College of Pharmacy, University of Illinois, Chicago, IL 61820, USA.
Zamara Mariam, School of Interdisciplinary Engineering & Sciences (SINES), National University of Sciences & Technology (NUST), Islamabad 24090, Pakistan.
Int J Mol Sci. 2023 Jul 15;24(14):11488. doi: 10.3390/ijms241411488.
In modern drug discovery, the combination of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structure-activity relationships (SARs), are pivotal in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. Due to embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.
在现代药物发现中，化学信息学和定量构效关系（QSAR）建模的结合已经成为一种强大的联盟，使研究人员能够利用机器学习（ML）技术的巨大潜力进行预测性分子设计和分析。本文深入探讨了化学信息学的基本方面，阐明了化学数据的复杂性质和分子描述符在揭示潜在分子性质方面的关键作用。分子描述符，包括 2D 指纹和拓扑指数，与结构-活性关系（SAR）一起，是解锁小分子药物发现途径的关键。本文讨论了开发强大的 ML-QSAR 模型的技术复杂性，包括特征选择、模型验证和性能评估。文中展示了各种 ML 算法，如回归分析和支持向量机，它们能够预测和理解分子结构与生物活性之间的关系。本文为研究人员提供了一个全面的指南，了解化学信息学、QSAR 和 ML 之间的协同作用。通过采用这些前沿技术，预测性分子分析有望加速药物科学中新型治疗剂的发现。
