在药物化学中,人工智能增强的R组探索
Hongtao Zhao1, Karolina Kwapień1, Eva Nittinger1
1Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, Gothenburg 43183, Sweden.
Journal of chemical information and modeling
|February 17, 2025
概括
本研究提出了一种增强的Free-Wilson定量结构-活动关系 (QSAR) 模型,用于高效的R组探索. 该模型通过考虑以原子为中心的特征和区域化学,准确地预测化学性质,有助于药物发现.
科学领域:
- 药用化学 医学化学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 探索广的化学空间寻找候选药物是一项挑战.
- 生成型人工智能和构建块提供了新的可能性,但需要有效的探索方法.
- 定量结构-活动关系 (QSAR) 模型对于预测分子性质至关重要.
研究的目的:
- 开发一个增强的Free-Wilson QSAR模型,用于高效的R组勘探.
- 通过结合以原子为中心的药法特征来提高QSAR模型的准确性.
- 为了能够在QSAR建模中区分R组区域异构体.
主要方法:
- 开发了一个增强的自由威尔逊QSAR模型.
- 嵌入式R组使用以原子为中心的药理特征.
- 考虑到原子位置来区分区域异构体.
- 在12个公共数据集中验证了模型.
主要成果:
- 在多个数据集中实现了良好的和一致的预测性.
- 证明模型能够处理R组变异,包括区域异构体.
- 成功地应用了Free-Wilson分析和R组探索的模型.
结论:
- 增强的Free-Wilson QSAR模型有效地解决了R组勘探挑战.
- 该模型提供了一个强大的工具,用于导航未知的化学空间.
- 将其集成到一个开源程序中,有助于在药物发现中得到更广泛的应用.
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