Journal
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Volume 24, Issue 14, Pages -Publisher
MDPI
DOI: 10.3390/ijms241411488
Keywords
QSAR; QSPR; chemoinformatics; small molecules; AI; ML; molecular descriptors; biological activity; SAR; predictive modeling; computational validation
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In modern drug discovery, the combination of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has become a powerful alliance for predictive molecular design and analysis using machine learning techniques. This review explores the fundamental aspects of chemoinformatics and the crucial role of molecular descriptors in uncovering molecular properties. It also discusses the technical intricacies of developing robust ML-QSAR models and showcases various ML algorithms for predicting and understanding the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, highlighting the synergy between chemoinformatics, QSAR, and ML and its potential for expediting the discovery of novel therapeutic agents.
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.
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