相关实验视频

Updated: Jul 23, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

在药物开发中预测心脏毒性:一种深度学习方法

Kaifeng Liu¹, Huizi Cui¹, Xiangyu Yu¹

¹Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, Edmond H. Fischer Signal Transduction Laboratory, School of Life Sciences, Jilin University, Changchun, 130012, China.

Journal of pharmaceutical analysis

|September 22, 2025

概括

计算模型使用机器学习准确预测药物心脏毒性,改善药物安全性评估. 这些方法提高了药物开发的效率并降低了药物开发成本.

科学领域:

计算化学是一种计算化学.
药物发现药物发现
毒理学毒理学毒理学

背景情况:

心脏毒性是药物开发中的重大风险,通常与hERG通道有关.
传统的心脏毒性检测是昂贵和耗时的.
计算式虚拟选提供了一个更有效的替代方案.

研究的目的:

开发准确和高效的计算模型来预测复合性心脏毒性.
通过机器学习和深度学习技术改进药物安全性评估.

主要方法:

使用分子指纹和描述符与机器学习 (高斯 NB,RF,SVM,KNN,XGBoost) 和深度学习 (变压器) 算法.
使用精度 (ACC) 和曲线下的面积 (AUC) 评估模型性能.
为了特征解释性,使用了夏普利添加式解释 (SHAP).

主要成果:

最好的机器学习模型 (XGBoost Morgan) 实现了0.84.8的ACC.
最好的深度学习模型 (Transformer_Morgan) 实现了0.85.5的ACC.
转换器_摩根模型在独立验证集上获得了0.93的AUC,超过了现有的工具.
SHAP分析确定了与心脏毒性相关的关键化学特征,例如环和含的组.

结论:

机器学习和深度学习模型为心脏毒性提供了高度准确的预测.
这些计算方法为药物安全性评估提供了可靠和可解释的方法.
该研究促进了高效的药物开发,降低了成本,并提高了新药候选药物的安全性.

关键词:

心脏毒性有关心脏的毒性.深度学习是一种深度学习.药物开发药物开发人类以太基因相关的基因通道分子指纹的分子指纹.

更多相关视频

Technical Applications of Microelectrode Array and Patch Clamp Recordings on Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes

Technical Applications of Microelectrode Array and Patch Clamp Recordings on Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes

Published on: August 4, 2022

Measurement of Heart Contractility in Isolated Adult Human Primary Cardiomyocytes

Measurement of Heart Contractility in Isolated Adult Human Primary Cardiomyocytes

Published on: August 9, 2022

相关概念视频

Drug Discovery: Overview

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

Pharmacogenomics: Identification of New Drug Targets

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...