"DompeKeys":一组基于子结构的新型描述符,用于高效的化学空间绘图,机器学习模型的开发和结构解释,以及大型数据库的索引
Candida Manelfi1, Valerio Tazzari1, Filippo Lunghini1
1EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123, Napoli, Italy.
Journal of cheminformatics
|February 23, 2024
概括
一套新的化学描述符,DompeKeys (DK),有效地编码药物发现的分子结构. 这些描述符使数据库搜索更快,并改善了机器学习对化合物活性和代谢的预测.
科学领域:
- 化学信息学 化学信息学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 分子指纹对于化学信息学和药物设计至关重要,但生成速度很慢,并且可能有偏见.
- 对于大规模的药物发现应用,需要高效,可解释和可扩展的化学描述符.
研究的目的:
- 开发一套基于基底结构的新型化学描述符,DompeKeys (DK),以高效和可解释地表示化学化合物.
- 证明DK在各种化学信息任务中的实用性,包括化学空间绘图,亚结构搜索和虚拟选.
主要方法:
- 开发了DompeKeys (DK) 作为一系列精选的SMARTS字符串,代表各种复杂度级别的化学特征.
- 将DK集成到EXSCALATE药物发现平台中.
- 应用DK用于化学空间绘图,亚结构搜索,虚拟选和机器学习模型开发.
主要成果:
- DK描述器可以在没有漫长的生成程序的情况下准确检测结构碎片.
- DK促进了有效的化学空间绘图,亚结构搜索和虚拟选.
- 将DK纳入机器学习模型显著改善了化合物活性和代谢反应发生的预测.
结论:
- DompeKeys (DK) 提供了一种强大,可解释和可扩展的解决方案,用于药物发现中的化学结构.
- DK提高了各种化学信息学应用程序和机器学习模型的性能.
- 该DK协议是免费的,可以与现有的药物设计工具集成.
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