Designing new hit series of JAK3 inhibitors using generative AI, reinforcement learning, and molecular dynamics. (PubMed, Comput Biol Med)
In a benchmark for ritlecitinib, we found that scaffolds NS_1813 and NS_2063 showed better binding affinities, with 20 compounds meeting our selection criteria...From these, six lead compounds were chosen for detailed molecular dynamics and docking pose analyses to assess their conformational stability and binding interactions within the JAK3 active site. These compounds are promising candidates for further development, including chemical synthesis, followed by in vitro and in vivo testing to evaluate their potential as JAK3-targeting therapeutic agents.