基于量子力学和机器学习,预测I期和II期新陈代谢的路径
Mario Öeren1, Peter A Hunt1, Charlotte E Wharrick1
1Optibrium Limited, Cambridge Innovation Park, Cambridge, UK.
Xenobiotica; the fate of foreign compounds in biological systems
|November 15, 2023
概括
预测药物代谢对于药物开发至关重要. 这项研究引入了一种新的模型,可以准确地识别关键的药物代谢酶,并预测代谢路径,提高候选药物的成功率.
科学领域:
- 药物代谢和药理动力学
- 计算化学和化学信息学
- 药物化学和药物发现
背景情况:
- 药物代谢对药物的有效性和安全性产生重大影响,意想不到的代谢途径会导致晚期失败或药物停用.
- 准确预测药物代谢对于早期的研究和开发来减轻风险至关重要.
- 关键的药物代谢酶包括细胞染色体P450,氧化酶,含有黄素的单氧化酶,UDP-glucuronosyltransferases和Sulfotransferases.
研究的目的:
- 开发和验证一种模型 ("WhichEnzyme") 以预测负责药物代谢的酶家族.
- 将"WhichEnzyme"与现有的区域选择性模型和"WhichP450"模型集成,用于全面的代谢途径预测.
- 提高在药物发现中预测体内代谢物概况的准确性和效率.
主要方法:
- 开发"WhichEnzyme"模型以确定可能的药物代谢酶家族.
- 整合机械的区域选择性模型 (使用量子力学和机器学习) 对细胞染色体P450,氧化酶,含有黄素的单氧化酶,UDP-glucuronosyltransferases和硫转移酶.
- 创建基于模型输出的启发式,以预测主要的代谢路径和代谢物.
主要成果:
- "WhichEnzyme"模型准确地预测了参与药物代谢的酶家族.
- 组合模型使用量子力学模拟和机器学习准确地预测了新陈代谢的部位和由此产生的代谢物.
- 与现有方法相比,综合方法在识别实验报告的代谢物方面表现出高灵敏度,在预测体内代谢物概况方面具有更高的精度.
结论:
- 开发的计算模型为预测药物代谢路径和代谢物提供了一个强大的框架.
- 这种方法有助于早期识别潜在的代谢负担,减少药物开发风险.
- 增强的预测准确性支持药物发现和开发管道中的知情决策.
关键词:
阿尔德氧化酶的使用方法在UDP-glucuronosyl转移酶中.细胞染色体P450的使用.含有黄素的单氧基因酶.代谢 代谢 代谢 代谢路径路径路径路径路径第一个阶段I阶段.第二阶段第二阶段.路线路线路的路线.硫酸转移酶的使用方法更多相关视频
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