相关实验视频

Updated: Jul 20, 2025

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

一个模型不可知的框架,以增强基于知识图的药物组合预测与药物相互作用数据和监督的对比学习

Jeonghyeon Gu¹, Dongmin Bang^2,3, Jungseob Yi¹

¹Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea.

Briefings in bioinformatics

|August 6, 2023

概括

这项研究引入了一个新的框架,使用药物相互作用数据和监督对比学习来改善有效药物组合的预测,克服以前方法的局限性.

科学领域:

生物医学信息学是生物医学信息学.
计算药理学是一种计算药理学.
机器学习在药物发现中的作用

背景情况:

组合疗法提供了重要的医学进步,但识别有效的药物配对是具有挑战性的.
生物医学知识图 (KGs) 显示出预测药物组合的前景,但缺乏可靠的机器学习负样本.
现有的方法与潜在药物组合的广搜索空间作斗争.

研究的目的:

为预测有效的药物组合开发一种新的模型不可知框架.
为了应对生物医学基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基基
通过监督对比学习提高药物组合预测模型的性能.

主要方法:

利用现有的药物相互作用 (DDI) 数据作为可靠的负数据集.
使用监督对比学习 (SCL) 来改进药物嵌入载体.
在生物医学KG上使用各种网络嵌入算法 (例如随机步行,图形神经网络).

主要成果:

与基线方法相比,拟议的框架显著改善了绩效指标.
嵌入空间可视化展示了学习药物表示的有效性.
案例研究验证了开发的方法的实际实用性.

结论:

新的框架有效地利用DDI数据和SCL进行增强的药物组合预测.
这种方法为更有效地识别有效药物组合提供了一个有希望的策略.
这些发现强调了将DDI数据和SCL集成到药物发现管道中的潜力.

关键词:

药物组合是药物组合.药物药物相互作用图表神经网络的神经网络知识图表知识图表随机步行随机步行随机步行监督的对比学习学习.

更多相关视频

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

Diagonal Method to Measure Synergy Among Any Number of Drugs

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

相关概念视频

Combined Effects of Drugs: Synergism

Combined Effects of Drugs: Synergism

Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
Such synergistic combinations...

Pharmacokinetic Models: Comparison and Selection Criterion

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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.

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the 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...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...

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...