从蓝藻细菌的二次代谢物中选潜在的抗病毒化合物,使用机器学习
Tingrui Zhang1,2,3,4, Geyao Sun4, Xueyu Cheng1,2,4
1Marine Ecology and Human Factors Assessment Technical Innovation Center of Natural Resources Ministry, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China.
Marine drugs
|November 26, 2024
概括
人工智能选了2000多种蓝绿藻化合物,确定了364种潜在的抗病毒药物. 机器学习模型有效地确定了新型抗病毒药物,加速了从自然产品中发现药物的速度.
科学领域:
- 自然产品化学 自然产品化学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 蓝绿藻产生的各种二次代谢物具有潜在的抗病毒性质.
- 这些化合物的现有选方法往往是低效的,阻碍了药物发现.
- 先进的虚拟查提供了一种更快,更具成本效益的方法来识别新型抗病毒药物.
研究的目的:
- 开发和应用机器学习模型,用于从蓝藻细菌的二次代谢产物中高效选抗病毒化合物.
- 用计算方法和生物标来识别和验证潜在的抗病毒候选者.
主要方法:
- 利用传递信息的神经网络的机器学习方法来训练预测模型.
- 选了2000多种蓝藻细菌二次代谢物的库,以确定潜在的抗病毒化合物.
- 使用针对HIV-1逆转录酶 (HIV-1RT) 的分子对接实验来验证候选化合物.
主要成果:
- 从蓝藻细菌库中成功选出364种潜在的抗病毒化合物,其中胺是占主导地位的类别.
- 实现了高模型性能,接收器运行特征曲线值下的面积为0.98.98.
- 确定了特定的化合物 (例如,kororamide,mollamide E),这些化合物与HIV-1 RT目标具有强烈的结合相互作用.
结论:
- 基于人工智能的查是从天然产品库中发现抗病毒化合物的强大而有效的工具.
- 开发的机器学习模型可以显著加快新药候选药物的识别.
- 这种方法有助于探索大自然产品池的治疗应用.
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