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RM-GPT:通过LocalRNN和RealFormer增强分子GPT模型的全面生成能力

Wenfeng Fan¹, Yue He¹, Fei Zhu¹

¹School of Computer Science and Technology, Soochow University, Suzhou, 215006, China.

Artificial intelligence in medicine

|March 29, 2024

概括

人工智能辅助的新药设计为药物发现提供了具有成本效益的替代方案. 一个新的循环分子生成预训练变压器 (RM-GPT) 模型使得精确的,有条件的分子生成具有所需的属性.

科学领域:

*计算化学和化学信息学.
* 药物发现中的人工智能.
* 分子设计的生成模型.

背景情况:

*传统的药物发现方法越来越昂贵.
* 人工智能 (AI) 正在成为一个可行的替代方案.
* 现有的生成模型与可控制的,有条件的分子生成作斗争.

研究的目的:

* 开发一种人工智能模型,用于可控制的新药设计.
* 为了使具有特定性质和支架的分子产生.
* 改进条件分子生成中的最先进 (SOTA) 方法.

主要方法:

* 介绍了循环分子生成预训练变压器 (RM-GPT) 模型.
*将LocalRNN和剩余注意层变压器集成到GPT架构中.
*利用多头注意力机制进行并行处理和局部信息提取.

主要成果:

*RM-GPT有效地提取本地结构信息并连接注意力块.
* 该模型证明了药物样分子的稳定和精确生成.
*在条件生成任务中,RM-GPT的性能优于现有的SOTA方法.

结论:

*RM-GPT为药物发现中的条件分子生成提供了强大的解决方案.
*模型的架构增强了对de novo设计过程的控制.
*这种人工智能方法有望加速新药候选药物的识别.

关键词:

在新的药物设计中.生成性的预培训模型.分子生成分子生成经常性的神经网络.剩余注意力机制剩余注意力机制变压器变压器变压器

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