AI-DPAPT:用于预测 PROTAC 活动的机器学习框架
Amr S Abouzied1,2, Bahaa Alshammari3, Hayam Kari4
1Department of Pharmaceutical Chemistry, College of Pharmacy, University of Hail, 81442, Hail, Saudi Arabia. as.ibrahim@uoh.edu.sa.
Molecular diversity
|October 19, 2024
概括
一个人工智能工具使用机器学习和分子结构预测蛋白解向奇美拉 (PROTAC) 活动. 这种计算方法可以准确地识别候选药物,加速治疗开发.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 药理学中的人工智能
背景情况:
- 针对性蛋白质降解 (TPD) 使用蛋白质溶解向金马 (PROTACs) 对于药物开发至关重要.
- 预测小分子与目标蛋白的相互作用是复杂和具有挑战性的.
研究的目的:
- 开发一种基于人工智能的工具,以基于化学结构来预测PROTAC活动.
- 提高识别和优化PROTAC候选药物的准确性和效率.
主要方法:
- 利用机器学习算法和分子指纹技术.
- 在PROTAC化学结构及其活动的数据集上训练模型.
- 包括随机森林在内的评估分类器性能,达到0.97.9的AUC.
主要成果:
- 人工智能-DPAPT工具准确预测PROTAC活动,所有模型都显示ROC曲线高于0.9.
- 随机森林模型表现出高预测准确度,AUC得分为0.97.
- 确定了影响PROTAC功能和结构-活动关系的关键分子特征.
结论:
- 开发的AI平台通过预测PROTAC功能,为计算药物开发做出了重大贡献.
- 该工具加速了新型治疗剂的识别和改进.
- 人工智能-DPAPT平台可在线访问,用于更广泛的研究应用.
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