使用融合逆合成规划改进路线开发
Paula Torren-Peraire1,2, Jonas Verhoeven3, Dorota Herman3
1In-Silico Discovery, Research & Development, Johnson & Johnson, Beerse, 2340, Belgium. ptorrenperaire@gmail.com.
Journal of cheminformatics
|February 27, 2025
概括
这项研究引入了用于融合合成规划的新计算方法,提高了药物化学的效率. 该方法为多个目标确定共享的合成途径,将化合物合成增加近30%.
科学领域:
- 计算化学计算化学
- 药用化学 医学化学
- 有机合成 有机合成
背景情况:
- 计算机辅助合成规划通常产生从目标分子到构建块的个别路线.
- 现有的方法不能有效地利用多个目标的共享合成途径,限制了药物化学中的库合成.
- 融合合成,最大限度地利用共享的中间体,对于高效的图书馆生产至关重要.
研究的目的:
- 开发一种新的计算方法,用于同时为多个目标分子规划融合的回合成路径.
- 提高复合计划在药物化学图书馆合成中的效率和实用性.
- 识别和分析复杂的合成路由,使用来自现实世界数据集的共享中间体.
主要方法:
- 利用基于图形的处理管道来分析约翰逊和约翰逊电子实验室笔记本 (J&J ELN) 和公共数据集.
- 开发了一种多目标,多中间计划方法,以机器学习单步回归合成模型为指导.
- 通过确定融合路线并评估其可解决性来评估该方法.
主要成果:
- 超过70%的分析反应是融合合成的一部分,覆盖了超过80%的J&J ELN项目.
- 新型规划方法确定了80%以上的试验案例的融合路线,单个化合物的可溶性超过90%.
- 与单个搜索方法相比,融合搜索能够同时合成近30%的化合物,从而增加了常见中间体的利用率.
结论:
- 开发的融合合成规划方法显著提高了药物化学中的效率和实用性.
- 融合逆合成策略普遍存在,并且非常有效,新方法显示出高可溶性和更高的化合物产量.
- 这种方法提供了一个强大的工具,通过最大限度地实现融合和共享中间体来优化库合成.
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