Jack A. Tuszyński

This paper uses DLA methods to analyze collagen fiber self-assembly in liver lobules and its impact on drug transport and metabolism across multiple scales.

The paper identifies potent broad-spectrum anti-coronaviral frameshift inhibitors through computational screening and experimental validation of RNA binding compounds.

This paper identifies key proteins in CLL using statistical thermodynamics and systems biology, advocating for multi-target inhibition strategies to improve therapy design.

This study compares machine learning models for predicting drug action on nuclear type I receptors, finding decision tree and naive Bayes models to be most efficient and accurate.

The paper trains machine learning models to replace core functions of BETSE for more efficient bioelectrical network simulations.

The paper analyzes the structural and evolutionary features of SARS-CoV-2 Mpro, finding that its active site differences and flexibility make it a poor target for small-molecule inhibitors.

The paper analyzes protein expression data from Lupus patients to identify key proteins in their interaction networks and suggests potential drug targets.

The paper analyzes thermodynamic measures of human brain development using Gibbs free energy from protein-protein interaction networks across age and sex.

The paper analyzes thermodynamic measures of human brain development by examining Gibbs free energy in protein-protein interaction networks across age and sex.