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人工智能辅助对抗色素氨酶抑制剂的优化:De Novo基于低活性化合物的分子生成
Hong Cai1,2, Wenchao Chen1,2, Jing Jiang3
1School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, PR China.
Journal of medicinal chemistry
|April 23, 2024
概括
人工智能 (AI) 使用强化学习 (RL) 产生了新的铁酶抑制剂. 优化的化合物显示出显著的抗色素活性,加速了药物发现.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 人工智能 (AI) 和深度强化学习 (RL) 是*de novo*分子生成的强大工具.
- 传统的药物发现往往是低效和耗时的.
研究的目的:
- 开发和应用一个碎片增长的RL策略,用于分子生成和优化.
- 确定具有增强活性和药物样性质的新型铁酶抑制剂.
主要方法:
- 利用强化学习 (RL) 框架进行序列分子生长.
- 集成的基于碎片生长的反应模板,同时进行目标对接和药物相似性预测.
- 合成和评估的产生的化合物对于铁酶抑制和抗颜色活性.
主要成果:
- 产生和合成了一系列的铁酶抑制剂,大多数化合物显示出比化合物更好的活性.
- 确定了一种最佳候选化合物,其有效性超过了酸,并在斑马鱼模型中显示出显著的抗色素.
- 代谢稳定性研究显示,合成的化合物对肝脏代谢的敏感性.
结论:
- 拟议的人工智能驱动的碎片生长战略有效地生成和优化药物发现分子.
- 这种方法提高了分子质量,多样性,可合成性和有效性,加速了强效药物候选者的识别.
- 基于人工智能的结构优化对提高传统药物发现过程的效率具有重大承诺.
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