毒素GIN:一个In silico预测模型,通过图形异态网络预测毒性,集成序列和结构信息
Qiule Yu1, Zhixing Zhang1, Guixia Liu1
1Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
Briefings in bioinformatics
|November 12, 2024
概括
一个新的模型ToxGIN通过整合3D结构和氨基酸序列,准确地预测短药物毒性. 这种方法改进了现有的方法,有助于开发更安全的药物.
科学领域:
- 计算化学和药物发现
- 生物信息学和化学信息学
背景情况:
- 类药物具有治疗潜力,但在开发过程中在毒性预测方面面临挑战.
- 目前的毒性预测模型主要使用序列数据,经常忽视关键的3D结构信息.
- 准确的毒性预测对于有效和安全的药物开发至关重要.
研究的目的:
- 介绍ToxGIN,一种用于预测短毒性的新型计算模型.
- 整合氨基酸序列组成和3D结构数据,以提高预测准确度.
- 为了验证模型的性能与现有的毒性预测方法.
主要方法:
- 开发了ToxGIN,一个使用图形同态网络 (GIN) 的模型.
- 实施了三个模块结构:序列处理,基于GIN的特征提取和分类.
- 将的3D结构和序列转换为图形表示 (节点和边缘) 用于GIN分析.
主要成果:
- 在一个独立的测试组中,ToxGIN在F1得分为0.83,AUROC为0.91,马修斯相关系数为0.68.8的独立测试组中实现了高性能.
- 该模型与现有的毒性预测方法相比,显示出更高的性能.
- 结果证实了使用GIN.使用3D结构和序列数据组合的有效性.
结论:
- 通过GIN将3D结构信息与序列数据集成,可以显著改善短毒性预测.
- ToxGIN代表了基于的治疗方法的计算药物发现的有希望的进步.
- 该ToxGIN模型和相关数据是公开可用的,以促进进一步的研究.
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