ArticleJournal of computer-aided molecular design2019
D3R Grand Challenge 3: blind prediction of protein-ligand poses and affinity rankings.
Article in Journal of computer-aided molecular design, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers.
What it found
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Who cites it
83 citing papers in PubMed, 149 citations in OpenAlex.
- Docking-based virtual screening: Past, present, and future.Biophysical journal · 2026Review
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- Evaluating generalization in protein-ligand cofolding methods.Nature structural & molecular biology · 2026Article
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- Discovery of potential RSV fusion protein inhibitors from benzimidazole derivatives using QSAR, molecular docking, and ADMET evaluation methods.Molecular diversity · 2026Article
- The Open Molecular Software Foundation (OMSF) and the Growing Role of Open Source Software in Molecular Modeling.Journal of chemical information and modeling · 2026Review
- A fully automated benchmarking suite to compare macromolecular complexes.Nature methods · 2026Review
- Article
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- Predicting Affinity Through Homology (PATH): Interpretable binding affinity prediction with persistent homology.PLoS computational biology · 2025Article
- ABCG2: A Milestone Charge Model for Accurate Solvation Free Energy Calculation.Journal of chemical theory and computation · 2025Article
- GRADE and X-GRADE: Unveiling Novel Protein-Ligand Interaction Fingerprints Based on GRAIL Scores.Journal of chemical information and modeling · 2025Article
- The Need for Continuing Blinded Pose- and Activity Prediction Benchmarks.Journal of chemical information and modeling · 2025Review
- Cheminformatics-based screening and evaluation of phytochemicals as CDK2 inhibitors in colorectal cancer therapy.PloS one · 2025Article
- Importance of Computer-aided Drug Design in Modern Pharmaceutical Research.Current drug discovery technologies · 2025Review
- Prediction of Threonine-Tyrosine Kinase Receptor-Ligand Unbinding Kinetics with Multiscale Milestoning and Metadynamics.The journal of physical chemistry letters · 2024Article
- Antibody-peptide conjugates deliver covalent inhibitors blocking oncogenic cathepsins.Nature chemical biology · 2024Article
- AI is a viable alternative to high throughput screening: a 318-target study.Scientific reports · 2024Article
- Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations.Molecules (Basel, Switzerland) · 2024Article
- Docking strategies for predicting protein-ligand interactions and their application to structure-based drug design.Communications in information and systems · 2024Article
23 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 1 country.
Funding
Abstract
The Drug Design Data Resource aims to test and advance the state of the art in protein-ligand modeling by holding community-wide blinded, prediction challenges. Here, we report on our third major round, Grand Challenge 3 (GC3). Held 2017-2018, GC3 centered on the protein Cathepsin S and the kinases VEGFR2, JAK2, p38-α, TIE2, and ABL1, and included both pose-prediction and affinity-ranking components. GC3 was structured much like the prior challenges GC2015 and GC2. First, Stage 1 tested pose prediction and affinity ranking methods; then all available crystal structures were released, and Stage 2 tested only affinity rankings, now in the context of the available structures. Unique to GC3 was the addition of a Stage 1b self-docking subchallenge, in which the protein coordinates from all of the cocrystal structures used in the cross-docking challenge were released, and participants were asked to predict the pose of CatS ligands using these newly released structures. We provide an overview of the outcomes and discuss insights into trends and best-practices.
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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.