Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction
Open accessPublished version of a preprint
Abstract
Multitask deep neural networks learn to predict ligand–target binding by example, yet public pharmacological data sets are sparse, imbalanced, and approximate. We constructed two hold-out benchmarks to approximate temporal and drug-screening test scenarios, whose characteristics differ from a random split of conventional training data sets. We developed a pharmacological data set augmentation procedure, Stochastic Negative Addition (SNA), which randomly assigns untested molecule–target pairs as transient negative examples during training. Under the SNA procedure, drug-screening benchmark performance increases from R2 = 0.1926 ± 0.0186 to 0.4269 ± 0.0272 (122%). This gain was accompanied by a modest decrease in the temporal benchmark (13%). SNA increases in drug-screening performance were consistent for classification and regression tasks and outperformed y-randomized controls. Our results highlight where data and feature uncertainty may be problematic and how leveraging uncertainty into training improves predictions of drug–target relationships.
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.
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
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249 · see citing works
(2014) · Cited by 33,419 · see citing works
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865 · see citing works
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359 · see citing works
Extended-Connectivity Fingerprints
Rogers, D. O. W., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,597 · see citing works
NWChem: A comprehensive and scalable open-source solution for large scale molecular simulations
Valiev, M., Bylaska, E. J., Govind, N., Kowalski, K., Straatsma, T. P., van Dam, H. J. J., Wang, D., Nieplocha, J., Aprà, E., Windus, T. L., et al. (2010) · Computer Physics Communications · doi:10.1016/j.cpc.2010.04.018 · Cited by 5,794 · see citing works
The class imbalance problem: A systematic study1
Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295 · see citing works
A systematic study of the class imbalance problem in convolutional neural networks
Buda, M., Maki, A., & Mazurowski, M. A. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.07.011 · Cited by 3,088 · see citing works
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 · see citing works
Theano: A Python framework for fast computation of mathematical expressionsPreprint
Al-Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., Ballas, N., Bastien, F., Bayer, J., Belikov, A., Belopolsky, A., et al. (2016) · arXiv.org · doi:10.48550/arxiv.1605.02688 · Cited by 2,438 · see citing works
Relating protein pharmacology by ligand chemistry
Keiser, M. J., Roth, B. L., Armbruster, B. N., Ernsberger, P., Irwin, J. J., & Shoichet, B. K. (2007) · Nature Biotechnology · doi:10.1038/nbt1284 · Cited by 2,402 · see citing works
Self-citationBest Practices for QSAR Model Development, Validation, and Exploitation
Tropsha, A. (2010) · Molecular Informatics · doi:10.1002/minf.201000061 · Cited by 2,042 · see citing works
Analyzing Learned Molecular Representations for Property Prediction
Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., et al. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00237 · Cited by 1,923 · see citing works
Predicting new molecular targets for known drugs
Keiser, M. J., Setola, V., Irwin, J. J., Laggner, C., Abbas, A. I., Hufeisen, S. J., Jensen, N. H., Kuijer, M. B., Matos, R. C., Tran, T. B., et al. (2009) · Nature · doi:10.1038/nature08506 · Cited by 1,667 · see citing works
Self-citationMolecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., & Riley, P. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9938-8 · Cited by 1,631 · see citing works
Static Analysis of Shape in TensorFlow ProgramsPreprint
The Theano Development Team, Al‐Rfou, R., Alain, G., et al. (2016) · arXiv (Cornell University) · doi:10.4230/lipics.ecoop.2020.15 · Cited by 1,629 · see citing works
The ChEMBL bioactivity database: an update
Bento, A. P., Gaulton, A., Hersey, A., Bellis, L. J., Chambers, J. K., Davies, M., Krüger, F. A., Light, Y., Mak, L., McGlinchey, S., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1031 · Cited by 1,487 · see citing works
Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships
Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., & Svetnik, V. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500747n · Cited by 1,159 · see citing works
Waskom, M., Botvinnik, O. B., O'Kane, D., Hobson, P., Ostblom, J., Lukauskas, S., Gemperline, D. C., Augspurger, T., Halchenko, Y., Cole, J. B., et al. (2014) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.592845 · Cited by 1,119 · see citing works
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,045 · see citing works
Learning Deep Representation for Imbalanced Classification
Huang, C., Li, Y., Loy, C. C., & Tang, X. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.580 · Cited by 1,027 · see citing works
y-Randomization and Its Variants in QSPR/QSAR
Rücker, C., Rücker, G., & Meringer, M. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci700157b · Cited by 916 · see citing works
Large-scale prediction and testing of drug activity on side-effect targets
Lounkine, E., Keiser, M. J., Whitebread, S., Mikhailov, D., Hamon, J., Jenkins, J. L., Lavan, P., Weber, E., Doak, A. K., Côté, S., et al. (2012) · Nature · doi:10.1038/nature11159 · Cited by 880 · see citing works
Self-citationLarge-scale comparison of machine learning methods for drug target prediction on ChEMBL
Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J. K., Ceulemans, H., Clevert, D.-A., & Hochreiter, S. (2018) · Chemical Science · doi:10.1039/c8sc00148k · Cited by 584 · see citing works
PotentialNet for Molecular Property Prediction
Feinberg, E. N., Sur, D., Wu, Z., Husic, B. E., Mai, H., Li, Y., Sun, S., Yang, J., Ramsundar, B., & Pande, V. S. (2018) · ACS Central Science · doi:10.1021/acscentsci.8b00507 · Cited by 485 · see citing works
Similarity-based machine learning methods for predicting drug–target interactions: a brief review
Ding, H., Takigawa, I., Mamitsuka, H., & Zhu, S. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbt056 · Cited by 427 · see citing works
mwaskom/seaborn: v0.8.1 (September 2017)
Waskom, M., Botvinnik, O. B., O'Kane, D., et al. (2017) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.883859 · Cited by 423 · see citing works
Massively Multitask Networks for Drug DiscoveryPreprint
Ramsundar, B., Kearnes, S. M., Riley, P., Webster, D. R., Konerding, D. E., & Pande, V. S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.02072 · Cited by 383 · see citing works
Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.
Sheridan, R. P. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400084k · Cited by 376 · see citing works
Dieleman, S., Schlüter, J., Raffel, C., Olson, E. M., Sønderby, S. K., Nouri, D., Maturana, D., Thoma, M., Battenberg, E., Kelly, J., et al. (2015) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.27878 · Cited by 324 · see citing works
Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization
Wallach, I., & Heifets, A. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00403 · Cited by 287 · see citing works
Ganter, B., Tugendreich, S., Pearson, C. I., Ayanoĝlu, E., Baumhueter, S., Bostian, K. A., Brady, L., Browne, L. J., Calvin, J. T., Day, G.-J., et al. (2005) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2005.03.022 · Cited by 286 · see citing works
Demystifying Multitask Deep Neural Networks for Quantitative Structure–Activity Relationships
Xu, Y., Ma, J., Liaw, A., Sheridan, R. P., & Svetnik, V. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00087 · Cited by 238 · see citing works
Withnall, M., Lindelöf, E., Engkvist, O., & Chen, H. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-019-0407-y · Cited by 232 · see citing works
Challenges and advances in computational chemistry and physics
Leszczyński, J. (2006) · Springer eBooks · Cited by 230 · see citing works
Target prediction utilising negative bioactivity data covering large chemical space
Mervin, L., Afzal, A. M., Drakakis, G., Lewis, R. P., Engkvist, O., & Bender, A. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0098-y · Cited by 134 · see citing works
A Simple Representation of Three-Dimensional Molecular Structure
Axen, S. D., Huang, X., Cáceres, E. L., Gendelev, L., Roth, B. L., & Keiser, M. J. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.7b00696 · Cited by 133 · see citing works
Self-citationDeep Learning-Based Imbalanced Data Classification for Drug Discovery
Korkmaz, S. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b01162 · Cited by 132 · see citing works
BioAssay Ontology (BAO): a semantic description of bioassays and high-throughput screening results
Visser, U., Abeyruwan, S. W., Vempati, U. D., Smith, R. P., Lemmon, V., & Schürer, S. C. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-257 · Cited by 128 · see citing works
QSAR Modeling of Imbalanced High-Throughput Screening Data in PubChem
Zakharov, A. V., Peach, M. L., Sitzmann, M., & Nicklaus, M. C. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci400737s · Cited by 127 · see citing works
Advances and Challenges in Computational Target Prediction
Sydow, D., Burggraaff, L., Szengel, A., van Vlijmen, H., IJzerman, A. P., van Westen, G. J. P., & Volkamer, A. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00832 · Cited by 127 · see citing works
Deep Learning Approaches in Predicting ADMET Properties
Cáceres, E. L., Tudor, M., & Cheng, A. C. (2020) · Future Medicinal Chemistry · doi:10.4155/fmc-2020-0259 · Cited by 94 · 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
Imputation of Assay Bioactivity Data Using Deep Learning
Whitehead, T. M., Irwin, B., Hunt, P. A., Segall, M., & Conduit, G. J. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00768 · Cited by 71 · see citing works
Adversarial Controls for Scientific Machine Learning
Chuang, K. V., & Keiser, M. J. (2018) · ACS Chemical Biology · doi:10.1021/acschembio.8b00881 · Cited by 67 · see citing works
Self-citationEffect of missing data on multitask prediction methods
de León, A. D. L. V., Chen, B., & Gillet, V. J. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0281-z · Cited by 38 · see citing works
Heikamp, K., & Bajorath, J. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci4002712 · Cited by 36 · see citing works
Lipiński, P. F. J., & Szurmak, P. (2017) · Chemical Papers · doi:10.1007/s11696-017-0215-7 · Cited by 32 · see citing works
An Overview of National Toxicology Program’s Toxicogenomic Applications: DrugMatrix and ToxFX
Svoboda, D. L., Saddler, T. O., & Auerbach, S. S. (2019) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-030-16443-0_8 · Cited by 29 · see citing works
Sanders, J. M., Beshore, D. C., Culberson, J. C., Fells, J. I., Imbriglio, J. E., Günaydin, H., Haidle, A. M., Labroli, M. A., Mattioni, B. E., Sciammetta, N., et al. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b01577 · Cited by 26 · see citing works
Accurate and efficient target prediction using a potency-sensitive influence-relevance voter
Lusci, A., Fooshee, D. R., Browning, M. R., Swamidass, S. J., & Baldi, P. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0110-6 · Cited by 22 · see citing works
The Effect of Debiasing Protein–Ligand Binding Data on Generalization
Sundar, V., & Colwell, L. J. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00415 · Cited by 20 · see citing works
Advances in Computational Toxicology
(2019) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-030-16443-0 · Cited by 17 · see citing works
(2008) · Nature Reviews Drug Discovery · doi:10.1038/nrd2649 · Cited by 16 · see citing works
Kurczab, R., & Bojarski, A. J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0175410 · Cited by 12 · see citing works
Lee, Y. O., & Kim, Y. J. (2020) · Molecular Informatics · doi:10.1002/minf.201900131 · Cited by 9 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- [13](13) Johnson, M. A.; Maggiora, G. M.; American Chemical Society. Meeting. Concepts and Applications of Molecular Similarity ; Wiley-Interscience, 1990. -
- [46](46) Oliphant, T. Guide to NumPy: 2nd Edition ; CreateSpace, 2015. -
- [50](50) Maas, A. L.; Hannun, A. Y.; Ng, A. Y. Rectifier Nonlinearities Improve Neural Network Acoustic Models. In Proc. icml ; 2013; Vol. 30, p 3. -
- [51](51) Nesterov, Y. A Method of Solving a Convex Programming Problem with Convergence Rate O(1/k^ 2). In Soviet Math. Dokl ; Vol. 27. -
Cited by
Sorted by citations
Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358 · see citing works
Unleashing the power of generative AI in drug discovery
Gangwal, A., & Lavecchia, A. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.103992 · Cited by 195 · see citing works
Advances and applications of machine learning and deep learning in environmental ecology and health
Cui, S., Gao, Y., Huang, Y., Shen, L., Zhao, Q., Pan, Y., & Zhuang, S. (2023) · Environmental Pollution · doi:10.1016/j.envpol.2023.122358 · Cited by 124 · see citing works
Machine Learning for the Discovery, Design, and Engineering of Materials.
Duan, C., Nandy, A., & Kulik, H. J. (2022) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-092320-120230 · Cited by 69 · see citing works
Kalemati, M., Emani, M. Z., & Koohi, S. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011036 · Cited by 61 · see citing works
Prioritizing Virtual Screening with Interpretable Interaction Fingerprints
Fassio, A. V., Shub, L., Ponzoni, L., McKinley, J. L., O’Meara, M. J., Ferreira, R. S., Keiser, M. J., & de Melo-Minardi, R. C. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00695 · Cited by 57 · see citing works
Self-citationPutting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
Duan, C., Liu, F., Nandy, A., & Kulik, H. J. (2021) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.1c00631 · Cited by 55 · see citing works
Yes SIR! On the structure–inactivity relationships in drug discovery
López‐López, E., Gortari, E. F., & Medina‐Franco, J. L. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.05.005 · Cited by 52 · see citing works
Machine learning for small molecule drug discovery in academia and industry
Volkamer, A., Riniker, S., Nittinger, E., Lanini, J., Grisoni, F., Evertsson, E., Rodríguez-Pérez, R., & Schneider, N. (2023) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2022.100056 · Cited by 48 · see citing works
Ricardo, F., Puentes, P. R., Reyes, L. H., Cruz, J. C., Álvarez, Ó., & Pradilla, D. (2022) · Chemical Engineering Science · doi:10.1016/j.ces.2022.118208 · Cited by 28 · see citing works
Wellnitz, J., Jain, S., Hochuli, J. E., Maxfield, T., Muratov, E. N., Tropsha, A., & Zakharov, A. V. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-00948-y · Cited by 24 · see citing works
BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening
Brocidiacono, M., Francoeur, P. G., Aggarwal, R., Popov, K. I., Koes, D. R., & Tropsha, A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01211 · Cited by 21 · see citing works
Marbán‐González, A., Ramírez-Cid, V., Cristóbal-Ramírez, A., & Medina‐Franco, J. L. (2025) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2025.2558161 · Cited by 16 · see citing works
Comparing classification models—a practical tutorial
Walters, W. P. (2021) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-021-00417-2 · Cited by 12 · see citing works
Machine Learning Prediction of High-Yield Cobalt- and Nickel-Catalyzed Borylations
Pereira, A., & Trofymchuk, O. S. (2023) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.3c01704 · Cited by 11 · see citing works
Advancing Bioactivity Prediction Through Molecular Docking and Self-Attention
Yin, Y., Lam, H. Y. I., Mu, Y., Li, H., & Kong, A. W. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3448455 · Cited by 10 · see citing works
Progress of machine learning in the application of small molecule druggability prediction
Li, J., Zhang, J., Guo, R. F., Dai, J., Niu, Z., Wang, Y., Wang, T., Jiang, X., & Hu, W. (2025) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2025.117269 · Cited by 9 · see citing works
Huỳnh, D. A., & Srisongkram, T. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01102-4 · Cited by 8 · see citing works
Martin, L. J., & Bowen, M. T. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00469 · Cited by 9 · see citing works
Artificial intelligence in computational materials science
Kulik, H. J., & Tiwary, P. (2022) · MRS Bulletin · doi:10.1557/s43577-022-00431-1 · Cited by 8 · see citing works
Machine learning anomaly detection of automated HPLC experiments in the cloud laboratory
Gusev, F., Kline, B. C., Quinn, R., Xu, A., Smith, B., Frezza, B., & Isayev, O. (2025) · Digital Discovery · doi:10.1039/d5dd00253b · Cited by 7 · see citing works
Biomedical data analyses facilitated by open cheminformatics workflows
Nittinger, E., Clark, A. M., Gaulton, A., & Zdrazil, B. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00718-8 · Cited by 7 · see citing works
Efficient decoy selection to improve virtual screening using machine learning models
Victoria-Muñoz, F., Menke, J., Sánchez‐Cruz, N., & Koch, O. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01107-z · Cited by 5 · see citing works
Stroobants, A., Mervin, L., Engkvist, O., & Robb, G. R. (2023) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2023.100079 · Cited by 4 · see citing works
Umucu, Y., Denız, V., & GÜRSOY, Y. H. (2025) · Bilimsel Madencilik Dergisi · doi:10.30797/madencilik.1456066 · Cited by 2 · see citing works
Dey, S., Wallqvist, A., & AbdulHameed, M. D. M. (2025) · Scientific Reports · doi:10.1038/s41598-025-00972-w · Cited by 1 · see citing works
Garcia-Rubio, A. M., Heck, L., Schweitzer, K. M., Bateman, R. M., Alaeddini, A., & Castillo-Villar, K. K. (2026) · IEEE Access · doi:10.1109/access.2026.3652106 · Cited by 2 · see citing works
Focusing on Data to Improve Machine Learning-Guided Antibiotic Discovery
Ta, W., Naiberg, R., Yoon, C. H., Pao, S. Y., Tindall, J., Liu, G., & Stokes, J. M. (2026) · Microbial Drug Resistance · doi:10.1177/10766294261431279 · Cited by 1 · see citing works
When Trees Guide Molecules: Multiobjective Search in de Novo Drug Design
Druchok, M., & Rovenchak, A. A. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00407 · Cited by 1 · see citing works
PI3K-Seeker: A Machine Learning-Powered Web Tool to Discover PI3K Inhibitors
de Sousa, F. J. F., Antunes, D. A., & Zanatta, G. (2025) · ACS Omega · doi:10.1021/acsomega.5c07315
Weber, L., Janssen, S., Richter, J., & Fischer, E. (2025) · International Journal of Pharmaceutical and Phytopharmacological Research · doi:10.51847/vn3miodt9m
Zayed, O., Ibrahim, L., & Mansour, A. (2025) · International Journal of Pharmaceutical and Phytopharmacological Research · doi:10.51847/kuws08zaqx
Mendoza, R., Cruz, I., & Lopez, C. (2025) · International Journal of Pharmaceutical and Phytopharmacological Research · doi:10.51847/h9vptvjuoa
- Authors
- Cáceres, E. L., Mew, N. C., & Keiser, M. J.
- Affiliations
- University of California, San Francisco , , 675 Nelson Rising Ln NS 416A , , ,
- University of California, San Francisco (US)
- Corresponding author
- Keiser, Michael J.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-11-27
- Volume
- 60
- Issue
- 12
- Pages
- 5957-5970
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W3109353507
- Cited by
- 33
- References
- 54
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 2 · 2023: 7 · 2022: 5 · 2021: 3 · 2020: 1
- FWCI
- 1.99
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- Howard Hughes Medical Institute
- Division of Graduate Education (1650113)
- Silicon Valley Community Foundation (2018-191905)
- Sandler Foundation
- 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
- data augmentation, class imbalance
- MeSH terms
- Machine Learning, Neural Networks, Computer
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
