针对特定目标的新型分子及其配方:将合成能力纳入设计过程中
Sowmya Ramaswamy Krishnan1, Navneet Bung1, Rajgopal Srinivasan1
1TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India.
Journal of molecular graphics & modelling
|March 5, 2024
概括
这项研究引入了一种用于药物发现的新型人工智能 (AI) 方法,通过有效地探索化学空间来产生新的,针对特定目标的和可合成的分子.
科学领域:
- 药用化学 医学化学
- 人工智能的人工智能
- 计算机化药物设计技术
背景情况:
- 传统的药物发现依赖于大图书馆的选,限制了新奇性.
- 德诺沃设计探索了广的化学空间,但面临着合成挑战.
- 通过人工智能设计的新型分子的合成是药物化学家面临的重大障碍.
研究的目的:
- 开发一种生成性AI方法,用于设计新的,针对特定目标的,可合成的药物分子.
- 为了应对在人工智能驱动的药物发现中合成新设计分子的挑战.
- 探索可合成的化学空间,以确定可行的候选药物.
主要方法:
- 一个基于结构的药物设计框架,使用目标蛋白质结构和种子碎片.
- 一个基于模板的前向合成路线预测和使用蒙特卡罗树搜索 (MCTS) 的分子生成.
- 药物标亲和力 (DTA) 模型与对接集成,以指导分子生成和奖励MCTS代.
主要成果:
- 拟议的AI方法成功生成了具有预测合成能力的新型分子.
- 该方法使药物设计能够探索以前无法进入的化学空间.
- 综合合成预测和DTA建模的整合提高了识别可行的候选药物的效率.
结论:
- 这种基于前进合成的新型生成AI方法克服了基于AI的药物设计中的重大障碍.
- 这种方法有助于设计新的,针对特定目标的,可合成的分子.
- 该方法增强了AI在发现新疗法的实际应用.
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