一个基于图形的机器学习框架,通过拓指数预测抗病毒药物的物理化学性质
Irfan Haider1, Mingchu Li1,2, Muhammad Kamran Jamil3
1School of Software, Dalian University of Technology, Dalian 116024, China.
Journal of chemical information and modeling
|October 20, 2025
概括
这项研究引入了一种两阶段的机器学习模型,用于预测抗病毒药物的特性. 该框架使用从分子结构中获得的拓指数准确估计物理化学性质.
科学领域:
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
- 机器学习在药物发现中的作用
背景情况:
- 对物理化学性质的准确预测对于抗病毒药物开发至关重要.
- 定量结构-属性关系 (QSPR) 建模提供了一个计算方法来预测药物特征.
- 对于各种抗病毒化合物库,现有的方法可能缺乏准确性或可扩展性.
研究的目的:
- 开发和验证一种新的两阶段机器学习框架,用于预测抗病毒药物的物理化学特性.
- 为关键药物描述符建立强大的定量结构与属性关系 (QSPR) 模型.
- 评估框架的预测性能和药物设计的可扩展性.
主要方法:
- 利用了59种抗病毒化合物的数据集,并使用基于SMILES的分子描述符.
- 实施了两阶段的机器学习方法:第一阶段预测了六个拓指数 (例如,兰迪克,萨格勒布指数).
- 第二阶段采用了表现最好的第一阶段模型来预测六种物理化学性质 (例如分子量,极化性).
主要成果:
- 该框架实现了高预测准确度,分子量为0.9950的R平方值,极化值为0.9891.
- 在拓指数和物理化学性质之间观察到强烈的相关性,特别是兰迪克指数 (R=0.9969).
- 开发的QSPR模型显示出显著的预测能力和强大的物业间关系.
结论:
- 拟议的两阶段机器学习框架为基于QSPR的抗病毒药物特性预测提供了准确,可解释和可扩展的解决方案.
- 这种方法促进了有效的药物发现和开发,因为它可以快速估计属性.
- 该研究强调了拓指数和机器学习在预测复杂分子特征方面的实用性.
相关概念视频
Structure-Activity Relationships and Drug Design
1.7K
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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...
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...
1.7K
Predicting Molecular Geometry
44.9K
VSEPR Theory for Determination of Electron Pair Geometries
44.9K
Factors Affecting Drug Biotransformation: Physicochemical and Chemical Properties of Drugs
704
A drug's physicochemical properties fundamentally influence its metabolism. For instance, a drug's molecular size and shape critically determine its interaction with enzymes and transporters — larger drugs may face difficulty reaching enzyme active sites, altering their metabolic pathways. The pKa of a drug, which establishes its ionization state, can impact its solubility and absorption, thereby influencing metabolism.
The drug's acidity or basicity is essential in...
The drug's acidity or basicity is essential in...
704
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
240
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and 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...
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...
240
Model Approaches for Pharmacokinetic Data: Physiological Models
246
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
246
Quantitative Aspects of Drug-Receptor Interaction
1.7K
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
1.7K


