & Lavecchia, A. (2026) · Computer Science Review · doi:10.1016/j.cosrev.2026.100943 · Cited by 21
Computer‐aided evaluation and exploration of chemical spaces constrained by reaction pathways
Levin, I., Fortunato, M., Tan, K. L., & Coley, C. W. (2023) · AIChE Journal · doi:10.1002/aic.18234 · Cited by 14
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 20
“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 15
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 38
Graff, D., Pyzer‐Knapp, E. O., Jordan, K. E., Shakhnovich, E. I., & Coley, C. W. (2023) · Digital Discovery · doi:10.1039/d3dd00088e · Cited by 14
Self-citationHe, S., Ye, X., & Sakurai, T. (2024) · Medinformatics · doi:10.47852/bonviewmedin42022049 · Cited by 13
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
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 21
“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 27
“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 17
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 10
Wang, Y., Li, Y., Liu, L., Hong, P., & Xu, H. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00430 · Cited by 17
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
“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
“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
“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
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
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 13
Vidiyala, N., Sunkishala, P., Parupathi, P. R., & Nyavanandi, D. (2025) · AAPS PharmSciTech · doi:10.1208/s12249-025-03134-3 · Cited by 10
“…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
Self-citationGangwal, A., & Lavecchia, A. (2026) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70072 · Cited by 9
“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 41
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 15
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
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
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 9
Self-citationFischer, Y., Southiratn, T., Triki, D., & Cedeno, R. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01982 · Cited by 7
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
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
Current advancement in AI-integrated drug discovery: Methods and applications
Mathur, Y., Choudhury, A., Prabha, S., Saeed, M. U., Sulaimani, M. N., Mohammad, T., & Hassan, M. I. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108642 · Cited by 8
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
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
Self-citationSelective conformal inference with false coverage-statement rate control
Bao, Y., Huo, Y., Ren, H., & Zou, C. (2024) · Biometrika · doi:10.1093/biomet/asae010 · Cited by 7
Zonyfar, C., Njimbouom, S. N., Mosalla, S., & Kim, J. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00915-z · Cited by 6
“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
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 11
“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
Cai, M., & Liu, T. (2026) · Pharmaceuticals · doi:10.3390/ph19050696 · Cited by 8
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
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 8
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
“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 11
Gowri, B. S., Sharan, M., Gehlot, Y., & Varma, V. C. (2024) · doi:10.1109/iccds60734.2024.10560446 · Cited by 5
Metadata
- 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
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › 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
About these checks
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