在药物发现过程中增强药物向相互作用预测的生成框架
Roshan R Kotkondawar1, Sanjay R Sutar2, Arvind W Kiwelekar3
1Department of Information Technology, Dr. Babasaheb Ambedkar Technological University, Lonere, Raigad, Maharashtra, 402103, India. kotkondawarroshan@gmail.com.
Scientific reports
|October 13, 2025
概括
本研究介绍了VGAN-DTI,这是一种使用生成对抗网络 (GAN) 和变异自编码器 (VAE) 的新型AI框架,用于精确的in silico药物向相互作用预测,加速制药研究.
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
- 生物信息学是一种生物信息学.
背景情况:
- 在Silico中,药物向相互作用 (DTI) 的预测对于加速药物发现至关重要.
- 传统方法面临生物化学数据复杂性和规模的挑战,限制了准确性.
- 需要先进的计算框架来增强DTI预测.
研究的目的:
- 介绍VGAN-DTI,一种结合GAN,VAE和MLP的生成AI框架,用于改进DTI预测.
- 提高分子特征的编码,揭示分子机制.
- 为了提高药物发现的预测准确性和可靠性.
主要方法:
- 开发了VGAN-DTI,集成用于候选生成的生成对抗网络 (GAN) 和用于特征表示优化的变异自编码器 (VAE).
- 利用在BindingDB数据上训练的多层感知子 (MLPs),用于相互作用分类和结合亲和度预测.
- 进行了严格的废弃研究,以验证框架的稳定性.
主要成果:
- 实现了高性能指标:96%的准确性,95%的精度,94%的回忆和94%的F1分数.
- 与现有的DTI预测方法相比,表现出优越的性能.
- 验证了VGAN-DTI框架的有效性和稳定性.
结论:
- VGAN-DTI显著提高了in silico药物向相互作用预测的准确性.
- 该框架优化了药物发现的创新,合成可行性和预测准确性.
- 通过确保可靠的DTI预测,VGAN-DTI推进了数据驱动的制药研究.
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