From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction.

ABSTRACT: Accurate in silico prediction of protein-ligand binding affinity is important in the early stages of drug discovery. Deep learning-based methods exist but have yet to overtake more conventional methods such as giga-docking largely due to their lack of generalizability. To improve generalizability, we need to understand what these models learn from input protein and ligand data. We systematically investigated a sequence-based deep learning framework to assess the impact of protein and ligand encodings on predicting binding affinities for commonly used kinase data sets. The role of proteins is studied using convolutional neural network-based encodings obtained from sequences and graph neural network-based encodings enriched with structural information from contact maps. Ligand-based encoding

SUBMITTER: Gorantla R 

PROVIDER: S-EPMC11005465 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature