Leveraging Transfer Learning for Predicting Protein-Small-Molecule Interaction Predictions.

Abstract

A complex web of intermolecular interactions defines and regulates biological processes. Understanding this web has been particularly challenging because of the sheer number of actors in biological systems: ∼10⁴ proteins in a typical human cell offer plausible 10⁸ interactions. This number grows rapidly if we consider metabolites, drugs, nutrients, and other biological molecules. The relative strength of interactions also critically affects these biological processes. However, the small and often incomplete data sets (10³-10⁴ protein-ligand interactions) traditionally used for binding affinity predictions limit the ability to capture the full complexity of these interactions. To overcome this challenge, we developed Yuel 2, a novel neural network-based approach that leverages transfer learning to address the limitations of small data sets. Yuel 2 is pretrained on a large-scale data set to learn intricate structural features and then fine-tuned on specialized data sets like PDBbind to enhance the predictive accuracy and robustness. We show that Yuel 2 predicts multiple binding affinity metrics, K_d, K_i, and IC₅₀, between proteins and small molecules, offering a comprehensive representation of molecular interactions crucial for drug design and development.