Big data

Process (computing)

Engineering

Pure mathematics

Biology

Field (mathematics)

Operating system

Data mining

Computer science

Drug discovery

Biochemical engineering

Bioinformatics

Mathematics

Pipeline (software)

Quantitative structure–activity relationship

Data science

Artificial intelligence

Programming language

Machine learning

Cheminformatics

Drug Discovery Today

Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field.

Machine learning in chemoinformatics and drug discovery