结合新的分子设计与半经验性蛋白质-合体结合的自由能量计算
Michael Iff1, Kenneth Atz1, Clemens Isert1
1ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland gisbert@ethz.ch.
RSC advances
|November 21, 2024
概括
我们结合了计算化学和人工智能来设计新的乙胆酶 (AChE) 抑制剂. 一个合成的分子显示了中度的微分子活性,验证了这种基于结构的药物设计方法.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 化学领域的人工智能
背景情况:
- 乙胆酶 (AChE) 是治疗神经退行性疾病的关键点.
- 胡佩尔A是一种强大的自然产品抑制剂的ACHE.
- 开发新型ACHE抑制剂需要有效的分子生成和活性预测方法.
研究的目的:
- 将半经验量子化学 (GFN2-xTB) 与 de novo 分子设计相结合,用于识别新型 AChE 抑制剂.
- 使用人工智能驱动的分子生成来探索Huperzine A周围的化学空间.
- 通过计算评估和实验验证设计的分子.
主要方法:
- 使用GFN2-xTB进行具有约束力的自由能量估计.
- 使用化学语言基于模型的de novo设计,具有SMILES和SELFIES表示.
- 进行了分子对接和吉布斯自由能量计算,用于虚拟选.
- 合成并经过生物测试的顶级候选分子.
主要成果:
- 创建了四个不同的分子库,采用不同的设计策略.
- 鉴定并合成了一种具有中度微分子ACHE抑制活性的结构新型分子.
- 证明了将人工智能驱动的生成与量子化学集成为药物设计的可行性.
结论:
- 综合方法显示出基于结构的药物设计的前景.
- 半经验量子化学和基于人工智能的生成可以加速发现潜在的候选药物.
- 需要进一步精细化,以克服预测生物活性和合成能力的局限性.
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