药物发现前沿的扩散模型:对生成小分子与治疗性的审查
Yiquan Wang1,2, Yahui Ma1, Yuhan Chang2
1Xinjiang Key Laboratory of Biological Resources and Genetic Engineering, College of Life Science and Technology, Xinjiang University, Urumqi 830049, China.
Biology
|December 30, 2025
概括
扩散模型通过设计小分子和治疗性来加速药物发现. 克服数据稀缺性和验证挑战是实现其在自动化治疗工程中的全部潜力的关键.
科学领域:
- 计算化学是一种计算化学.
- 生物技术是生物技术.
- 药物发现 药物发现
背景情况:
- 扩散模型是一种强大的生成人工智能框架.
- 传统的药物发现是缓慢而昂贵的.
- 人工智能正在改变治疗设计.
研究的目的:
- 对比小型分子和设计的扩散模型.
- 分析模式特定的适应和挑战.
- 突出了人工智能驱动的药物发现中的共同障碍.
主要方法:
- 对扩散模型应用的系统审查.
- 在不同的分子环境中对代性无化进行分析.
- 设计目标和验证策略的剖析.
主要成果:
- 小分子设计优于基于结构的配体生成,但面临合成性问题.
- 体设计侧重于功能序列和de novo结构,这些结构受到稳定性和免疫性挑战.
- 共同的挑战包括有限的数据,评分函数的准确性和实验验证.
结论:
- 弥合模式特定差距对于扩散模型至关重要.
- 集成到设计-构建-测试-学习 (DBTL) 平台是必不可少的.
- 扩散模型可以实现对新疗法的按需工程.
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