Advancing Drug Design through Data Mining and QSAR Analysis

SCIENCE

Chemoinformatics is a well-established field that focuses on extracting, processing, and extrapolating meaningful data from chemical structures. With the rapid expansion of chemical 'big' data from High-Throughput Screening (HTS) and combinatorial synthesis, machine learning has become an essential tool for drug designers to mine chemical information from vast compound databases and design drugs with important biological properties. In this context, we reviewed multiple processing layers in the chemoinformatics pipeline and introduced commonly used machine learning models in drug discovery and Quantitative Structure-Activity Relationship (QSAR) analysis. In this paper, we present the fundamental principles and recent case studies that demonstrate the utility of machine learning techniques in chemoinformatics analyses. Additionally, we discuss the limitations and future directions to guide further development in this rapidly evolving field.

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