The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
CACHE (Critical Assessment of Computational Hit-finding Experiments): A public–private partnership benchmarking initiative to enable the development of computational methods for hit-finding
Open accessPublished version of a preprint
Abstract
Computational approaches in drug discovery and development hold great promise, with artificial intelligence methods undergoing widespread contemporary use, but the experimental validation of these new approaches is frequently inadequate. We are initiating Critical Assessment of Computational Hit-finding Experiments (CACHE) as a public benchmarking project that aims to accelerate the development of small molecule hit-finding algorithms by competitive assessment. Compounds will be identified by participants using a wide range of computational methods for dozens of protein targets selected for different types of prediction scenarios, as well as for their potential biological or pharmaceutical relevance. Community-generated predictions will be tested centrally and rigorously in an experimental hub(s), and all data, including the chemical structures of experimentally tested compounds, will be made publicly available without restrictions. The ability of a range of computational approaches to find novel compounds will be evaluated, compared, and published. The overarching goal of CACHE is to accelerate the development of computational chemistry methods by providing rapid and unbiased feedback to those developing methods, with an ancillary and valuable benefit of identifying new compound-protein binding pairs for biologically interesting targets. The initiative builds on the power of crowd sourcing and expands the open science paradigm for drug discovery.
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
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
Accurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,549
Escape from Flatland: Increasing Saturation as an Approach to Improving Clinical Success
Lovering, F., Bikker, J. A., & Humblet, C. C. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm901241e · Cited by 4,614
Protein complex prediction with AlphaFold-Multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al. (2021) · doi:10.1101/2021.10.04.463034 · Cited by 3,808
Ertl, P., Rohde, B., & Selzer, P. M. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm000942e · Cited by 3,149
ZINC20─A Free Ultralarge-Scale Chemical Database for Ligand Discovery
Irwin, J. J., Tang, K. G., Young, J., Dandarchuluun, C., Wong, B. R., Khurelbaatar, M., Moroz, Y. S., Mayfield, J. W., & Sayle, R. A. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00675 · Cited by 1,122
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
Self-citationAn open-source drug discovery platform enables ultra-large virtual screens
Gorgulla, C., Boeszoermenyi, A., Wang, Z., Fischer, P. D., Coote, P. W., Das, K. M., Malets, Y. S., Radchenko, D. S., Moroz, Y. S., Scott, D. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2117-z · Cited by 677
Seven Year Itch: Pan-Assay Interference Compounds (PAINS) in 2017─Utility and Limitations
Baell, J. B., & Nissink, J. W. M. (2017) · ACS Chemical Biology · doi:10.1021/acschembio.7b00903 · Cited by 669
Central nervous system multiparameter optimization desirability: Application in drug discovery
Wager, T. T., Hou, X., Verhoest, P. R., & Villalobos, A. (2016) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.6b00029 · Cited by 653
A Specific Mechanism of Nonspecific Inhibition
McGovern, S. L., Helfand, B. T., Feng, B. Y., & Shoichet, B. K. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm030266r · Cited by 600
A large‐scale experiment to assess protein structure prediction methods
Moult, J., Pedersen, J. T., Judson, R., & Fidelis, K. (1995) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.340230303 · Cited by 555
Self-citationComputed structures of core eukaryotic protein complexes
Humphreys, I. R., Pei, J., Baek, M., Krishnakumar, A., Anishchenko, I., Ovchinnikov, S., Zhang, J., Ness, T. J., Banjade, S., Bagde, S. R., et al. (2021) · Science · doi:10.1126/science.abm4805 · Cited by 542
D3R grand challenge 2015: evaluation of protein-ligand pose and affinity predictions.
Gathiaka, S., Liu, S., Chiu, M., Yang, H., Stuckey, J. A., Kang, Y. N., Delproposto, J., Kubish, G., Dunbar, J. B., Carlson, H. A., et al. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9946-8 · Cited by 207
Self-citationGaieb, Z., Liu, S., Gathiaka, S., Chiu, M., Yang, H., Shao, C., Feher, V. A., Walters, W. P., Kuhn, B., Rudolph, M. G., et al. (2018) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-017-0088-4 · Cited by 193
Self-citationOverview of the SAMPL5 host–guest challenge: Are we doing better?
Yin, J., Henriksen, N. M., Slochower, D. R., Shirts, M. R., Chiu, M. W., Mobley, D. L., & Gilson, M. K. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9974-4 · Cited by 175
Self-citationApplication of Belief Theory to Similarity Data Fusion for Use in Analog Searching and Lead Hopping
Muchmore, S. W., Debe, D. A., Metz, J. T., Brown, S. P., Martin, Y. C., & Hajduk, P. J. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci7004498 · Cited by 163
Parks, C. D., Gaieb, Z., Chiu, M. W., Yang, H., Shao, C., Walters, W. P., Jansen, J. M., McGaughey, G. B., Lewis, R. A., Bembenek, S. D., et al. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00289-y · Cited by 140
Self-citationD3R Grand Challenge 3: blind prediction of protein–ligand poses and affinity rankings
Gaieb, Z., Parks, C. D., Chiu, M. W., Yang, H., Shao, C., Walters, W. P., Lambert, M. H., Nevins, N., Bembenek, S. D., Ameriks, M. K., et al. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-018-0180-4 · Cited by 138
Self-citationOpen access chemical and clinical probes to support drug discovery
Edwards, A. M., Bountra, C., Kerr, R. S., & Willson, T. M. (2009) · Nature Chemical Biology · doi:10.1038/nchembio0709-436 · Cited by 130
Self-citationIn Silico ADMET Traffic Lights as a Tool for the Prioritization of HTS Hits
Lobell, M., Hendrix, M., Hinzen, B., Keldenich, J., Meier, H., Schmeck, C., Schohe-Loop, R., Wunberg, T., & Hillisch, A. (2006) · ChemMedChem · doi:10.1002/cmdc.200600168 · Cited by 110
Self-citationBlind prediction of cyclohexane–water distribution coefficients from the SAMPL5 challenge
Bannan, C. C., Burley, K. H., Chiu, M. W., Shirts, M. R., Gilson, M. K., & Mobley, D. L. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9954-8 · Cited by 107
Self-citationBadapple: promiscuity patterns from noisy evidence
Yang, J. J., Ursu, O., Lipinski, C. A., Sklar, L. A., Oprea, T. I., & Bologa, C. G. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0137-3 · Cited by 103
Self-citationTarget 2035 – update on the quest for a probe for every protein
Müller, S., Ackloo, S. Z., Chawaf, A. A., Al‐Lazikani, B., Antolín, A. A., Baell, J. B., Beck, H., Beedie, S., Betz, U. A. K., Bezerra, G. A., et al. (2021) · RSC Medicinal Chemistry · doi:10.1039/d1md00228g · Cited by 85
Self-citationVirtual Screening in the Cloud: How Big Is Big Enough?
Grebner, C., Malmerberg, E., Shewmaker, A., Batista, J., Nicholls, A. O., & Sadowski, J. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00779 · Cited by 73
New Trends in Virtual Screening
Walters, W. P., & Wang, R. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01009 · Cited by 64
Cummins, D. J., & Bell, M. A. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b01338 · Cited by 50
A white-knuckle ride of open COVID drug discovery
von Delft, F., Calmiano, M., Chodera, J. D., Griffen, E., Lee, A. A., London, N., Matviuk, T., Perry, B., Robinson, M., & von Delft, A. (2021) · Nature · doi:10.1038/d41586-021-01571-1 · Cited by 39
Self-citationXiong, Z., Jeon, M., Allaway, R. J., Kang, J., Park, D., Lee, J., Jeon, H., Ko, M., Jiang, H., Zheng, M., et al. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009302 · Cited by 18
Teach–Discover–Treat (TDT): Collaborative computational drug discovery for neglected diseases
Jansen, J. M., Cornell, W. D., Tseng, Y. J., & Amaro, R. E. (2012) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2012.07.007 · Cited by 17
Self-citationComputational Chemistry And Drug Discovery: A Call To Action
Jansen, J. M., Amaro, R. E., Cornell, W. D., Tseng, Y. J., & Walters, W. P. (2012) · Future Medicinal Chemistry · doi:10.4155/fmc.12.137 · Cited by 5
Self-citation
Cited by
Sorted by citations
Computational approaches streamlining drug discovery
Sadybekov, A., & Katritch, V. (2023) · Nature · doi:10.1038/s41586-023-05905-z · Cited by 1,424
Machine learning in preclinical drug discovery
Catacutan, D. B., Alexander, J., Arnold, A., & Stokes, J. M. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01679-1 · Cited by 227
Protein–Ligand Docking in the Machine-Learning Era
Yang, C., Chen, E. A., & Zhang, Y. (2022) · Molecules · doi:10.3390/molecules27144568 · Cited by 229
TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction
Lu, W., Wu, Q., Zhang, J., Rao, J., Li, C., & Zheng, S. (2022) · neural information processing systems · doi:10.52202/068431-0525 · Cited by 181
Cao, D., Chen, M., Zhang, R., Wang, Z., Huang, M., Yu, J., Jiang, X., Fan, Z., Zhang, W., Zhou, H., et al. (2025) · Nature Methods · doi:10.1038/s41592-024-02516-y · Cited by 106
Metadata
- Authors
- Ackloo, S., Al-awar, R., Amaro, R. E., Arrowsmith, C. H., Azevedo, H., Batey, R. A., Bengio, Y., Betz, U. A. K., Bologa, C. G., Chodera, J. D., Cornell, W. D., Dunham, I., Ecker, G. F., Edfeldt, K., Edwards, A. M., Gilson, M. K., Gordijo, C. R., Hessler, G., Hillisch, A., Hogner, A., Irwin, J. J., Jansen, J. M., Kuhn, D., Leach, A. R., Lee, A. A., Lessel, U., Morgan, M. R., Moult, J., Muegge, I., Oprea, T. I., Perry, B. G., Riley, P., Rousseaux, S. A. L., Saikatendu, K. S., Santhakumar, V., Schapira, M., Scholten, C., Todd, M. H., Vedadi, M., Volkamer, A., & Willson, T. M.
- Affiliations
- University of Toronto (CA)
- University of California San Diego (US)
- Mila - Quebec Artificial Intelligence Institute (CA)
- Merck Healthcare KGaA, Darmstadt (Germany) (DE)
- University of New Mexico (US)
- Memorial Sloan Kettering Cancer Center (US)
- IBM (United States) (US)
- European Bioinformatics Institute (GB)
- University of Vienna (AT)
- Karolinska University Hospital (SE)
- Sanofi (Germany) (DE)
- Bayer (Germany) (DE)
- AstraZeneca (Sweden) (SE)
- University of California, San Francisco (US)
- University of Cambridge (GB)
- Boehringer Ingelheim (Germany) (DE)
- Institute for Bioscience and Biotechnology Research (US)
- Alkermes (United States) (US)
- Drugs for Neglected Diseases Initiative (CH)
- Relay Therapeutics (United States) (US)
- Takeda (United States) (US)
- UCL School of Pharmacy (GB)
- Charité – Universitätsmedizin Berlin (DE)
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Edwards, Aled M.
- Hillisch, Alexander
- ORCID
- Ackloo, Suzanne
- Amaro, Rommie E.
- Arrowsmith, Cheryl H.
- Batey, Robert A.
- Bengio, Yoshua
- Betz, Ulrich A. K.
- Bologa, Cristian G.
- Chodera, John D.
- Cornell, Wendy D.
- Dunham, Ian
- Ecker, Gerhard F.
- Edfeldt, Kristina
- Edwards, Aled M.
- Gilson, Michael K.
- Gordijo, Claudia R.
- Hessler, Gerhard
- Hillisch, Alexander
- Hogner, Anders
- Irwin, John J.
- Jansen, Johanna M.
- Kuhn, Daniel
- Leach, Andrew R.
- Lee, Alpha A.
- Lessel, Uta
- Morgan, Maxwell R.
- Moult, John
- Muegge, Ingo
- Oprea, Tudor I.
- Perry, Benjamin G.
- Riley, Patrick
- Rousseaux, Sophie A. L.
- Saikatendu, Kumar Singh
- Santhakumar, Vijayaratnam
- Schapira, Matthieu
- Todd, Matthew H.
- Vedadi, Masoud
- Volkamer, Andrea
- Willson, Timothy M.
- Venue
- Nature Reviews Chemistry
- Published
- 2022-02-15
- Volume
- 6
- Issue
- 4
- Pages
- 287-295
- ISSN
- 2397-3358 (ISSN-L)
- PMC
- PMC9246350
- OpenAlex
- W4283835519
- Cited by
- 112
- References
- 32
- Citations by year
- 2026: 35 · 2025: 27 · 2024: 29 · 2023: 13 · 2022: 8
- FWCI
- 1.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
- Locations
- Nature Reviews Chemistry (publishedVersion, public-domain)
- ChemRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- The Novartis Repository (Novartis) (submittedVersion)
- Funders
- Bristol-Myers Squibb (875510)
- Pfizer
- AstraZeneca
- Bayer (875510)
- McGill University
- Genome Canada (875510)
- Merck KGaA
- Diamond Light Source (1097737)
- Ontario Genomics
- European Federation of Pharmaceutical Industries and Associations (875510)
- Ontario Genomics Institute (875510)
- University of Cambridge
- European Commission (HORIZON2020, 875510)
- Karolinska Institutet
- Innovative Medicines Initiative (875510)
- National Institutes of Health (U24TR002278, GM133836, CA224370)
- Genentech (875510)
- Engineering and Physical Sciences Research Council
- National Cancer Institute (U24CA224370, P30CA008748)
- National Institute of General Medical Sciences (R01GM133836, R01GM124270, R01GM061300, GM133836, GM061300)
- National Center for Advancing Translational Sciences
- Common Fund
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- small-molecule binders, protein-ligand binding prediction, public-private partnerships, pharmacological probes, drug discovery
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
