变压器解码器GPT模型用于生成虚拟选库的HMG-CoenzymeA降解酶抑制剂:温度的影响,快速长度和转移学习策略.
1School of Chemistry, Food and Pharmacy, University of Reading, Reading RG6 6AD, U.K.
Journal of chemical information and modeling
|November 7, 2024
概括
深度学习模型产生了新的HMG-Coenzyme A减少酶 (HMGCR) 抑制剂. 针对已知的抑制剂进行微调的最佳模型产生了强大的库,具有药物发现所需的分子性质.
科学领域:
- 计算化学和化学信息学
- 人工智能在药物发现中的作用
- 酶抑制研究的研究.
背景情况:
- HMG-Coenzyme A减少酶 (HMGCR) 是降胆固醇药物的关键标.
- 开发新的HMGCR抑制剂需要有效的方法来探索化学空间.
- 深度学习模型为新的药物设计提供了潜力.
研究的目的:
- 使用基于注意力的解码器模型生成新型HMGCR抑制剂的库.
- 优化模型预训练和微调策略,以产生强大的分子库.
- 确定分子性质和模型参数,从而产生理想的抑制剂候选者.
主要方法:
- 基于注意力的深度神经网络在ZINC15数据库上进行了预训练,并在HMGCR抑制剂上进行了微调.
- 模型架构 (层数) 和微调参数 (温度,提示长度) 系统地变化.
- 使用预测的IC50值,对接分数,药物相似性和与已知抑制剂的相似性来选生成的库.
主要成果:
- 具有50/50或25/75%预训练/微调比率,非零温度和更短提示的模型产生了最强大的库.
- 预测的IC50值与对接分数和与现有他类药物相似度相对应得很好.
- 42%的产生的分子被归类为类他类,与罗斯瓦斯塔丁类分子显示最好的预测疗效.
结论:
- 基于注意力的解码器模型可以有效地生成新的HMGCR抑制器库.
- 优化的深度学习方法可以指导发现强效和类似药物的分子.
- 这一战略加速了对HMGCR相关疾病有前途的候选药物的鉴定.
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