IAP-CFDock: Iterative Anchor Prediction and Coarse-to-Fine Protein–Ligand Blind Docking
- 31 March 2026
- journal article
- Published by American Chemical Society (ACS) in Journal of Chemical Information and Modeling
- Vol. 66 (7) , 3509-3524
Abstract
Protein-ligand blind docking refers to predicting the docking conformation of a ligand with a complete protein, which is crucial for drug discovery. However, the large size of complete proteins, coupled with the complexity of their structures, makes this task highly challenging. To address these issues, we propose a novel blind docking framework, IAP-CFDock, which consists of three stages: Iterative Anchor Prediction, Coarse Docking, and Fine-Grained Docking. To enable high-accuracy anchor prediction, we design an efficient protein-ligand interaction network, termed the AP Layer. Experimental results show that the anchor prediction mechanism exhibits strong robustness, high accuracy, and flexibility. Comprehensive comparisons across multiple data sets and experimental settings demonstrate that IAP-CFDock achieves superior docking accuracy and generalization capability while maintaining high inference efficiency. Furthermore, we investigate the docking performance of various blind docking methods across different ligand sizes. Finally, extensive ablation studies validate the effectiveness of each component in IAP-CFDock and highlight its strong potential for further improvement.Keywords
Funding Information
- Science and Technology Innovation Plan Of Shanghai Science and Technology Commission (23S41900400)
This publication has 31 references indexed in Scilit:
- Leak Proof PDBBind: A Reorganized Data Set of Protein–Ligand Complexes for More Generalizable Binding Affinity PredictionThe Journal of Physical Chemistry B, 2026
- ESSENCE-Dock: A Consensus-Based Approach to Enhance Virtual Screening Enrichment in Drug DiscoveryJournal of Chemical Information and Modeling, 2024
- Machine-learning methods for ligand–protein molecular dockingDrug Discovery Today, 2022
- Forging the Basis for Developing Protein–Ligand Interaction Scoring FunctionsAccounts of Chemical Research, 2017
- Protein Binding Pocket DynamicsAccounts of Chemical Research, 2016
- Better Informed Distance Geometry: Using What We Know To Improve Conformation GenerationJournal of Chemical Information and Modeling, 2015
- Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking ExerciseJournal of Chemical Information and Modeling, 2013
- AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreadingJournal of Computational Chemistry, 2009
- Crystal structure of a cyanobacterial sucrose‐phosphatase in complex with glucose‐containing disaccharidesProteins: Structure, Function, and Bioinformatics, 2007
- Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciencesAtmospheric Environment, 1998