基于最佳运输的图核用于药物财产预测
Mohammed Aburidi1, Roummel Marcia1
1Department of Applied MathematicsUniversity of California Merced Merced CA 95348 USA.
本研究介绍了基于最佳运输 (OT) 的图核,用于预测药物吸收,分布,新陈代谢,分泌和毒性 (ADMET) 特性. 这些新的方法优于当前的图形深度学习模型,在药物开发中提供了更好的准确性和可解释性.
科学领域:
- 计算化学是一种计算化学.
- 机器学习是机器学习.
- 药物发现 药物发现
背景情况:
- 优化制药剂特性 (ADMET) 是至关重要的,但由于实验成本和数据限制,具有挑战性.
- 计算和预测工具,包括机器学习和基于图形的方法,在早期药物开发中变得越来越重要.
- 现有的方法在准确有效地预测复杂的ADMET配置文件方面面临挑战.
研究的目的:
- 开发和评估基于最佳传输 (OT) 理论的新型图核,用于预测药物ADMET特性.
- 评估基于OT的图形内核与最先进的深度学习模型的性能.
- 突出拟议方法的解释性,适应性和通用性的优点.
主要方法:
- 利用最佳运输 (OT) 理论来构建三个图核.
- 使用图形匹配生成相似性矩阵.
- 将相似性矩阵集成到ADMET属性的预测建模框架中.
主要成果:
- 基于OT的图形内核在19个ADMET数据集中表现出卓越的性能.
- 在19个数据集中的9个中表现优于最先进的图形深度学习模型.
- 在2个额外的数据集中显示了竞争性结果,在某些情况下甚至超过了高级图形神经网络 (GNN).
结论:
- 基于OT的新型图核为ADMET属性预测提供了高效和竞争力的方法.
- 这些方法比传统的图形神经网络提供了优势,包括增强的解释性,适应性和通用性.
- 这项研究证实了OT理论在推进计算药物发现和开发方面的潜力.
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