通过机器学习重复开发QSAR建模的新方法:关于药物分布到每个组织的案例研究.
Koichi Handa1, Saki Yoshimura1, Michiharu Kageyama1
1Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan.
一种新的定量结构-活性关系 (QSAR) 方法使用预测数据来改进来自不完整数据集的组织-血分区系数 (Kp) 预测. 这种人工智能方法通过更好地估计组织中的药物分布来增强药物发现.
科学领域:
- 计算化学和化学信息学
- 药理动力学和药物代谢的药理动力学
- 人工智能在药物发现中的作用
背景情况:
- 药物发现依赖于对化合物属性的准确预测.
- 稀疏和不完整的数据集对传统的定量结构-活动关系 (QSAR) 模型构成挑战.
- 组织与血分区系数 (Kp) 对于理解药物分布和药理动力学建模至关重要.
研究的目的:
- 开发一种新的QSAR方法,使用不完整的数据集精确预测Kp值.
- 通过结合预测的解释变量来优化数据处理.
- 为了应对在各种组织中预测Kp时的小型和稀疏数据的挑战.
主要方法:
- 开发了一个两阶段随机森林 (RF) 模型来预测Kp值.
- 第一个射频模型使用体外参数预测了缺失的Kp值.
- 第二个RF模型结合了体外参数和来自其他组织的Kp值,以预测特定于组织的Kp值.
主要成果:
- 拟议的QSAR方法在Kp预测准确性方面显著优于传统的射频和传递信息的神经网络.
- 对脂肪组织,大脑,脏,肝脏和皮肤的预测准确度有显著的改善.
- 该研究通过评估解释变量的所有组合,揭示了新的组织间关系.
结论:
- 一个基于射频的QSAR模型成功地开发出来,用于从不完整的数据中预测Kp值.
- 该方法证明了跨组织Kp信息的实用性,用于增强特定组织的预测.
- 这种方法对各种缺乏数据的实验生物学问题具有前景.
更多相关视频
09:58Spatial Quantification of Drugs in Pulmonary Tuberculosis Lesions by Laser Capture Microdissection Liquid Chromatography Mass Spectrometry LCM-LC/MS
Published on: April 18, 2018
10:29Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
Published on: May 9, 2025
相关概念视频
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Model Approaches for Pharmacokinetic Data: Compartment Models
Two primary types of compartment models are recognized: mammillary and catenary. The more...
Pharmacokinetic Models: Comparison and Selection Criterion
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Model Approaches for Pharmacokinetic Data: Physiological Models
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Structure-Activity Relationships and Drug Design
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
