BAPULM: Binding Affinity Prediction using Language Models

TL;DR

BAPULM introduces a sequence-based framework utilizing language models to predict drug-target binding affinities, providing a scalable and accurate alternative to traditional 3D structural methods in drug discovery.

Contribution

The paper presents BAPULM, a novel sequence-based approach leveraging ProtT5-XL-U50 and MolFormer for binding affinity prediction without relying on 3D structural data.

Findings

Achieved high correlation scores on benchmark datasets

Demonstrated robustness across diverse datasets

Validated as an effective alternative to 3D methods

Abstract

Identifying drug-target interactions is essential for developing effective therapeutics. Binding affinity quantifies these interactions, and traditional approaches rely on computationally intensive 3D structural data. In contrast, language models can efficiently process sequential data, offering an alternative approach to molecular representation. In the current study, we introduce BAPULM, an innovative sequence-based framework that leverages the chemical latent representations of proteins via ProtT5-XL-U50 and ligands through MolFormer, eliminating reliance on complex 3D configurations. Our approach was validated extensively on benchmark datasets, achieving scoring power (R) values of 0.925 $\pm$ 0.043, 0.914 $\pm$ 0.004, and 0.8132 $\pm$ 0.001 on benchmark1k2101, Test2016_290, and CSAR-HiQ_36, respectively. These findings indicate the robustness and accuracy of BAPULM across diverse datasets and underscore the potential of sequence-based models in-silico drug discovery, offering a scalable alternative to 3D-centric methods for screening potential ligands.