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一种基于深度学习的多模型方法,用于预测类似药物的化学化合物的毒性
Konda Mani Saravanan1, Jiang-Fan Wan2, Liujiang Dai3
1Department of Biotechnology, Bharath Institute of Higher Education and Research, Chennai 600073, Tamil Nadu, India.
深度学习模型准确地预测小分子药物毒性,包括急性毒性,致癌性和致变性. 这种方法通过早期识别更安全的化合物来加速药物发现,从而降低成本和风险.
科学领域:
- 计算化学和化学信息学
- 药理学和毒理学 药理学和毒理学
- 人工智能在药物发现中的作用
背景情况:
- 药物开发是昂贵和耗时的,在过程中发现的有毒化合物导致了重大挫折.
- 对化合物毒性的早期和准确的预测对于减轻风险和优化小分子药物发现中的资源配置至关重要.
研究的目的:
- 开发和评估深度学习模型,用于预测各种类型的化合物毒性.
- 将这些模型集成到虚拟选管道中,以早期识别低毒性候选药物.
主要方法:
- 使用图形卷积网络 (GCN) 进行回归 (急性毒性) 和二进制分类 (致癌性,hERG_心脏毒性,肝毒性,变异性) 任务.
- 采用不同的培训策略来处理数据大小,标签类型和分布在不同毒性终点上的变化.
- 验证模型使用已批准的药物数据集来确定预测得分值.
主要成果:
- GCN回归模型在急性毒性预测方面取得了显著的表现 (皮尔森R:0.76,0.74,0.65用于IP,IV,口服途径).
- GCN二元分类模型显示出高预测能力,AUC分数在致癌性,hERG_心脏毒性,变异性和肝毒性方面从0.69到0.88不等.
- 综合模型成功地在虚拟查管道中识别出潜在的低毒性候选药物.
结论:
- 深度学习模型为药物开发中的化合物毒性的早期和准确预测提供了强大的方法.
- 这些模型可以通过更快地选择更安全的候选药物来显著降低与药物发现相关的成本和风险.
- 开发的模型是虚拟选和优先考虑具有有利毒性概况的化合物的宝贵工具.
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