药物SynthMC:一种以原子为基础的药物样分子与蒙特卡洛搜索的生成
Milo Roucairol1, Alexios Georgiou1, Tristan Cazenave1
1LAMSADE, Université Paris-Dauphine, Pl. du Maréchal de Lattre de Tassigny, 75016 Paris, France.
Journal of chemical information and modeling
|September 9, 2024
概括
一个新的算法,DrugSynthMC,使用蒙特卡洛搜索方法快速生成新的药物样分子. 它产生可合成的化合物,扩大化学空间,克服当前药物发现方法的局限性.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 化学信息学 化学信息学
背景情况:
- 深度学习 (DL) 方法越来越多地用于新型化合物设计.
- 现有的DL方法往往专注于特定目标的绑定,忽视了一般的虚拟库扩展.
- 新设计的分子的合成能力仍然是药物发现的重大挑战.
研究的目的:
- 开发一种新的算法,用于快速,de novo生成可解释和可合成的类似药物的分子.
- 为了有效地扩大虚拟图书馆内的化学空间.
- 创建一个多功能工具,可以使用或不使用深度学习模型.
主要方法:
- 开发了DrugSynthMC,一个蒙特卡洛搜索 (MCS) 算法.
- 利用DL和统计先验来生成分子.
- 采用基于原子的搜索模型,逐个字符构建分子作为SMILES字符串.
- 确保生成的分子遵守利宾斯基的第5规则.
主要成果:
- 药物SynthMC每秒产生数千种可解释的,类似药物的分子.
- 该算法产生具有高水溶性和预测合成能力的化合物.
- 生成的分子有效地扩展化学空间,没有预定义的指标或训练数据.
- 这种方法是多功能性的,可以使用或不使用底层神经网络.
结论:
- 药物合成 (DrugSynthMC) 提供了一种快速多功能的方法,用于生成新型的药物样分子.
- 该算法克服了新药设计中的关键挑战,特别是合成能力和化学空间扩张.
- 药物合成 (DrugSynthMC) 便于创建大型,多样化的化合物库,用于功能评估和药物发现.
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