辅助区分器序列生成对抗网络对于少数样本分子的生成
Haocheng Tang1, Jing Long2, Beihong Ji1
1School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.
Journal of chemical information and modeling
|September 22, 2025
概括
辅助区分序列生成对抗网络 (ADSeqGAN) 增强了小数据集的分子生成. 这种新的方法通过生成特定的分子来改善药物发现,即使数据有限.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习用于化学.
背景情况:
- 生成模型在药物发现中常见的小数据集中扎.
- 对中枢神经系统药物和核酸结合剂等特定点的数据稀缺性阻碍了开发.
研究的目的:
- 介绍用于分子生成的辅助区分序列生成对抗网络 (ADSeqGAN).
- 在数据稀缺的场景中提高产生的分子的质量和类特异性.
主要方法:
- 将辅助随机森林分类器集成到GAN框架中.
- 为了稳定性和多样性,使用了预训练的发电机和瓦瑟斯坦距离.
- 评估ADSeqGAN对核酸结合剂,中枢神经系统药物和CB1配体.
主要成果:
- 与基线模型相比,ADSeqGAN显示出优越的核酸结合剂生成.
- 用ADSeqGAN过量采样改善了中枢神经系统药物生成产量.
- 产生了新的,类似药物的CB1配体,预测活性率高 (32.8%).
结论:
- ADSeqGAN是一个多功能框架,用于在数据有限的环境中进行分子设计.
- 该方法对开发向治疗药物,包括中枢神经系统药物和特定的连接物类显著有前途.
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