通过药理学信息的生成模型加速发现生物活性连接体
Weixin Xie1, Jianhang Zhang2, Qin Xie2
1Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
TransPharmer是一个新的生成模型,通过将药指纹与变压器框架集成,创造结构上独特的药物候选者. 这种方法通过产生新型,强效的化合物,如PLK1抑制剂IIP0943.3,从而增强药物发现.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 深度生成模型推进药物发现,但往往缺乏结构性新性.
- 有限的结构新性限制了药物化学家的灵感.
研究的目的:
- 开发TransPharmer,一种具有增强结构新性的新分子生成生成模型.
- 将基于连接体的药指纹与基于GPT的框架集成.
主要方法:
- 开发了TransPharmer,这是一个结合药指纹和GPT框架的生成模型.
- 利用无条件的分布学习, de novo生成和脚手架编制.
- 通过对多巴胺受体D2 (DRD2) 和波罗样酶1 (PLK1) 的案例研究验证了有效性.
主要成果:
- 特朗普在分销学习,新一代和支架制作方面表现出卓越.
- 一个探索模式促进了脚手架跳跃,产生结构上截然不同的但相关的化合物.
- 在4种合成的PLK1抑制剂中,有3种表现出亚微分子活性;IIP0943实现了5.1nM强度,具有高的PLK1选择性.
结论:
- TransPharmer成功地产生了结构新和生物活性联结体.
- 该模型的独特探索模式可以增强用于药物发现的脚手架跳跃.
- TransPharmer代表了一种有前途的工具,用于识别新型治疗剂.
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