CASTER-DTA: equivariant graph neural networks for predicting drug-target affinity.

ABSTRACT: Accurately determining the binding affinity of a ligand with a protein is important for drug design, development, and screening. With the advent of accessible protein structure prediction methods such as AlphaFold, predicted protein 3D structures are readily available; however, scalable methods for predicting binding affinity currently do not take full advantage of 3D protein information. Here, we present CASTER-DTA (Cross-Attention with Structural Target Equivariant Representations for Drug-Target Affinity), which uses an equivariant graph neural network (GNN) to learn more robust protein representations alongside a standard GNN to learn molecular representations to predict DTA. We augment these representations by incorporating an attention-based mechanism between protein residues and dru

SUBMITTER: Kumar R 

PROVIDER: S-EPMC12554097 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature