用SPLIF增强的注意力驱动的3DCNN为METTL3提供精确可靠的蛋白质-连接体相互作用建模
Muhammad Junaid1,2, Muhammad Zeeshan3, Abbas Khan4
1Institute for Advanced Study, Shenzhen University, Shenzhen 518060, China.
ACS omega
|May 5, 2025
概括
DeepMETTL3是一种新的评分功能,通过将深度学习与蛋白质-连接体相互作用指纹集成来增强基于结构的虚拟选 (SBVS). 这种人工智能驱动的方法提高了药物发现的准确性和效率.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 结构生物学是结构生物学.
背景情况:
- 在基于结构的虚拟选 (SBVS) 中,传统的评分函数难以准确地模拟复杂的蛋白质-连接体相互作用.
- 需要更准确和更强大的评分功能来提高药物发现管道的效率.
研究的目的:
- 开发和验证DeepMETTL3,一种基于深度学习的新型评分功能,用于增强SBVS.
- 评估DeepMETTL3与使用METTL3作为治疗目标的传统评分函数的性能.
主要方法:
- 集成3D卷积神经网络 (CNN),多头注意力机制和高维结构蛋白-连接物相互作用指纹 (SPLIF).
- 基于架构的数据分割策略和对多个测试集的验证,包括那些与训练数据化学相似度较低的测试集.
- 研究了主动与诱比率和注意力机制放置对模型性能的影响.
主要成果:
- 对于SBVS来说,DeepMETTL3在准确性,稳定性和可扩展性方面明显优于传统的评分功能.
- 在训练组中,主动与诱的比例为1:50,并将注意力机制放置在CNN1之后,改善了模型的概括性.
- 在对METTL3目标进行活性和非活性化合物的分类方面表现出卓越的表现.
结论:
- 在SBVS的目标特定机器学习中,DeepMETTL3代表了显著的进步,提供了更好的预测能力.
- 开发的框架可以适应其他生物目标,突出显示了基于人工智能的药物设计中深度学习的潜力.
- DeepMETTL3平衡了计算效率与预测准确性,推进了分子对接和虚拟选功能.
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