重新思考人工智能范式,用双视角建模和实验验证来预测类似药物化合物的可溶性
Qilin Zhu1, Yuxin Qiu1, Guzhong Chen2
1State Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|September 25, 2025
概括
这项研究开发了可靠的机器学习 (ML) 模型来预测药物的水溶性,克服了药物开发中的挑战. 这些模型的性能优于深度学习,为发现新药提供了宝贵的工具.
科学领域:
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 水溶性对于药物输送和生物可用性至关重要.
- 准确的可溶性预测是药物开发中的持续挑战.
- 现有的机器学习模型通常由于庞大的化学空间而难以概括.
研究的目的:
- 编制最大的药物和类似药物的水溶性数据集.
- 开发和比较可靠的机器学习模型,用于可溶性预测.
- 为未来的溶解性预测模型建立一个基准,并帮助药物发现.
主要方法:
- 编制了迄今为止最大的水溶性数据集.
- 开发并比较各种回归和分类机器学习算法.
- 使用比较建模和堆叠组合方法.
- 分析了适用性领域,以验证模型的概括性.
主要成果:
- 由于数据的限制,统计机器学习算法,特别是堆叠模型,显示出比深度学习模型更高的准确性.
- 模型在药物领域内表现出强大的概括能力.
- 对十种潜在药物分子的实验验证证证了开发模型的高可靠性.
结论:
- 开发的模型为预测药物的水溶性提供了可靠的工具.
- 这项工作为未来的可溶性预测研究建立了一个全面的基准.
- 这些发现将通过指导潜在候选药物的选择来加速新药发现.
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