从使用变异性自编码器的差异性基因表达来设计具有多动力潜力的分子
Nutaya Pravalphruekul1, Maytus Piriyajitakonkij2, Phond Phunchongharn1,3
1Department of Computer Engineering, King Mongkut's University of Technology Thonburi, Bang Mod, Thung Khru, Bangkok 10140, Thailand.
Journal of chemical information and modeling
|June 22, 2023
概括
研究人员开发了BiCEV,这是一种使用基因表达数据来设计新分子的新型生成模型. 这种工具显示了加速药物发现的前途,通过帮助成功识别和优化来加速药物发现.
科学领域:
- 计算化学是一种计算化学.
- 基因组学就是基因组学.
- 药物发现 药物发现
背景情况:
- 药物对基因转录的影响在临床和研究应用中得到了充分研究.
- 药物诱导的转录变化为新的分子设计提供了潜力.
- 将表型与基因表达特征匹配的分子结构是一个新兴的概念.
研究的目的:
- 开发一种基于自编码器的生成模型,BiCEV,用于新的分子设计.
- 利用基因表达数据创造新的分子结构.
- 评估模型在生成有效,独特和多样化的分子方面的表现.
主要方法:
- 建立了一个基于自编码器的生成模型 (BiCEV).
- 该模型使用基因表达数据作为输入进行训练.
- 模型验证涉及对基因敲击概况和协同药物对签名的测试.
主要成果:
- BiCEV产生了具有高有效性 (96%),独特性 (98%) 和内部多样性 (0.77) 的新分子.
- 设计的分子表现出一致的高集体质量.
- 基于共同目标的功能等效的相似性分析产生了混合的结果.
结论:
- BiCEV模型显示了作为药物发现的辅助工具的潜力.
- 建议进一步开发和体外验证.
- 该模型可以支持早期打击识别和领先优化.
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