Synthesizability-constrained expansion and multi-objective evolution of antitubercular compounds

项目总结 迫切需要新的方法来推动新的化学实体作为临床抗结核药物 关联性。一个关键障碍是构成小分子从点击到领先的优化的多标准过程 与病原菌的药物敏感和耐药菌株的体外效力 结核分枝杆菌(Mtb)该项目将解决的设计，建设和验证的小说 用机器学习方法预测两种关键分子特性：体外结核分枝杆菌生长抑制 和小鼠在血浆中的药代动力学暴露。通过外部测试验证的最终模型 统计将得到计算方法的补充，依赖于专家编码的反应模板 和/或学习的反应预测模型，以预测两种有希望的抗结核药物的最佳合成路线 小分子：JSF-3005和CD117。这些数据将通知HIT的可合成类似物的枚举 用多目标帕累托最优化对候选类比进行选择后进行扩展 结合具有或不具有对接分数的多个替代QSAR模型的标准。最优度分数将指导 可合成性约束分子优化的遗传算法替代穷举法 正向枚举。然后对每个系列中得分最高的候选先导化合物进行关键成分分析 验证这一新方法的分子性质并为进一步研究提供新的抗结核药物 不在这项提议的范围之内。

PROJECT SUMMARY New approaches are urgently needed to advance new chemical entities as antitubercular agents of clinical relevance. A key hurdle is the multiple criteria process that constitutes hit-to-lead optimization of small molecules with demonstrated in vitro potency versus drug-sensitive and drug-resistant strains of the causative bacterium Mycobacterium tuberculosis (Mtb). This project will address the design, construction, and validation of novel machine learning approaches to predict two of these critical molecular properties: in vitro Mtb growth inhibition and mouse pharmacokinetic exposure in the plasma. The resulting models, validated through external test statistics, will be complemented by computational approaches, relying on expert-encoded reaction templates and/or learned reaction prediction models, to predict optimal synthetic routes to two promising antitubercular small molecules: JSF-3005 and CD117. These data will inform the enumeration of synthesizable analogs for hit expansion to be followed by the selection of candidate analogs scored with a multi-objective Pareto optimization criterion combining multiple surrogate QSAR models with or without docking scores. Optimality scores will guide a genetic algorithm for synthesizability-constrained molecular optimization as a replacement for exhaustive forward enumeration. The top-scoring candidate lead compounds in each series will be then assayed for critical molecular properties to validate this novel approach and supply novel antitubercular agents for further study outside the scope of this proposal.