Protein-ligand binding affinity prediction based on profiles of intermolecular contacts

As a key element in structure-based drug design, binding affinity prediction (BAP) for putative proteinligand complexes can be efficiently achieved by the incorporation of structural descriptors and machine-learning models. However, developing concise descriptors that will lead to accurate and interpretable BAP remains a difficult problem in this field. Herein, we introduce the profiles of intermolecular contacts (IMCPs) as descriptors for machine-learning-based BAP. IMCPs describe each group of proteinligand contacts by the count and average distance of the group members, and collaborate closely with classical machine-learning models. Performed on multiple validation sets, IMCP-based models often result in better BAP accuracy than those originating from other similar descriptors. Additionally, IMCPs are simple and concise, and easy to interpret in model training. These descriptors highly conclude the structural information of protein-ligand complexes and can be easily updated with personalized profile features. IMCPs have been implemented in the BAP Toolkit on github ( https://github.com/debbydanwang/BAP).(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

Predicting the binding affinity of protein-ligand complexes is crucial for structure-based drug design. In this study, the authors introduce intermolecular contact profiles (IMCPs) as descriptors for machine-learning-based binding affinity prediction. IMCPs show better accuracy and interpretability compared to other similar descriptors, providing concise structural information for protein-ligand complexes.