Discovery of hematopoietic progenitor kinase 1 inhibitors using machine learning-based screening and free energy perturbation.

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 (IC < 1 μM). The FEP-guided modification of the in-house false-positive hit, , revealed that a single key atom change could trigger activity cliffs. The resulting was a potent HPK1 inhibitor (IC = 2.1 nM) and potently inhibited cellular HPK1 signaling and enhanced T-cell function. Molecular dynamics (MD) simulations and ADME predictions confirmed as candidate compound. This study provides new strategies and chemical scaffolds for HPK1 inhibitor development.