ArticleNature reviews. Chemistry2022
CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding.
Article in Nature reviews. Chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
55 citing papers in PubMed, 92 citations in OpenAlex.
- Docking-based virtual screening: Past, present, and future.Biophysical journal · 2026Review
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Pushing the boundaries of virtual screening scale of combinatorial spaces with the V-SYNTHES approach.npj drug discovery · 2026Article
- Targeting the RNA-Binding Site of SARS-CoV-2 NSP13 by FRASE-bot in CACHE Challenge #2.Journal of chemical information and modeling · 2026Article
- A blinded, prospective benchmark of in silico antibody discovery anchored to experimental affinity and developability.Nature biotechnology · 2026Article
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- Atomic-level protein-ligand recognition with PBCNet2.0 for probe discovery.Nature chemical biology · 2026Article
- Modeling the Sensitivity of Large-Scale Virtual Screening to Scoring Function Accuracy, Artifacts, and Library Composition.Journal of chemical information and modeling · 2026Article
- FlowDock: A unified flow-based framework for flexible protein-ligand docking and binding affinity prediction.Acta pharmaceutica Sinica. B · 2026Article
- Mapping the avoid-ome: a systematic open-science approach to predictive ADMET.Nature communications · 2026Review
- Enabling Automatic Generation of Protein-Ligand Complex Data Sets with Atomistic Detail.Journal of chemical information and modeling · 2026Article
- The Last Mile Problem: A Critical Assessment of Physics-Based and AI Tools for Small Molecule Binding Prediction in Virtual Screening.Journal of chemical information and modeling · 2026Article
- A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data.Journal of chemical information and modeling · 2026Article
- AI-driven computational methods and benchmarking for T-cell antigen identification.Briefings in bioinformatics · 2026Review
- Blind Challenges Let Us See the Path Forward for Predictive Models.Journal of chemical information and modeling · 2026Article
- CACHE Challenge #3: Targeting the Nsp3 Macrodomain of SARS-CoV-2.Journal of chemical information and modeling · 2026Article
- Using generative AI to transform peptide hits into small molecule leads.Beilstein journal of organic chemistry · 2026Article
- An efficient computational chemistry approach to generating negative data for drug discovery pipeline validation.Frontiers in bioinformatics · 2026Article
- Large scale prospective evaluation of co-folding across 557 Mac1-ligand complexes and three virtual screens.bioRxiv : the preprint server for biology · 2025Article
- Results of the Protein Engineering Tournament: An Open Science Benchmark for Protein Modeling and Design.Proteins · 2025Article
Corrections and comments
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Authors and funding
41 authors at 20 institutions in 7 countries.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.