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基于机器学习的代化学相似性搜索以确定新型化学抑制剂

Prasannavenkatesh Durai¹, Sue Jung Lee², Jae Wook Lee²

¹Natural Product Informatics Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea.

Journal of cheminformatics

|September 23, 2023

概括

这项研究介绍了机器学习模型的代优化策略,以发现用于药物开发的新型化学支架. 该方法提高了准确性,并识别了具有改善结合亲和力的新MEK1抑制剂.

科学领域:

计算化学和化学信息学
药物发现和药物化学
在化学科学中的机器学习.

背景情况:

化学选中的机器学习显示出有希望的结果,但在新脚手架的准确性和新性方面存在困难.
现有的方法往往产生结构相似的化合物,限制了药物发现的创新.

研究的目的:

使用实验数据开发和验证化学结合相似性模型的代优化策略.
解决识别新型活性化学支架时的低准确度和高不确定性的局限性.
为了发现新的,结构上独特的抑制因子激活蛋白激酶激酶1 (MEK1) 的抑制剂.

主要方法:

演化化学结合相似性 (ECBS) 模型的代优化,结合实验验证数据.
测试各种数据更新和模型再培训方案,以有效地整合新的实验结果.
利用新的药物设计工具,根据已识别的抑制剂生成新的类似药物的分子.

主要成果:

一个微调的ECBS模型在预测活性化学支架方面表现出更好的准确性和覆盖范围.
鉴定具有亚微分子亲和度 (Kd 0.1-5.3 μM) 的新型MEK1抑制剂,与已知的抑制剂不同.
通过de novo设计生成额外的具有增强结合亲和度得分的药物样分子.

结论:

提出的代优化策略有效地提高了基于机器学习的化学选的准确性和新性.
该方法成功识别了新的MEK1抑制剂,并证明了优化结合亲缘关系的潜力.
这种方法为发现新型热门分子和推进药物开发管道提供了强大的工具.

关键词:

药物设计药物设计进化化学结合的相似性化学结合的相似性击中识别标识击中识别这是一种MEK抑制剂.虚拟选是一个虚拟的选.

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