摩尔-零-GAN:为特定的蛋白质标分子生成对抗网络的零射击适应
Ravipas Aphikulvanich1, Natapol Pornputtapong2, Duangdao Wichadakul1,3
1Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University Bangkok 10330 Thailand ravipas.apk@gmail.com.
RSC advances
|December 13, 2023
概括
Mol-Zero-GAN产生具有理想特性的新药候选者,即使对于数据有限的目标也是如此. 这种深度学习框架优化了分子生成,而不需要额外的药物信息.
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
- 分子设计的生成模型.
背景情况:
- 药物发现对人类健康至关重要,深度学习提高了效率.
- 目前的生成模型难以创建具有特定特性 (QED,SA,BA) 的候选药物,并面临新型蛋白质标有限数据的挑战.
研究的目的:
- 引入Mol-Zero-GAN,这是一个创新的框架,用于生成具有所需特性的候选药物.
- 为了应对缺乏药物数据的蛋白质标生成模型培训的挑战.
- 在不需要额外的训练数据的情况下实现属性优化.
主要方法:
- 使用贝叶斯优化 (BO) 来识别最佳模型权重.
- 采用单值分解 (SVD) 进行重量分解.
- 适用于分子生成的生成对抗网络 (GAN) 架构.
主要成果:
- 摩尔-零-GAN成功地产生了具有药物相似性 (QED),合成可访问性 (SA) 和结合性亲和力 (BA) 所需的定量估计的候选药物.
- 与现有的最先进的方法相比,该框架显示出更高的性能.
- 在没有额外数据的情况下,实现针对特定蛋白质点的属性优化.
结论:
- Mol-Zero-GAN提供了一个有效的解决方案,用于生成具有特定特性的向药物候选物.
- 该框架克服了用于药物发现的深度学习中的数据限制.
- 能够为新的蛋白质标设计高效的de novo药物设计.
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