John J. Irwin

The paper identifies TAAR1 agonists with antipsychotic-like effects through computational screening and experimental validation, showing improved potency and reduced side effects in a schizophrenia model.

This study uses library docking to identify subtype-selective CB2 receptor ligands, finding improved hit rates and affinities with larger libraries and structure-based optimization.

The paper tests if molecules from ultra-large libraries are more selective than those from bio-like libraries, finding similar off-target activity despite differences in chemical novelty.

The paper develops a database of docking results and experimental data to improve benchmarking and chemical space exploration.

The paper screens 14.6 million virtual isoquinuclidines against opioid receptors to identify dual antagonists-inverse agonists with reduced withdrawal effects compared to naloxone.

This paper tests the impact of library size and testing scale on virtual screening by comparing results from 1.7 billion vs. 99 million molecule libraries against AmpC β-lactamase, finding improved hit rates and potency with larger libraries.

The paper evaluates computational hit-finding methods for targeting the WDR domain of LRRK2, a Parkinson's disease protein, finding that diverse strategies yielded weakly potent compounds.

This paper introduces a method to calculate ligand strain energy using torsional populations from the Cambridge Structural Database, improving docking hit rates by filtering strained decoys.