The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Accelerating high-throughput virtual screening through molecular pool-based active learning
Open accessAbstract
Bayesian optimization can accelerate structure-based virtual screening campaigns by minimizing the total number of simulations performed while still identifying the vast majority of computational hits.
Overview
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
Verification
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.
Citations
Cites
Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
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
ZINC 15 – Ligand Discovery for Everyone
Sterling, T., & Irwin, J. J. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00559 · Cited by 3,224
OpenMM 7: Rapid development of high performance algorithms for molecular dynamics
Eastman, P., Swails, J., Chodera, J. D., McGibbon, R. T., Zhao, Y., Beauchamp, K. A., Wang, L.-P., Simmonett, A. C., Harrigan, M. P., Stern, C. D., et al. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005659 · Cited by 2,748
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
Self-citationUltra-large library docking for discovering new chemotypes
Lyu, J., Wang, S., Balius, T. E., Singh, I., Levit, A., Moroz, Y. S., O’Meara, M. J., Che, T., Algaa, E., Tolmachova, K., et al. (2019) · Nature · doi:10.1038/s41586-019-0917-9 · Cited by 998
Role of computer-aided drug design in modern drug discovery
Macalino, S. J. Y., Gosu, V., Hong, S., & Choi, S. (2015) · Archives of Pharmacal Research · doi:10.1007/s12272-015-0640-5 · Cited by 762
Janet, J. P., Ramesh, S., Duan, C., & Kulik, H. J. (2020) · ACS Central Science · doi:10.1021/acscentsci.0c00026 · Cited by 200
Research works printed but not linked: 53
- W. Yu and A. D.MacKerell , in Antibiotics: Methods and Protocols , ed. P. Sass , Methods in Molecular Biology , Springer , New York, NY , 2017 , pp. 85–106
- Li, 2019, Interdiscip. Sci.: Comput. Life Sci., 11, 320, 10.1007/s12539-019-00327-w · DOI · Paper page
- Irwin, 2016, J. Med. Chem., 59, 4103, 10.1021/acs.jmedchem.5b02008 · DOI · Paper page
- REAL Database – Enamine , https://enamine.net/library-synthesis/real-compounds/real-database , accessed 09/15/2020
- Patel, 2020, Sci. Data, 7
- Nicolaou, 2016, J. Chem. Inf. Model., 56, 1253, 10.1021/acs.jcim.6b00173 · DOI
- Hu, 2012, ACS Comb. Sci., 14, 579, 10.1021/co300096q · doi:10.1021/co300096q
- Clark, 2020, J. Chem. Inf. Model., 60, 4120, 10.1021/acs.jcim.0c00101 · DOI
- Gorgulla, 2020, Nature, 580, 663, 10.1038/s41586-020-2117-z · DOI · Paper page
- Acharya, 2020, J. Chem. Inf. Model., 5832, 10.1021/acs.jcim.0c01010 · DOI · Paper page
- M. McGann , OpenEye Scientific , GigaDocking™ - Structure Based Virtual Screening of Over 1 Billion Molecules Webinar , 2019 , https://www.eyesopen.com/webinars/giga-docking-structure-based-virtual-screening , accessed 09/01/2020
- P. I. Frazier , A Tutorial on Bayesian Optimization, arXiv:1807.02811 [cs, math, stat] , 2018 · arXiv
- Balachandran, 2016, Sci. Rep., 6
- Gubaev, 2019, Comput. Mater. Sci., 156, 148, 10.1016/j.commatsci.2018.09.031 · DOI · Paper page
- Xue, 2016, Nat. Commun., 7
- Montoya, 2020, Chem. Sci., 11, 8517, 10.1039/D0SC01101K · DOI
- Bilsland, 2013, Open Biol., 3, 120158, 10.1098/rsob.120158 · DOI
- Czechtizky, 2013, ACS Med. Chem. Lett., 4, 768, 10.1021/ml400171b · DOI
- Williams, 2015, J. R. Soc., Interface, 12, 20141289, 10.1098/rsif.2014.1289 · DOI
- Ghanakota, 2020, ChemRxiv
- Konze, 2019, J. Chem. Inf. Model., 59, 3782, 10.1021/acs.jcim.9b00367 · DOI
- Svensson, 2017, J. Chem. Inf. Model., 57, 439, 10.1021/acs.jcim.6b00532 · DOI
- Ahmed, 2018, J. Cheminf., 10
- Gentile, 2020, ACS Cent. Sci., 6, 939, 10.1021/acscentsci.0c00229 · DOI · Paper page
- E. O. Pyzer-Knapp , Using Bayesian Optimization to Accelerate Virtual Screening for the Discovery of Therapeutics Appropriate for Repurposing for COVID-19, arXiv:2005.07121 [cs, q-bio], 2020 · arXiv
- J. M. Hernández-Lobato , J.Requeima , E. O.Pyzer-Knapp and A.Aspuru-Guzik , Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space , arXiv:1706.01825 [stat], 2017 · arXiv
- M. Gibbs and D. J. C.MacKay , Efficient Implementation of Gaussian Processes , 1997
- Yang, 2021, ChemRxiv
- Kalliokoski, 2021, ChemRxiv
- Martin, 2021, ChemRxiv
- Naik, 2015, J. Med. Chem., 58, 753, 10.1021/jm5012947 · DOI
- L. McInnes , J.Healy and J.Melville , UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , arXiv:1802.03426 [cs, stat], 2020 · arXiv
- Hachmann, 2011, J. Phys. Chem. Lett., 2, 2241, 10.1021/jz200866s · DOI · Paper page
- Desautels, 2014, J. Mach. Learn. Res., 15, 4053
- Tsymbalov, 2019, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 3599
- Hirschfeld, 2020, J. Chem. Inf. Model., 60, 3770, 10.1021/acs.jcim.0c00502 · DOI · Paper page
- J. T. Ash and R. P.Adams , On Warm-Starting Neural Network Training, arXiv:1910.08475 [cs, stat] , 2020 · arXiv
- J. Azimi , A.Jalali and X.Fern , Dynamic Batch Bayesian Optimization , arXiv:1110.3347 [cs], 2011 · arXiv
- J. Azimi , A.Jalali and X.Fern , Hybrid Batch Bayesian Optimization , arXiv:1202.5597 [cs], 2012 · arXiv
- Z. Wang , C.Gehring , P.Kohli and S.Jegelka , Batched Large-scale Bayesian Optimization in High-dimensional Spaces , arXiv:1706.01445 [stat], 2018 · arXiv
- Schuffenhauer, 2020, J. Med. Chem., 63, 14425, 10.1021/acs.jmedchem.0c01332 · DOI
- Shahriari, 2016, Proc. IEEE, 104, 148, 10.1109/JPROC.2015.2494218 · DOI · Paper page
- Svetnik, 2003, J. Chem. Inf. Comput. Sci., 43, 1947, 10.1021/ci034160g · DOI · Paper page
- Pedregosa, 2011, J. Mach. Learn. Res., 12, 2825
- Carhart, 1985, J. Chem. Inf. Comput. Sci., 25, 64, 10.1021/ci00046a002 · DOI · Paper page
- D. Bajusz , A.Rácz and K.Héberger , Chemical Data Formats, Fingerprints, and Other Molecular Descriptions for Database Analysis and Searching , Comprehensive Medicinal Chemistry III , 2017 , pp. 329–378
- Paszke, 2019, Advances in Neural Information processing systems, 8026
- A. Vaswani , N.Shazeer , N.Parmar , J.Uszkoreit , L.Jones , A. N.Gomez , L.Kaiser and I.Polosukhin , Attention Is All You Need , arXiv:1706.03762 [cs], 2017 · arXiv
- D. A. Nix and A. S.Weigend , Estimating the mean and variance of the target probability distribution . Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN’94) , 1994 , vol. 1 , pp 55–60
- Diversity Libraries – Enamine , https://enamine.net/hit-finding/diversity-libraries , accessed 04/01/2020
- HTS Collection – Enamine , https://enamine.net/hit-finding/compound-collections/screening-collection/hts-collection , accessed 04/01/2020
- RDKit , http://rdkit.org/ , accessed 10/20/2020
- O’Boyle, 2011, J. Cheminf., 3
Cited by
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
“The results can be fed back into the AI models using active learning 136 and Bayesian optimization 137 (Box 1), allowing the algorithms to refine their predictions and focus on the most promising candidates.”
MethodSelf-citationSelf-Driving Laboratories for Chemistry and Materials Science
Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707
Gentile, F., Yaacoub, J. C., Gleave, J., Fernandez, M., Ton, A.-T., Ban, F., Stern, A., & Cherkasov, A. (2022) · Nature Protocols · doi:10.1038/s41596-021-00659-2 · Cited by 397
Synthon-based ligand discovery in virtual libraries of over 11 billion compounds
Sadybekov, A. A., Sadybekov, A. V., Liu, Y., Iliopoulos-Tsoutsouvas, C., Huang, X.-P., Pickett, J., Houser, B., Patel, N., Tran, N. K., Tong, F., et al. (2022) · Nature · doi:10.1038/s41586-021-04220-9 · Cited by 417
“Recently, an iteration of docking and machine learning steps 9 , or step-wise filtering of the whole enumerated library using docking algorithms of increasing accuracy 8 , were suggested to tackle ultra-large libraries of 138 million and 1.4 billion compounds, respectively.”
MethodInfluentialAn artificial intelligence accelerated virtual screening platform for drug discovery
(2024) · doi:10.1038/s41467-024-52061-7 · Cited by 259
Metadata
- Authors
- Graff, D. E., Shakhnovich, E. I., & Coley, C. W.
- Venue
- Chemical Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2021
- Volume
- 12
- Issue
- 22
- Pages
- 7866-7881
- ISSN
- 2041-6520 (ISSN-L)
- 2041-6539
- OpenAlex
- W3112474878
- Cited by
- 302
- References
- 9
- FWCI
- 24.12
- Type
- journal article
- Language
- en
- Open access
- diamond
- Funders
- National Institute of General Medical Sciences (RO1 068670)
- Massachusetts Institute of Technology
- International Business Machines Corporation
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Accelerating high-throughput virtual screening through molecular pool-based active learning. https://app.cochat.ai/papers/10.1039/d0sc06805e
- The paper
- Graff, D. E., Shakhnovich, E. I., & Coley, C. W. (2021). Accelerating high-throughput virtual screening through molecular pool-based active learning. Chemical Science. https://doi.org/10.1039/d0sc06805e
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
