结构意识的双重向药物设计通过合作学习的药组合和分子模拟
Sheng Chen1,2, Junjie Xie1,2, Renlong Ye2
1School of Computer Science and Engineering, Sun Yat-sen University Guangzhou 510006 China.
Chemical science
|July 12, 2024
概括
AIxFuse是一种新的AI方法,通过学习药融合模式来设计双重向药物. 它有效地产生复杂疾病的分子,在药物发现成功率方面超过现有方法.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 医学中的人工智能
背景情况:
- 双目标药物设计对于癌症等复杂疾病至关重要.
- 结合药是一种常见的策略,但由于复杂的结构约束,它面临着挑战.
- 现有的方法往往涉及漫长的试错过程.
研究的目的:
- 开发一种结构意识的人工智能方法,AIxFuse,用于高效的双目标药物设计.
- 为了克服传统药物组合方法的局限性.
- 产生符合双重目标结构约束的候选药物.
主要方法:
- AIxFuse使用两个自玩强化学习 (RL) 代理来进行药选择和融合.
- 积极学习 (AL) 协作地完善分子对接分数.
- 该方法通过双目标分子对接模拟来学习融合模式.
主要成果:
- AIxFuse的性能优于最先进的方法,可以产生针对GSK3β/JNK3.3的双药物.
- 它在RORγt/DHODH目标上表现一致,成功率比竞争对手高5倍.
- 生成的分子满足两个目标的结合模式,具有有前途的结合自由能量.
结论:
- AIxFuse提供了一种高效和有效的方法,用于结构意识的双重向药物设计.
- 这种人工智能驱动的方法加速了对复杂疾病的新疗法的发现.
- AIxFuse成功地产生了具有所需双标性质的候选药物.
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