ArticleJournal of chemical information and modeling2020
Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design.
Article in Journal of chemical information and modeling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 164 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
164 citing papers in PubMed.
- The rise of agentic AI in drug discovery from static tools to autonomous virtual scientists.npj drug discovery · 2026Review
- Pocket contrastive learning and conformation fusion for robust structure-conditioned 3D molecular generation under receptor conformational variation.Bioinformatics (Oxford, England) · 2026Article
- PockLigGPT: Pocket-Sequence-Conditioned Molecular Generation with GPTs and RL.Journal of chemical information and modeling · 2026Article
- Cloud-based ligand-guided virtual screening with deep-learning-enhanced docking identifies a micromolar CXCR4 antagonist.Molecular diversity · 2026Article
- GAMT-GINE: A Graph Isomorphism Network Integrating Continuous Spatial Awareness and Multi-Task Learning for Protein-Ligand Binding Affinity Prediction.International journal of molecular sciences · 2026Article
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- Autonomous bioisosteric replacement for multi-property optimization in drug design.Nature communications · 2026Article
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- Deep generative models for 3D structure-based drug design and molecular optimisation: a comprehensive survey.Journal of computer-aided molecular design · 2026Review
- Navigating chemical-linguistic sharing space with heterogeneous molecular encoding.Nature communications · 2026Article
- FLOWR.ROOT - A flow matching-based foundation model for joint multi-purpose structure-aware 3D ligand generation and affinity prediction.Nature communications · 2026Article
- ScrambleBench: a workflow for comparative assessment of structure-based de novo generative models.Journal of cheminformatics · 2026Article
- Structure-aware artificial intelligence for next-generation drug discovery: from protein-ligand modeling to generative biomolecular design.Briefings in bioinformatics · 2026Review
- Revisiting Target-Aware de novo Molecular Generation with TarPass: Between Rational Design and Texas Sharpshooter.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Evaluating AI-Generated Molecules for Drug Discovery: From Generic Metrics to Translational Readiness.International journal of molecular sciences · 2026Review
- Pep2Mol: 3D Molecule Generation Targeting Protein-Protein Interfaces with Diffusion Models.bioRxiv : the preprint server for biology · 2026Article
- MotifLeadDB: A Hierarchical Structural Data Set for Congeneric Ligand Binding Activity Change.Journal of chemical information and modeling · 2026Article
- Prediction variability in physiologically based pharmacokinetic modeling of tissue disposition under deep uncertainty.NPJ systems biology and applications · 2026Article
- MolPIF: a parameter interpolation flow model for molecule generation.Bioinformatics (Oxford, England) · 2026Article
- More protein-ligand data are needed for AlphaFold-like models to enable drug discovery.Current opinion in structural biology · 2026Review
104 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
One of the main challenges in drug discovery is predicting protein-ligand binding affinity. Recently, machine learning approaches have made substantial progress on this task. However, current methods of model evaluation are overly optimistic in measuring generalization to new targets, and there does not exist a standard data set of sufficient size to compare performance between models. We present a new data set for structure-based machine learning, the CrossDocked2020 set, with 22.5 million poses of ligands docked into multiple similar binding pockets across the Protein Data Bank, and perform a comprehensive evaluation of grid-based convolutional neural network (CNN) models on this data set. We also demonstrate how the partitioning of the training data and test data can impact the results of models trained with the PDBbind data set, how performance improves by adding more lower-quality training data, and how training with docked poses imparts pose sensitivity to the predicted affinity of a complex. Our best performing model, an ensemble of five densely connected CNNs, achieves a root mean squared error of 1.42 and Pearson
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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.