Open-ComBind: Harnessing unlabeled data for improved binding pose prediction

06 September 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Determination of the bound pose of a ligand is a critical first step in many in silico drug discovery tasks. Molecular docking is the main tool for the prediction of non-covalent binding of a protein and ligand system. Molecular docking pipelines often only utilize the information of one ligand binding to the protein despite the commonly held hypothesis that different ligands share binding interactions when bound to the same receptor. Here we describe Open-ComBind, an easy-to-use, open-source version of the ComBind molecular docking pipeline that leverages information from multiple ligands without known bound structures to enhance pose selection. We first create distributions of feature similarities between ligand pose pairs, comparing near-native poses with all sampled docked poses. These distributions capture the likelihood of observing similar features, such as hydrogen bonds or hydrophobic contacts, in different pose configurations. These similarity distributions are then combined with a per-ligand docking score to enhance overall pose selection by 8.5% and 4.5% for high-affinity and congeneric series helper ligands, respectively. Open-ComBind reduces the average RMSD of ligands in our benchmark dataset by 8.2%. We provide Open-ComBind as an easy-to-use command line and Python API to increase pose prediction performance at


Molecular Docking
Machine Learning
Structure-based drug design

Supplementary materials

Supplementary Information
Supplementary Tables S1-S3 and Supplementary Figures S1-S7.


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