用分子动力学数据预测生物活动的神经网络模型:可光切换的案例
Anton Cherednichenko1,2, Sergii Afonin3, Oleg Babii3
1Taras Shevchenko National University of Kyiv, Kyiv, Ukraine.
Molecular informatics
|July 14, 2025
概括
机器学习模型使用分子动力学 (MD) 特性预测化合物活动. 这些动态特征改善了可光切换的神经网络 (NN) 预测,克服了静态描述器的局限性.
科学领域:
- 计算化学是一种计算化学.
- 机器学习 机器学习
- 药物发现 药物发现
背景情况:
- 预测化合物的生物活性通常依赖于使用神经网络 (NN) 的结合亲和力或基于连接体的方法.
- 静态分子描述器与大,灵活的分子 (如可光切换的二甲基乙烯 (DAE) ) 进行斗争,这些根据它们的异构形式表现出不同的活动.
- 现有的NN模型在准确预测这些复杂,动态分子的活动方面面临挑战.
研究的目的:
- 调查分子动力学 (MD) 轨迹特征的实用性,以改善含有DAE的可光切换的基于NN的活性预测.
- 开发和验证NN模型,可以预测柔性类模拟剂的生物活动,包括光异构体的区分.
主要方法:
- 从含有DAE的可光切换的经典分子动力学 (MD) 轨迹中提取特征.
- 开发了NN模型,利用这些MD衍生特征进行活动预测.
- 将MD衍生特征的性能与传统的2D和3D分子描述器进行了比较.
主要成果:
- 在DAE的NN活动预测模型中,MD衍生的特征显著优于静态的2D/3D描述符.
- 成功开发了两个NN模型:一个预测类同类的细胞毒性活性,另一个区分DAE光异构体的生物活性.
- 这些模型证明了对光异构体的活动的可靠预测,即使活动类型与训练集不同.
结论:
- MD衍生的动态特征增强了基于联体的NN活动预测模型的概括能力.
- 这种方法可以准确地预测以前被认为是难以处理的大型,形状灵活的分子.
- 这些发现为药物发现和设计开辟了新的途径,涉及可光切换和动态分子系统.
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