Rayees Rahman

CCGM: a Compound Coarse Grain Model representation for enhanced chemotype exploration, annotation and screening

The paper introduces CCGM, a compound representation model that improves chemotype exploration and screening by using a simplified graph structure to capture pharmacophore and chemotype features.

Leveraging multiple data types for improved compound-kinase bioactivity prediction

The paper proposes a two-stage machine learning method that combines single-dose and dose-response data to improve compound-kinase activity prediction, achieving high accuracy and hit rates in experimental validation.

Leveraging Machine Learning and AlphaFold2 Steering to Discover State-Specific Inhibitors Across the Kinome

This study uses AlphaFold2 Steering and machine learning to model kinase conformations and identify state-specific inhibitors, achieving a 53% hit rate in virtual screening and validating conformation-specificity for FLT3 ligands.

Protein structure-based gene expression signatures

This paper introduces structural gene expression signatures (sGES) derived from protein structure features, showing their utility in identifying robust biological signatures across datasets and experimental platforms.