Jeffery B. Klauda

This paper introduces XAMPGen, an interpretable generative model for discovering novel antimicrobial peptides using fine-tuned protein language models and explainable AI techniques.

This paper introduces STAMP, a machine learning framework for predicting antimicrobial peptide efficacy across bacterial species, achieving strong predictive performance and real-world validation.

This study shows chlorpromazine inhibits EAG1 channels by disrupting PAS-CNBH domain coupling, without affecting VSD movement.

This study uses molecular dynamics simulations and machine learning to analyze how mutations affect Smlt1473's binding to substrates, identifying key residues involved in substrate interaction.

This study compares how two antipsychotics, chlorpromazine and clozapine, partition into and modify lipid membranes, revealing differences in their membrane remodeling effects despite differing D2R antagonism strengths.

The paper introduces two physically interpretable clustering metrics for evaluating MD simulation data, showing they align with physical intuition across different systems.

This study uses molecular dynamics simulations to compare phosphatidylcholine membrane properties between pure and cancer model membranes, finding differences in surface area, chain order, and bilayer thickness.

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