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通过多任务图形变压器改进了药物样分子的内在溶解度的估计
Jiaxi Zhao1, Eline Hermans2, Kia Sepassi3
1Department of Pharmacy, Uppsala University, Uppsala, Sweden.
Journal of cheminformatics
|October 14, 2025
概括
准确预测药物的水溶性至关重要. 一个新的多任务图形变压器模型,在13306种化合物上进行训练,实现了最先进的性能,改善了药物发现预测.
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:
- 水溶性对于药物发现和开发至关重要.
- 使用机器学习准确地预测可溶性,由于数据限制和算法约束,仍然具有挑战性.
- 现有的方法往往缺乏用于训练的药物类化合物的全面数据集.
研究的目的:
- 开发一个改进的机器学习模型来预测内在的水溶性.
- 为了应对有限,集中数据集的挑战,用于溶解性预测.
- 为了利用类似药物化合物的大型内部数据集进行模型培训.
主要方法:
- 利用了约翰逊和约翰逊公司内部的溶解度数据.
- 使用理论pH-溶解度方程和pKa预测工具计算内在溶解度.
- 在13306种化合物上开发和训练了一种多任务图形变压器模型,其中包括物理化学性质.
主要成果:
- 多任务图形变压器模型实现了0.61.6的根平均平方误差 (RMSE).
- 该模型在高质量的测试数据上显示了0.60的确定系数 (R2).
- 在估计类似药物化合物的内在溶解度方面取得了最先进的性能.
结论:
- 开发的模型显示了对准确的内在可溶性预测的重大前景.
- 利用大型,精心策划的数据集是推进可溶性预测模型的关键.
- 这种方法可以提高药物发现和开发管道的效率.
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