Deep learning for low-data drug discovery: Hurdles and opportunities
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Abstract
Deep learning is becoming increasingly relevant in drug discovery, from de novo design to protein structure prediction and synthesis planning. However, it is often challenged by the small data regimes typical of certain drug discovery tasks. In such scenarios, deep learning approaches-which are notoriously 'data-hungry'-might fail to live up to their promise. Developing novel approaches to leverage the power of deep learning in low-data scenarios is sparking great attention, and future developments are expected to propel the field further. This mini-review provides an overview of recent low-data-learning approaches in drug discovery, analyzing their hurdles and advantages. Finally, we venture to provide a forecast of future research directions in low-data learning for drug discovery.
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
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 · see citing works
Advances in Neural Information Processing Systems 14
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 9,184 · see citing works
Weininger, D. (1988) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00057a005 · Cited by 7,436 · see citing works
ZINC − A Free Database of Commercially Available Compounds for Virtual Screening
Irwin, J. J., & Shoichet, B. K. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci049714+ · Cited by 4,058 · see citing works
Geometric Deep Learning: Going beyond Euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661 · see citing works
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106 · see citing works
Planning chemical syntheses with deep neural networks and symbolic AI
Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature · doi:10.1038/nature25978 · Cited by 1,847 · see citing works
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.
Gómez‐Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera‐Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru‐Guzik, A. (2018) · Cambridge University Engineering Department Publications Database · doi:10.17863/cam.34134 · Cited by 1,813 · see citing works
Text Data Augmentation for Deep Learning
Shorten, C., Khoshgoftaar, T. M., & Furht, B. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00492-0 · Cited by 1,798 · see citing works
Zdrazil, B., Félix, E., Hunter, F. M. I., Manners, E. J., Blackshaw, J., Corbett, S., Veij, M. D., Ioannidis, H., Méndez, D., Morales, J. F. M., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1004 · Cited by 1,463 · see citing works
Molecular de-novo design through deep reinforcement learning
Olivecrona, M., Blaschke, T., Engkvist, O., & Chen, H. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0235-x · Cited by 1,358 · see citing works
Drug discovery with explainable artificial intelligence
Jiménez-Luna, J., Grisoni, F., & Schneider, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00236-4 · Cited by 1,063 · see citing works
Self-citationA survey on deep learning for multimodal data fusion
Gao, J., Li, P., Chen, Z., & Zhang, J. (2020) · Neural Computation · doi:10.1162/neco_a_01273 · Cited by 821 · see citing works
Low Data Drug Discovery with One-Shot Learning
Altae-Tran, H., Ramsundar, B., Pappu, A. S., & Pande, V. (2017) · ACS Central Science · doi:10.1021/acscentsci.6b00367 · Cited by 799 · see citing works
Molecular representations in AI-driven drug discovery: a review and practical guide
David, L., Thakkar, A., Mercado, R., & Engkvist, O. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00460-5 · Cited by 587 · see citing works
Machine Learning Methods for Small Data Challenges in Molecular Science
Dou, B., Zhu, Z., Merkurjev, E., Ke, L., Chen, L., Jian, J., Zhu, Y., Liu, J., Zhang, B., & Wei, G. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00189 · Cited by 522 · see citing works
REINVENT 2.0: An AI Tool for De Novo Drug Design
Blaschke, T., Arús-Pous, J., Chen, H., Margreitter, C., Tyrchan, C., Engkvist, O., Papadopoulos, K., & Patronov, A. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00915 · Cited by 496 · see citing works
SMILES Enumeration as Data Augmentation for Neural Network Modeling of MoleculesPreprint
Bjerrum, E. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.07076 · Cited by 411 · see citing works
Geometric deep learning on molecular representations
Atz, K., Grisoni, F., & Schneider, G. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00418-8 · Cited by 387 · see citing works
Self-citationRandomized SMILES strings improve the quality of molecular generative models
Arús‐Pous, J., Johansson, S., Prykhodko, O., Bjerrum, E. J., Tyrchan, C., Reymond, J., Chen, H., & Engkvist, O. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0393-0 · Cited by 384 · see citing works
Transfer Learning for Drug Discovery
Cai, C., Wang, S., Xu, Y., Zhang, W., Tang, K., Ouyang, Q., Lai, L., & Pei, J. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b02147 · Cited by 363 · see citing works
Vanschoren, J. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-05318-5_2 · Cited by 361 · see citing works
Exposing the Limitations of Molecular Machine Learning with Activity Cliffs
van Tilborg, D., Alenicheva, A., & Grisoni, F. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01073 · Cited by 337 · see citing works
Self-citationActive-learning strategies in computer-assisted drug discovery
Reker, D., & Schneider, G. (2014) · Drug Discovery Today · doi:10.1016/j.drudis.2014.12.004 · Cited by 262 · see citing works
SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug DiscoveryPreprint
Honda, S., Shi, S., & Ueda, H. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.04738 · Cited by 260 · see citing works
Huang, Y., Lin, J.-Y., Zhou, C., Yang, H.-X., & Huang, L.-B. (2022) · International Conference on Machine Learning · doi:10.48550/arxiv.2203.12221 · Cited by 238 · see citing works
A systematic study of key elements underlying molecular property prediction
Deng, J., Yang, Z., Wang, H., Ojima, I., Samaras, D., & Wang, F. (2023) · Nature Communications · doi:10.1038/s41467-023-41948-6 · Cited by 233 · see citing works
Generative molecular design in low data regimes
Moret, M., Friedrich, L., Grisoni, F., Merk, D., & Schneider, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0160-y · Cited by 219 · see citing works
Self-citationVolkov, M., Turk, J.-A., Drizard, N., Martin, N., Hoffmann, B., Gaston‐Mathé, Y., & Rognan, D. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00487 · Cited by 182 · see citing works
Combining IC 50 or K i Values from Different Sources Is a Source of Significant Noise
Landrum, G. A., & Riniker, S. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00049 · Cited by 177 · see citing works
Chemical language models enable navigation in sparsely populated chemical space
Skinnider, M. A., Stacey, R. G., Wishart, D. S., & Foster, L. J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00368-1 · Cited by 169 · see citing works
On failure modes in molecule generation and optimization
Renz, P., Rompaey, D. V., Wegner, J. K., Hochreiter, S., & Klambauer, G. (2019) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2020.09.003 · Cited by 154 · see citing works
Merk, D., Grisoni, F., Friedrich, L., & Schneider, G. (2018) · Communications Chemistry · doi:10.1038/s42004-018-0068-1 · Cited by 136 · see citing works
Self-citationMultimodal Prototypical Networks for Few-shot Learning
Pahde, F., Puscas, M., Klein, T., & Nabi, M. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00269 · Cited by 116 · see citing works
Understanding and Improving Information Transfer in Multi-Task LearningPreprint
Wu, S., Zhang, H. R., & Ré, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.00944 · Cited by 113 · see citing works
Deep generative design with 3D pharmacophoric constraints
Imrie, F., Hadfield, T. E., Bradley, A. R., & Deane, C. M. (2021) · Chemical Science · doi:10.1039/d1sc02436a · Cited by 113 · see citing works
Meta Learning With Graph Attention Networks for Low-Data Drug Discovery
Lv, Q., Chen, G., Yang, Z., Zhong, W., & Chen, C. Y. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3250324 · Cited by 108 · see citing works
A Survey of Multi‐task Learning Methods in Chemoinformatics
Sosnin, S., Vashurina, M., Withnall, M., Karpov, P., Fedorov, M. V., & Tetko, I. V. (2018) · Molecular Informatics · doi:10.1002/minf.201800108 · Cited by 82 · see citing works
Structure-Aware Multimodal Deep Learning for Drug–Protein Interaction Prediction
Wang, P., Zheng, S., Jiang, Y., Li, C., Liu, J., Wen, C., Patronov, A., Qian, D., Chen, H., & Yang, Y. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00060 · Cited by 80 · see citing works
The influence of negative training set size on machine learning-based virtual screening
Kurczab, R., Podlewska, S., & Bojarski, A. J. (2014) · Journal of Cheminformatics · doi:10.1186/1758-2946-6-32 · Cited by 78 · see citing works
Structure‐Based Drug Discovery with Deep Learning**
Özçelik, R., van Tilborg, D., Jiménez-Luna, J., & Grisoni, F. (2023) · ChemBioChem · doi:10.1002/cbic.202200776 · Cited by 69 · see citing works
Self-citationOptiMol : Optimization of Binding Affinities in Chemical Space for Drug Discovery
Boitreaud, J., Mallet, V., Oliver, C., & Waldispühl, J. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00833 · Cited by 67 · see citing works
De Novo Design of Nurr1 Agonists via Fragment-Augmented Generative Deep Learning in Low-Data Regime
Ballarotto, M., Willems, S., Stiller, T., Nawa, F., Marschner, J. A., Grisoni, F., & Merk, D. (2023) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.3c00485 · Cited by 54 · see citing works
Self-citationEnhancing Protein–Ligand Binding Affinity Predictions Using Neural Network Potentials
Zariquiey, F. S., Galvelis, R., Gallicchio, E., Chodera, J. D., Markland, T. E., & Fabritiis, G. D. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c02031 · Cited by 54 · see citing works
Hechinger, M., Leonhard, K. O., & Marquardt, W. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300246m · Cited by 53 · see citing works
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural LanguagePreprint
Su, B., Du, D., Zhao, Y., Yujie, Z., Li, J., Rao, A., Sun, H., Lu, Z., & Wen, J.-R. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2209.05481 · Cited by 44 · see citing works
AugLiChem: data augmentation library of chemical structures for machine learning
Magar, R., Wang, Y., Lorsung, C., Chen, L., Ramasubramanian, H., Li, P., & Farimani, A. B. (2022) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ac9c84 · Cited by 43 · see citing works
COVER: conformational oversampling as data augmentation for molecules
Hemmerich, J., Asilar, E., & Ecker, G. F. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00420-z · Cited by 40 · see citing works
Review of unsupervised pretraining strategies for molecules representation
Yu, L., Su, Y., Liu, Y., & Zeng, X. (2021) · Briefings in Functional Genomics · doi:10.1093/bfgp/elab036 · Cited by 39 · see citing works
QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach
Zankov, D. V., Matveieva, M., Nikonenko, A., Ирекович, Н. Р., Baskin, I. I., Varnek, A. A., Полищук, П. Г., & Madzhidov, T. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00692 · Cited by 36 · see citing works
Explaining and avoiding failure modes in goal-directed generation of small molecules
Langevin, M., Vuilleumier, R., & Bianciotto, M. (2022) · Journal of Cheminformatics · doi:10.1186/s13321-022-00601-y · Cited by 32 · see citing works
Predicting molecular activity on nuclear receptors by multitask neural networks
Valsecchi, C., Collarile, M., Grisoni, F., Todeschini, R., Ballabio, D., & Consonni, V. (2020) · Journal of Chemometrics · doi:10.1002/cem.3325 · Cited by 26 · see citing works
Self-citationPerplexity-Based Molecule Ranking and Bias Estimation of Chemical Language Models
Moret, M., Grisoni, F., Katzberger, P., & Schneider, G. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00079 · Cited by 25 · see citing works
Self-citationFS-Mol: A Few-Shot Learning Dataset of Molecules
Stanley, M., Bronskill, J. F., Maziarz, K., Misztela, H., Lanini, J., Segler, M., Schneider, N., & Brockschmidt, M. (2021) · neural information processing systems · Cited by 24 · see citing works
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguagePreprint
Seidl, P., Vall, A., Hochreiter, S., & Klambauer, G. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.03363 · Cited by 24 · see citing works
Kwapień, K., Nittinger, E., He, J., Margreitter, C., Voronov, A., & Tyrchan, C. (2022) · ACS Omega · doi:10.1021/acsomega.2c02738 · Cited by 22 · see citing works
Benchmarking 2D/3D/MD-QSAR Models for Imatinib Derivatives: How Far Can We Predict?
Zin, P. P. K., Borrel, A., & Fourches, D. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00200 · Cited by 19 · see citing works
NoiseMol: A noise-robusted data augmentation via perturbing noise for molecular property prediction
Jiang, J., Zhang, R., Yuan, Y., Li, T., Li, G., Zhao, Z., & Yu, Z. (2023) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2023.108454 · Cited by 19 · see citing works
Ji, Y., Zhang, L., Wu, J., Wu, B., Huang, L.-K., Xu, T., Rong, Y., Li, L., Ren, J., Ding, X., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2201.09637 · Cited by 19 · see citing works
Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects
Fluetsch, A., Lascio, E. D., Gerebtzoff, G., & Rodríguez-Pérez, R. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.3c01124 · Cited by 15 · see citing works
Meta-Learning Initializations for Low-Resource Drug DiscoveryPreprint
Nguyen, C. Q., Kreatsoulas, C., & Branson, K. (2020) · ChemRxiv · doi:10.26434/chemrxiv.11981622.v1 · Cited by 13 · see citing works
Meta-Learning Initializations for Low-Resource Drug DiscoveryPreprint
Nguyen, C. Q., Kreatsoulas, C., & Branson, K. (2020) · ChemRxiv · doi:10.26434/chemrxiv.11981622 · Cited by 11 · see citing works
Context-enriched molecule representations improve few-shot drug discoveryPreprint
Schimunek, J., Seidl, P., Friedrich, L., Kühn, D., Rippmann, F., Hochreiter, S., & Klambauer, G. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.09481 · Cited by 11 · see citing works
Berrevoets, J., Kacprzyk, K., Qian, Z., & van der Schaar, M. (2023) · arXiv · doi:10.48550/arxiv.2303.02186 · Cited by 8 · see citing works
Swallowing the Bitter Pill: Simplified Scalable Conformer Generation
Wang, Y., Elhag, A. A., Jaitly, N., Susskind, J. M., & Bautista, M. A. (2023) · arXiv · doi:10.48550/arxiv.2311.17932 · Cited by 7 · see citing works
Learning Over Molecular Conformer Ensembles: Datasets and BenchmarksPreprint
Zhu, Y., Hwang, J., Adams, K., Liu, Z., Nan, B., Stenfors, B. A., Du, Y., Chauhan, J., Wiest, O., Isayev, O., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.00115 · Cited by 5 · see citing works
In-Context Learning for Few-Shot Molecular Property PredictionPreprint
Fifty, C., Leskovec, J., & Thrun, S. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.08863 · Cited by 4 · see citing works
Traversing Chemical Space with Active Deep LearningPreprint
van Tilborg, D., & Grisoni, F. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-wgl32 · Cited by 4 · see citing works
Self-citationFunctionally Regionalized Knowledge Transfer for Low-resource Drug Discovery
Yao, H., Wei, Y., Huang, L.-K., Ding, X. W., Huang, J., & Li, Z. (2021) · neural information processing systems · Cited by 3 · see citing works
Medical Multimodal Classifiers Under Scarce Data ConditionPreprint
Aydin, F., Zhang, M., Ananda-Rajah, M. R., & Haffari, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.08888 · Cited by 3 · see citing works
Flagged first, then by citations
2 of the 73 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Machine learning-guided high throughput nanoparticle design
Ortiz‐Perez, A., van Tilborg, D., van der Meel, R., Grisoni, F., & Albertazzi, L. (2024) · Digital Discovery · doi:10.1039/d4dd00104d · Cited by 113 · see citing works
Self-citationTraversing chemical space with active deep learning for low-data drug discovery
van Tilborg, D., & Grisoni, F. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00697-2 · Cited by 51 · see citing works
Self-citationGenerative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey
Özçelik, R., Brinkmann, H., Criscuolo, E., & Grisoni, F. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00641 · Cited by 48 · see citing works
Self-citationModeling protein–ligand interactions for drug discovery in the era of deep learning
Wang, Y. X., Li, Y., Chen, J., & Lai, L. (2025) · Chemical Society Reviews · doi:10.1039/d5cs00415b · Cited by 46 · see citing works
The predictivity of QSARs for toxicity: Recommendations for improving model performance
Cronin, M., Basiri, H., Chrysochoou, G., Enoch, S. J., Firman, J. W., Sp̂înu, N., & Madden, J. C. (2024) · Computational Toxicology · doi:10.1016/j.comtox.2024.100338 · Cited by 41 · see citing works
Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery
Pallikkavaliyaveetil, N., & Chandrasekaran, S. (2025) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2025.115762 · Cited by 41 · see citing works
Bernal, L., Rastelli, G., & Pinzi, L. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02015 · Cited by 31 · see citing works
A comprehensive review of emerging approaches in machine learning for de novo PROTAC design
Gharbi, Y., & Mercado, R. (2024) · Digital Discovery · doi:10.1039/d4dd00177j · Cited by 31 · see citing works
Frasnetti, E., Cucchi, I., Pavoni, S., Frigerio, F., Cinquini, F., Serapian, S. A., Pavarino, L. F., & Colombo, G. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01097 · Cited by 19 · see citing works
A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data
MacDermott-Opeskin, H., Scheen, J., Wognum, C., Horton, J. T., West, D., Payne, A. M., Castellanos, M. A., Colby, S., Griffen, E., Cousins, D. L., et al. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02106 · Cited by 24 · see citing works
Schuh, M. G., Boldini, D., & Sieber, S. A. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00765 · Cited by 18 · see citing works
Advances in bioinformatic methods for the acceleration of the drug discovery from nature
Maciejewska‐Turska, M., Georgiev, M. I., Kai, G., & Sieniawska, E. (2025) · Phytomedicine · doi:10.1016/j.phymed.2025.156518 · Cited by 17 · see citing works
Generalizable, fast, and accurate DeepQSPR with fastprop
Burns, J. W., & Green, W. H. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01013-4 · Cited by 20 · see citing works
Manen-Freixa, L., & Antolín, A. A. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2376643 · Cited by 15 · see citing works
A hitchhiker's guide to deep chemical language processing for bioactivity prediction
Özçelik, R., & Grisoni, F. (2024) · Digital Discovery · doi:10.1039/d4dd00311j · Cited by 15 · see citing works
Self-citationEnhancing molecular property prediction through data integration and consistency assessment
Parrondo-Pizarro, R., Menestrina, L., García-Serna, R., Fernández‐Torras, A., & Mestres, J. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01103-3 · Cited by 15 · see citing works
Automated navigation of condensate phase behavior with active machine learning
Leurs, Y. H. A., van den Hout, W., Gardin, A., van Dongen, J. L. J., Rodriguez‐Abetxuko, A., Erkamp, N. A., van Hest, J. C. M., Grisoni, F., & Brunsveld, L. (2025) · Nature Communications · doi:10.1038/s41467-025-64617-2 · Cited by 13 · see citing works
Self-citationFrom Prompt to Drug: Toward Pharmaceutical Superintelligence
Zhavoronkov, A. A., Gennert, D. G., & Shi, J. (2026) · ACS Central Science · doi:10.1021/acscentsci.5c01473 · Cited by 10 · see citing works
Li, X., Zhang, F., Zheng, L., & Guo, J. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.134828 · Cited by 9 · see citing works
Finding the most potent compounds using active learning on molecular pairs
Fralish, Z., & Reker, D. (2024) · Beilstein Journal of Organic Chemistry · doi:10.3762/bjoc.20.185 · Cited by 9 · see citing works
Identifying Dynamic Metal–Ligand Coordination Modes with Ensemble Learning
Toney, J., Michel, R. G. S., Garrison, A. G., Kevlishvili, I., & Kulik, H. J. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.5c17169 · Cited by 12 · see citing works
Ivković, Ž., Jover, J., & Harvey, J. N. (2024) · Digital Discovery · doi:10.1039/d4dd00168k · Cited by 10 · see citing works
Going beyond SMILES enumeration for data augmentation in generative drug discovery
Brinkmann, H., Argante, A., ter Steege, H., & Grisoni, F. (2025) · Digital Discovery · doi:10.1039/d5dd00028a · Cited by 10 · see citing works
Self-citationBanerjee, A., Kar, S., Roy, K., Patlewicz, G. Y., Shah, I., Karamertzanis, P. G., Gini, G. C., & Benfenati, E. (2025) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70057 · Cited by 8 · see citing works
Liu, Q., Wang, T., Nian, B., Ma, F., Zhao, S., Vásquez, A. F., Guo, L., Ding, C., & Davari, M. D. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.108803 · Cited by 7 · see citing works
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 · see citing works
Application and Prospects of Large Language Models in Small-Molecule Drug Discovery
Ma, J., Liu, J., Xu, D., Song, X., & Zhang, Z. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.5c04083 · Cited by 9 · see citing works
Álvarez-Gómez, C., Fonseca-Benítez, A. V., & Guevara‐Pulido, J. O. (2025) · PLoS ONE · doi:10.1371/journal.pone.0319415 · Cited by 6 · see citing works
Baker, C., Acquah, F. A., Chivukula, L. G., Wu, L., Philippe-Venec, L., Abedi, M., Tao, Y., Ramirez, D., McCoy, M., Moore, B. D., et al. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c01836 · Cited by 5 · see citing works
van Tilborg, D., & Grisoni, F. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2023-wgl32-v3 · Cited by 5 · see citing works
Self-citationGui, B., Sam, L., Bhardwaj, A., Soto‐Gómez, D., Peñaloza, F. G., Buchroithner, M. F., & Green, D. R. (2025) · International Journal of Remote Sensing · doi:10.1080/01431161.2025.2532837 · Cited by 4 · see citing works
Predicting Prognosis of Early-Stage Mycosis Fungoides with Utilization of Machine Learning
Mendi, B. İ., Sanlı, H. E., İnsel, M. A., Bayındır, B., & Atak, M. F. (2024) · Life · doi:10.3390/life14111371 · Cited by 4 · see citing works
AI-driven drug discovery using a context-aware hybrid model to optimize drug-target interactions
Kumar, A., Gupta, S. K., & Kim, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-19593-4 · Cited by 6 · see citing works
Valsecchi, C., Arjona-Medina, J. A., Dyubankova, N., & Ирекович, Н. Р. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01004-5 · Cited by 3 · see citing works
Deep Learning Integration in Optical Microscopy: Advancements and Applications
Lahari, P. V., Dutta, S., Deeksha, H., Patel, S. A., Dehury, B., & Mazumder, N. (2026) · Microscopy Research and Technique · doi:10.1002/jemt.70112 · Cited by 3 · see citing works
Harnessing Machine Learning for Accelerated Drug Discovery: Opportunities and Unmet Challenges
El-Tanani, M., Rabbani, S. A., Wali, A. F., Muhana, F., El‐Tanani, Y., & Kumar, R. (2026) · Pharmaceuticals · doi:10.3390/ph19060810 · Cited by 4 · see citing works
Deep learning computer vision system for estimating sheep age using teeth images
Hassanat, A. B. A., Al-Sarayreh, M. A., Tarawneh, A. S., Abbadi, M. A., Almohammadi, K., Alghamdi, M., Alamri, M., Alzahrani, A., & Altarawneh, G. (2025) · Connection Science · doi:10.1080/09540091.2025.2506456 · Cited by 2 · see citing works
Doering, N. P., Talagayev, V., Liu, S., & Wolber, G. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02014 · Cited by 2 · see citing works
Msobo, A., Maphari, P. W., Koorsen, G., Singab, A., & Mhlongo, M. (2026) · In Silico Pharmacology · doi:10.1007/s40203-026-00620-9 · Cited by 2 · see citing works
Acaru, S. F., Comí, M., Falireas, P. G., Vanpoucke, D. E. P., Vendamme, R., & Bernaerts, K. V. (2026) · Materials & Design · doi:10.1016/j.matdes.2026.116265 · Cited by 2 · see citing works
Enhancing deep chemical reaction prediction with advanced chirality and fragment representation
Mastrolorito, F., Ciriaco, F., Nicolotti, O., & Grisoni, F. (2025) · Chemical Communications · doi:10.1039/d5cc02641e · Cited by 1 · see citing works
Self-citationNoto, N., Nagano, T., Fujinami, M., Kojima, R., & Saito, S. (2025) · Communications Chemistry · doi:10.1038/s42004-025-01678-w · Cited by 1 · see citing works
Wang, X., Wang, Y., Huang, T.-Y., Dong, Y., Deng, J., Xu, L., Li, X., & He, R. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356718 · Cited by 9 · see citing works
Beyond the Hype: The Reality of Implementing AI to Drive Drug Discovery Success
Segall, M. (2024) · GEN Biotechnology · doi:10.1089/genbio.2024.0042
Kinase Drug Discovery: An Artificial Intelligence Revolution
Johnson, T., Tulshan, N., & Mathuram, T. L. (2025) · AI in Precision Oncology · doi:10.1089/aipo.2025.0002
Radaideh, M. I., Raymond, M., Elvati, P., Saldinger, J. C., Radaideh, M. I., & Violi, A. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100528
Stability of Machine Learning Predictive Features Under Limited Data
Capała, K., Tworek, P., & Sousa, J. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3580671
Cross-Platform Deep Learning Compilation with Model-Specific Cost Model Pre-training
Wu, X., Zhao, J., Xu, P., & Liu, C. H. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-94892-3_7
Few-Shot Learning in Drug Discovery
Schimunek, J., & Klambauer, G. (2026) · Springer eBooks · doi:10.1007/978-3-031-98022-0_16 · Cited by 1 · see citing works
Gene Ontology-Based Drug Function Identification via Multi-Output Deep Learning Framework
Das, P., Chhetry, B., & Borah, R. J. (2025) · IEEE Silchar Subsection Conference (SILCON) · doi:10.1109/silcon67893.2025.11327077
Lance, C., Shitov, V. A., Wen, H., Ji, Y., Holderrieth, P., Wu, Y., Liu, R., Cannoodt, R., Tang, W., Waldrant, K., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.24.707614
Dihydroergotamine alleviates circadian rhythm disorders through predicted interaction with to CRY1
Cai, L., Sun, Y. W., Wang, S., You, Q., Xie, W., Zhang, J., & 李伯安 (2026) · Molecular Diversity · doi:10.1007/s11030-026-11652-w
Evolution of peptide-mediated siRNA delivery – from early design to next generation platform
Chow, M. Y. T., & Lam, J. K. W. (2026) · Nanomedicine · doi:10.1080/17435889.2026.2711703
CABA-Bind: Confounder-aligned backdoor adjustment for debiased RNA-ligand binding prediction
Guo, Q., Liu, X., Zhang, Z., Zhou, M., Ma, T., Liu, Y., Yuan, J., Liu, T., Li, Y., & Guo, Q. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.109293
Papadourakis, M., Amaxopoulou, E. E., & Matsoukas, M. (2026) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2026.2712551
You, Z., Qin, Y., Gao, Y., Mu, Z., Liu, F., & Ge, S. S. (2026) · Molecules · doi:10.3390/molecules31162808
Carr, A. R., & Reuel, N. F. (2026) · doi:10.1021/bk-2026-1529.ch002
ActiveFusion: Fused Representations Improve Active Learning for Molecular Property Prediction
Evbarunegbe, N. I., Wa, S., Taylor, L., & Green, A. G. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01708
Korutla, R., Garg, T., Pol, S. S., Shivandi, V., Gnanam, S., Padari, K. S., & Amal, S. (2026) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2026.100183 · Cited by 1 · see citing works
Uncertainty-aware deep learning for drug-target interaction prediction
You, R., Le, N. Q. K., & Chua, M. C. H. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.113371
- Authors
- van Tilborg, D., Brinkmann, H., Criscuolo, E., Rossen, L., Özçelik, R., & Grisoni, F.
- Affiliations
- Eindhoven University of Technology (NL)
- University Medical Center Utrecht (NL)
- Corresponding author
- Grisoni, Francesca
- Venue
- Current Opinion in Structural Biology
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 86
- Pages
- 102818
- ISSN
- 0959-440X (ISSN-L)
- OpenAlex
- W4395481566
- Cited by
- 61
- References
- 73
- Citations by year
- 2026: 18 · 2025: 28 · 2024: 15
- FWCI
- 1.72
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Current Opinion in Structural Biology (publishedVersion, cc-by)
- ChemRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- TU/e Research Portal (publishedVersion, cc-by)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Funders
- European Commission (101077879)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Keywords
- deep learning, low-data learning, drug discovery, small data regime, de novo drug design, protein structure prediction, synthesis planning, data-efficient machine learning
- MeSH terms
- Deep Learning, Humans, Proteins, Drug Discovery
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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.
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