IDRdecoder: a machine learning approach for rational drug discovery toward intrinsically disordered regions.

ABSTRACT:

Introduction

Intrinsically disordered regions (IDRs) of proteins have traditionally been overlooked as drug targets. However, with growing recognition of their crucial role in biological activity and their involvement in various diseases, IDRs have emerged as promising targets for drug discovery. Despite this potential, rational methodologies for IDR-targeted drug discovery remain underdeveloped, primarily due to a lack of reference experimental data.

Methods

This study explores a machine learning approach to predict IDR functions, drug interaction sites, and interacting molecular substructures within IDR sequences. To address the data gap, stepwise transfer learning was employed. IDRdecoder sequentially generate predictions for IDR classification, interaction sites, and int

SUBMITTER: Shionyu-Mitusyama C 

PROVIDER: S-EPMC12313641 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature