拓识别生成和基于活动的过:为数据稀缺的四度化合物发现提供了一个计算-实验框架
Shiva Ghaemi1, Amanda Consylman2, Bo Pan3
1Department of Computer Science, George Mason University, Fairfax, Virginia 22030, United States.
Journal of chemical information and modeling
|March 5, 2026
概括
人工智能加速发现新的四级化合物 (QAC) 来对抗细菌耐药性. 计算过显著提高了新型抗菌候选药物的质量和成功率.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 抗微生物药物发现发现
背景情况:
- 四级化合物 (QAC) 是面临细菌耐药性上升的重要抗菌剂.
- 由于结构-活动数据和生成方法有限,开发新的QAC具有挑战性.
- 现有的方法很难有效地识别有前途的新QAC候选人.
研究的目的:
- 为了比较两个人工智能驱动的计算工作流程,以在数据有限的条件下加速QAC发现.
- 评估预测计算过对新型QAC候选人的质量的影响.
- 识别具有针对关键细菌病原体验证的抗微生物活性的新型QAC.
主要方法:
- 利用拓意识的变化自编码器来生成新的QAC结构.
- 工作流程1:直接对生成的质量评估结果进行专家评估.
- 工作流程2:在专家评估之前,对预测的抗微生物活性候选物的计算过.
- 对所选化合物的最小抑制度 (MIC) 的实验验证.
主要成果:
- 工作流2显著提高了候选化合物的质量:合成值得的化合物从9%增加到38%,无效输出从21%减少到0%.
- 通过计算预先过,可以更有效地满足专家评估时间限制.
- 针对四种细菌病原体,确定了11种新的QAC,其验证的MIC范围为1-32μM.
结论:
- 人工智能引导的生成与计算预先过相结合,有效地导航药物发现的数据稀缺的化学空间.
- 这种方法提高了识别新型抗菌剂的效率和成功率.
- 开发的工作流程提供了一个系统的策略,用于发现具有强大的生物活性的质量控制系统,解决抗菌素耐药性的挑战.
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