Research Progress in Predicting Binding Affinity between Drug Molecules and Target Proteins

Computer Engineering And Application 计算机工程与应用 Predicting the binding affinity between drug molecules and target proteins contributes to understanding biological systems and aiding drug discovery. With the development of computational biology technology driven by biological big da... 研究药物分子与靶标蛋白结合亲和力有助于了解生物系统和辅助药物开发。随着生物大数据驱动下的计算生物学技术发展,药物分子与靶标蛋白结合亲和力研究策略从传统单一生物医学实验迈向综合计算技术辅助预测,为药物开发提供新技术新方法。鉴于药物分子与靶标蛋白结合亲和力研究的重要性,从传统生物实验方法和计算生物学方法两个维度对其研究进展进行综述,重点介绍了预测药物分子与靶标蛋白结合亲和力的分子计算模拟、传统机器学习和深度学习方法,并阐述了每种计算

Background:

Predicting the binding affinity between drug molecules and target proteins contributes to understanding biological systems and aiding drug discovery. With the development of computational biology technology driven by biological big data, the research strategy for drug-target binding affinity has moved from traditional single biomedical experiments to comprehensive computational technology-assisted prediction, providing new techniques and methods for drug development.

Methods:

This is a review article. The review covers research progress from two dimensions: traditional biological experimental methods and computational biology methods, with a focus on introducing molecular computational simulation, traditional machine learning, and deep learning methods for predicting drug-target binding affinity, and elaborates each calculation method (text incomplete).

Results:

The abstract does not provide explicit results; it primarily summarizes the research progress and highlights the importance of computational approaches in binding affinity prediction.

Data Summary:

No quantitative results or key statistics are reported in the abstract.

Conclusions:

The abstract does not present explicit conclusions; it states the significance of studying binding affinity and the transition from traditional experiments to computational technology-assisted prediction.

Practical Significance:

The research provides new techniques and methods for drug development by offering comprehensive computational technology-assisted prediction of drug-target binding affinity.