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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.
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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.
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