Abstract
Hematopoietic progenitor kinase 1 (HPK1) is a key negative regulator of T-cell receptor (TCR) signaling and a promising target for cancer immunotherapy. The development of novel HPK1 inhibitors is challenging yet promising. In this study, we used a combination of machine learning (ML)-based virtual screening and free energy perturbation (FEP) calculations to identify novel HPK1 inhibitors. ML-based screening yielded 10 potent HPK1 inhibitors (IC50 < 1 μM). The FEP-guided modification of the in-house false-positive hit, DW21302, revealed that a single key atom change could trigger activity cliffs. The resulting DW21302-A was a potent HPK1 inhibitor (IC50 = 2.1 nM) and potently inhibited cellular HPK1 signaling and enhanced T-cell function. Molecular dynamics (MD) simulations and ADME predictions confirmed DW21302-A as candidate compound. This study provides new strategies and chemical scaffolds for HPK1 inhibitor development.
Communicated by Ramaswamy H. Sarma
Disclosure statement
No potential conflict of interest was reported by the authors.
Data availability statement
All data is provided in the manuscript and supplemental information.