» Articles » PMID: 35783295

CACHE (Critical Assessment of Computational Hit-finding Experiments): A Public-private Partnership Benchmarking Initiative to Enable the Development of Computational Methods for Hit-finding

Abstract

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 3 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.

Citing Articles

Scaling Structure Aware Virtual Screening to Billions of Molecules with SPRINT.

McNutt A, Adduri A, Ellington C, Dayao M, Xing E, Mohimani H ArXiv. 2025; .

PMID: 39975427 PMC: 11838698.


Functionally active modulators targeting the LRRK2 WD40 repeat domain identified by FRASE-bot in CACHE Challenge #1.

Mettu A, Glavatskikh M, Wang X, Lara Ordonez A, Li F, Chau I Chem Sci. 2025; 16(8):3430-3439.

PMID: 39877816 PMC: 11770807. DOI: 10.1039/d4sc07532c.


Active learning driven prioritisation of compounds from on-demand libraries targeting the SARS-CoV-2 main protease.

Cree B, Bieniek M, Amin S, Kawamura A, Cole D Digit Discov. 2025; 4(2):438-450.

PMID: 39816163 PMC: 11726688. DOI: 10.1039/d4dd00343h.


SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction.

Cao D, Chen M, Zhang R, Wang Z, Huang M, Yu J Nat Methods. 2024; 22(2):310-322.

PMID: 39604569 DOI: 10.1038/s41592-024-02516-y.


vScreenML v2.0: Improved Machine Learning Classification for Reducing False Positives in Structure-Based Virtual Screening.

Andrianov G, Haroldsen E, Karanicolas J Int J Mol Sci. 2024; 25(22).

PMID: 39596415 PMC: 11595162. DOI: 10.3390/ijms252212350.


References
1.
McGovern S, Helfand B, Feng B, Shoichet B . A specific mechanism of nonspecific inhibition. J Med Chem. 2003; 46(20):4265-72. DOI: 10.1021/jm030266r. View

2.
Irwin J, Tang K, Young J, Dandarchuluun C, Wong B, Khurelbaatar M . ZINC20-A Free Ultralarge-Scale Chemical Database for Ligand Discovery. J Chem Inf Model. 2020; 60(12):6065-6073. PMC: 8284596. DOI: 10.1021/acs.jcim.0c00675. View

3.
Muller S, Ackloo S, Al Chawaf A, Al-Lazikani B, Antolin A, Baell J . Target 2035 - update on the quest for a probe for every protein. RSC Med Chem. 2022; 13(1):13-21. PMC: 8792830. DOI: 10.1039/d1md00228g. View

4.
Jansen J, Amaro R, Cornell W, Tseng Y, Walters W . Computational chemistry and drug discovery: a call to action. Future Med Chem. 2012; 4(15):1893-6. DOI: 10.4155/fmc.12.137. View

5.
Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee G . Accurate prediction of protein structures and interactions using a three-track neural network. Science. 2021; 373(6557):871-876. PMC: 7612213. DOI: 10.1126/science.abj8754. View