一个数据驱动的群体回归合成规划模型,灵感来自神经符号编程
Xuefeng Zhang1, Haowei Lin1, Muhan Zhang1
1Institute for Artificial Intelligence, Peking University, Beijing, China.
Nature communications
|January 2, 2025
概括
本研究介绍了一种用于回合成规划的神经符号算法,该算法学习可重复使用的合成模式. 这种方法显著减少了药物发现的计算时间,改善了人工智能生成的分子验证.
科学领域:
- 计算化学是一种计算化学.
- 化学领域的人工智能
- 药物发现信息学 药物发现信息学
背景情况:
- 深度生成模型加速药物发现,但在合成拟议的分子方面面临挑战.
- 当前的回合成规划方法经常独立处理分子,缺少可重复使用的合成模式.
- 由人工智能生成的小分子由于其新的结构和合成途径而存在独特的挑战.
研究的目的:
- 开发一个先进的回合成规划算法,利用可重复使用的合成模式.
- 为了提高预测反合成反应的效率和准确性.
- 改进深度生成模型产生的分子的验证.
主要方法:
- 开发了一个神经符号编程启发的算法,具有唤醒,抽象和梦想阶段.
- 增强了反应模板库,从数据中发现了可重复使用的合成模式.
- 实施了一种进化过程,以改进反应模板的预测模型.
- 将算法应用于类似分子组,以确定共享的合成路径.
主要成果:
- 该算法识别并纳入了可重复使用的合成模式,减少了边际推理时间.
- 神经象征方法与现有的回复合成方法相比,显示出更高的性能.
- 当计划类似分子的回复合成时,观察到推断时间的显著减少.
- 该方法有效地发现了潜在的化学模式,并增强了模型预测.
结论:
- 拟议的算法提供了一种更高效和有效的方法来回合成规划.
- 这种方法有可能通过改进分子合成验证来显著加速药物发现管道.
- 进化学习过程允许模型随着时间的推移而适应和改进,发现新的化学见解.
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