Bernard R. Brooks

Machine Learning-Driven Drug Repurposing for KRAS G12C and KRAS G12D Inhibition

This study uses machine learning to screen FDA-approved compounds for KRAS G12C and G12D inhibition, identifying potential drug candidates for further experimental validation.

Graph Neural Networks Model Based on Atomic Hybridization for Predicting Drug Targets

This paper develops a hybrid deep learning model combining graph neural networks and molecular descriptors to predict drug targets with high accuracy and interpretability.

Characterizing the Ion-Conductive State of the α7-Nicotinic Acetylcholine Receptor via Single-Channel Measurements and Molecular Dynamics Simulations

This study combines single-channel recordings and MD simulations to evaluate the conductive properties of different 7-nAChR structures, identifying which conformations align with physiological function.

The role of induced polarization in drug discovery applications

This study calculates polarizability tensors for drug-like molecules and uses machine learning to predict IC50s, showing induced polarizability's importance in binding.

Deep attention based variational autoencoder for antimicrobial peptide discovery

This study uses a variational autoencoder with attention mechanisms to generate novel antimicrobial peptides with physicochemical properties similar to real ones.