Mixture-of-Experts Based Dissociation Kinetic Model for De Novo Design of HSP90 Inhibitors with Prolonged Residence Time

作者：Yujing Zhao, Lei Zhang, Jian Du, Qingwei Meng, Li Zhang, Heshuang Wang, Liang Sun, Qilei Liu · 发表于：Journal of Chemical Information and Modeling · 年份：2024 · DOI：10.1021/acs.jcim.4c00726 · 被引用次数：9 · 研究领域：Computational Drug Discovery Methods、Crystallization and Solubility Studies、thermodynamics and calorimetric analyses

The dissociation rate constant ( k off ) significantly impacts the drug potency and dosing frequency. This work proposes a powerful optimization-based framework for de novo drug design guided by k off . First, a comprehensive database containing 2,773 unique k off values is created. Based on the database, a novel generic dissociation kinetic model is developed with a mixture-of-experts architecture, enabling high-throughput predictions of k off with high accuracy. The developed model is then integrated with an optimization-based mathematical programming approach to design drug candidates with low k off . Finally, the τ-RAMD method is utilized to rigorously verify the designed potential drug candidates. In a case study, the framework successfully identified numerous new potential HSP90 inhibitor candidates, achieving a maximum 45.7% improvement in residence time (τ = 1/ k off ) compared to that of a known exceptional HSP90 inhibitor. These findings demonstrate the feasibility and effectiveness of the kinetics-guided optimization-based de novo drug design framework in designing drug candidates with prolonged τ.