Designing highly selective molecules for a drug target protein is a challenging task in drug discovery. This task can be regarded as a multiobjective problem that simultaneously satisfies criteria for various objectives, such as selectivity for a target protein, pharmacokinetic endpoints, and drug-like indices. Recent breakthroughs in artificial intelligence have accelerated the development of molecular structure generation methods, and various researchers have applied them to computational drug designs and successfully proposed promising drug candidates. However, designing efficient selective inhibitors with releasing activities against various homologs of a target protein remains a difficult issue. In this study, we developed a de novo structure generator based on reinforcement learning that is capable of simultaneously optimizing multiobjective problems. Our structure generator successfully proposed selective inhibitors for tyrosine kinases while optimizing 18 objectives consisting of inhibitory activities against 9 tyrosine kinases, 3 pharmacokinetics endpoints, and 6 other important properties. These results show that our structure generator and optimization strategy for selective inhibitors will contribute to the further development of practical structure generators for drug designs.

中文翻译：

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使用多目标蒙特卡洛树搜索的激酶同系物的选择性抑制剂设计

为药物靶蛋白设计高选择性分子是药物发现中的一项具有挑战性的任务。该任务可以被视为一个多目标问题，它同时满足各种目标的标准，例如目标蛋白质的选择性、药代动力学终点和类药指数。最近人工智能的突破加速了分子结构生成方法的发展，各种研究人员将其应用于计算药物设计，并成功提出了有前景的候选药物。然而，设计针对目标蛋白的各种同源物具有释放活性的有效选择性抑制剂仍然是一个难题。在这项研究中，我们开发了一种从头基于强化学习的结构生成器，能够同时优化多目标问题。我们的结构生成器成功提出了酪氨酸激酶的选择性抑制剂，同时优化了 18 个目标，包括对 9 种酪氨酸激酶的抑制活性、3 个药代动力学终点和 6 个其他重要特性。这些结果表明，我们的选择性抑制剂结构生成器和优化策略将有助于进一步开发用于药物设计的实用结构生成器。