AdapTor:适应拓回归用于定量结构-活动关系建模
Yixiang Mao1, Souparno Ghosh2, Ranadip Pal3
1Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA.
自适应拓回归 (AdapToR) 通过增强定量结构-活性关系 (QSAR) 模型来改进药物设计. 这种新方法提供了更好的药物反应预测,增加了解释性和降低了计算成本.
科学领域:
- 计算化学
- 化学信息学
- 药物发现和开发
背景情况:
- 定量结构-活性关系 (QSAR) 建模对于药物设计至关重要.
- 拓回归 (TR) 提供了效率和可解释性,但在选和重建方面存在局限性.
- 现有的QSAR模型,包括深度学习方法,在平衡预测能力和可解释性方面面临挑战.
研究的目的:
- 引入自适应拓回归 (AdapToR),一个增强的QSAR模型.
- 通过实施适应性选和基于优化的重建来克服标准TR的局限性.
- 提高药物反应预测模型的准确性,可解释性和计算效率.
主要方法:
- 开发了新的适应性选策略的AdapToR.
- 实施了基于优化的响应重建方法.
- 在NCI60GI50数据集上评估了AdapToR,包括60个癌细胞系的50,000多个药物反应.
- 将AdapToR与Transformer CNN,Graph Transformer,TR和其他基线QSAR模型进行比较.
主要成果:
- 与现有的QSAR模型相比,AdapToR在预测药物反应方面表现优异.
- 拟议的方法实现了比基于深度学习的模型更低的计算成本.
- 与复杂的深度学习架构相比,AdapToR在QSAR建模中提供了更好的解释性.
结论:
- 在药物反应预测的QSAR建模中,AdapToR代表了重大进步.
- 该模型有效地平衡了预测准确性,可解释性和计算效率.
- AdapToR有望加速药物发现和开发过程.
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