多药学浏览器PPB3:一个基于Web的深度学习工具,用于使用ChEMBL数据进行目标预测
Maedeh Darsaraee1, Sacha Javor1, Jean-Louis Reymond1
1Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland.
Journal of chemical information and modeling
|February 21, 2026
概括
深度神经网络可以预测多药学中的药物向相互作用. 这种方法通过识别分子如何与多个生物点相互作用,提高预测准确度来帮助药物开发.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 机器学习 机器学习
背景情况:
- 类似药物的分子经常与多个生物标相互作用,这种现象被称为多药理学.
- 评估多药理学对于有效的药物开发和理解药物机制至关重要.
研究的目的:
- 开发和验证用于预测药物向相互作用的深度神经网络模型.
- 使用大规模数据集评估生物活性分子的多药理学.
主要方法:
- 训练深度神经网络使用二进制子结构指纹的分子从ChEMBL 34.
- 基于活性数据的相关分子与目标 (≥50%在≤10μM处活跃).
- 利用了一个数据集,包括110万个分子和7546个目标之间的超过240万个相互作用.
主要成果:
- 这些模型在分子和目标的回忆和精度方面表现良好.
- 数据集的大小和范围 (包括蛋白质以外的各种目标类型) 远远超过以前的模型.
- 一个案例研究强调了与其他在线工具相比,这些模型的预测能力.
结论:
- 深度神经网络可以有效地预测药物向相互作用和多药理.
- 开发的模型为药物发现和开发提供了有价值的工具.
- 在线预测服务 (PPB3) 可供公众使用.
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