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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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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
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Schiedel, M., Barbie, P., Pape, F., Pinto, M., Lopez, A. U., Méndez, M. M., Heßler, G., Merk, D., Gehringer, M., & Lamers, C. (2024) · ChemMedChem · doi:10.1002/cmdc.202400543
PocketDTA: A pocket-based multimodal deep learning model for drug–target affinity prediction
Xie, J., Zhong, S., Huang, D., & Shao, W. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108416 · Cited by 1
Large chemical spaces—Modeling and searching of large chemical spaces
Orsi, M., & Reymond, J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00045-5
Nivedha, A. K., Burai-Patrascu, M., Rostaing, O., Chukka, P., Singh, B., Janezic, M., Moitessier, A., Moitessier, N., & Pottel, J. (2025) · ChemRxiv · doi:10.26434/chemrxiv-2023-5g38r-v2
Kim, J., Correy, G. J., Hall, B. W., Rachman, M. M., Mailhot, O., Togo, T., Gonciarz, R. L., Jaishankar, P., Neitz, R. J., Hantz, E. R., et al. (2026) · eLife · doi:10.7554/elife.110475
Enabling Automatic Generation of Protein–Ligand Complex Data Sets with Atomistic Detail
Gutermuth, T., Ehmki, E. S. R., Flachsenberg, F., Penner, P., Hönig, S. M. N., Harren, T., & Rarey, M. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00150
Huang, H. (2026) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2026.118960
Thai, K., Duong, C. Q., Nguyen, T. T. T., Tran, V.-T., Mai, Q.-M., & Le, M.-T. (2026) · Royal Society of Chemistry eBooks · doi:10.1039/9781837070206-00001
Targeting the RNA-Binding Site of SARS-CoV-2 NSP13 by FRASE-bot in CACHE Challenge #2
Wang, X., Mettu, A., Herasymenko, O., Silva, M., Houliston, S., Chau, I., Ghiabi, P., Gibson, E., Perveen, S., Seitova, A., et al. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00560
Self-citationPapadourakis, M., Amaxopoulou, E. E., & Matsoukas, M. (2026) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2026.2712551
The sweet spot in protein design—Where deep learning meets first principles
Cia, G., Orlando, G., Cianferoni, D., van der Kant, R., Delgado, J., Verschueren, E., Rousseau, F., Serrano, L. G., & Schymkowitz, J. W. H. (2026) · FEBS Letters · doi:10.1002/1873-3468.70429
APPLICATION OF MACHINE LEARNING METHODS IN EARLY DRUG DEVELOPMENT
Maksimov, A. S., & Дергачёва, Ж. М. (2026) · Vestnik Farmacii · doi:10.52540/2074-9457.2026.1.72
Hentabli, H., Mettu, A., Wang, X., & Kireev, D. (2026) · ChemRxiv · doi:10.26434/chemrxiv.15007932/v1
Machine Learning‐Aided Small‐Molecule Virtual Screening: Recent Advances and Future Perspectives
Wang, Y., Bansal, N., Wa, S., Sciabola, S., & Wang, Y. (2026) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70084
Li, L., Soriano, L. D., Kedir, W. M., Hoch, F. L., & Loke, D. K. (2026) · Molecular Biomedicine · doi:10.1186/s43556-026-00498-1
About artificial intelligence adoption in antiviral drug discovery: Thoughts after ICAR 39
Harutyunyan, V., Matevosyan, M., Brancale, A., & Zakaryan, H. S. (2026) · Antiviral Research · doi:10.1016/j.antiviral.2026.106538
Kim, J., Correy, G. J., Hall, B. W., Rachman, M. M., Mailhot, O., Togo, T., Gonciarz, R. L., Jaishankar, P., Neitz, R. J., Hantz, E. R., et al. (2026) · eLife · doi:10.7554/elife.110475.2
Cooper, C. D. O., Tankhilevich, E., Remans, K., Marjou, A. E., Edwards, A. M., Leach, A. R., Quigley, A., Schütz, A., Zemella, A., de Marco, A., et al. (2026) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2026.113610
Self-citationTitus, I., & Palaniappan, A. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00304
Castellanos, M. A., MacDermott-Opeskin, H., Ainsley, J., Swain, J. A., Colby, S., West, D., Channing, G., Good, A., Bourque, É., Vogeti, L., et al. (2026) · ChemRxiv · doi:10.26434/chemrxiv.15009342/v2
Castellanos, M. A., MacDermott-Opeskin, H., Ainsley, J., Swain, J. A., Colby, S., West, D., Channing, G., Good, A., Bourque, É., Vogeti, L., et al. (2026) · ChemRxiv · doi:10.26434/chemrxiv.15009342/v3
Shivaraj, D. R., A, R. M., & Pushpanjali, M. T. (2026) · doi:10.66079/japsnp.v1i5.90
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
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