Multifunctional therapeutics have emerged as a solution to the constraints imposed by drugs with singular or insufficient therapeutic effects. The primary challenge is to integrate diverse pharmacophores within a single-molecule framework. To address this, we introduced DeepSA, a novel edit-based generative framework that utilizes deep simulated annealing for the modification of articaine, a well-known local anesthetic. DeepSA integrates deep neural networks into metaheuristics, effectively constraining molecular space during compound generation. This framework employs a sophisticated objective function that accounts for scaffold preservation, anti-inflammatory properties, and covalent constraints. Through a sequence of local editing to navigate the molecular space, DeepSA successfully identified , a derivative exhibiting potent analgesic properties and significant anti-inflammatory activity in various animal models. Mechanistic insights into revealed its dual mode of action: selective inhibition of Na1.7 and 1.8 channels, contributing to its prolonged local anesthetic effects, and suppression of inflammatory mediators modulation of the NLRP3 inflammasome pathway. These findings not only highlight the efficacy of as a multifunctional drug candidate but also highlight the potential of DeepSA in facilitating AI-enhanced drug discovery, particularly within stringent chemical constraints.

中文翻译：

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深度模拟退火用于发现具有局部麻醉和抗炎特性的新型牙科麻醉剂

多功能疗法的出现是为了解决治疗效果单一或不足的药物所带来的限制。主要挑战是将不同的药效团整合到单分子框架内。为了解决这个问题，我们引入了 DeepSA，这是一种新颖的基于编辑的生成框架，它利用深度模拟退火来修改阿替卡因（一种著名的局部麻醉剂）。DeepSA 将深度神经网络集成到元启发法中，有效地约束化合物生成过程中的分子空间。该框架采用复杂的目标函数来解释支架的保存、抗炎特性和共价约束。通过一系列局部编辑来导航分子空间，DeepSA 成功鉴定出一种衍生物，在各种动物模型中表现出有效的镇痛特性和显着的抗炎活性。机制研究揭示了其双重作用模式：选择性抑制 Na1.7 和 1.8 通道，有助于其延长局部麻醉作用，并抑制 NLRP3 炎症小体途径的炎症介质调节。这些发现不仅突显了 DeepSA 作为多功能候选药物的功效，而且突显了 DeepSA 在促进人工智能增强药物发现方面的潜力，特别是在严格的化学限制下。