CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding
By S. Ackloo, R. Al-Awar, R. Amaro, C. Arrowsmith, Hatylas Azevedo, R. Batey, Yoshua Bengio, U. Betz, C. Bologa, John D. Chodera and 29 more. 2021
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One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small-molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small-molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared and openly published. CACHE will launch three new benchmarking exercises every year. The outcomes will be better prediction methods, new small-molecule binders for target proteins of importance for fundamental biology or drug discovery and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins. Critical Assessment of Computational Hit-finding Experiments (CACHE) is a public benchmarking project to compare and improve computational small-molecule hit-finding approaches through cycles of prediction, compound synthesis and experimental testing. By that, CACHE will enable a more efficient and effective approach to hit identification and drug discovery.
Subfields: Computational Theory and Mathematics, Immunology, Molecular Biology
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Citations in the corpus
- Computational approaches streamlining drug discovery (2023, methodology): “Going forward, thorough benchmarking of physics-based, ML and hybrid approaches will be a key focus of a new Critical Assessment of Computational Hit-finding Experiments (CACHE), which will assess five specific scenarios relevant to practical hit and lead discovery and optimization 97 .”