Drug-like compounds are most of the time denied approval and use owing to the
unexpected clinical side effects and cross-reactivity observed during clinical trials. These …

Highlights•We review machine learning methods/tools relevant to ligand-based virtual
screening.•Machine learning methods classify compounds and predict new active …

Shallow machine learning methods have been applied to chemoinformatics problems with
some success. As more data becomes available and more complex problems are tackled …

Computational methods that operate on three-dimensional molecular structure have the
potential to solve important questions in biology and chemistry. In particular, deep neural …

Motivation: Predicting interactions between small molecules and proteins is a crucial step to
decipher many biological processes, and plays a critical role in drug discovery. When no …

Introduction: Support vector machines (SVMs) are supervised machine learning algorithms
for binary class label prediction and regression-based prediction of property values. In …

Being able to predict the course of arbitrary chemical reactions is essential to the theory and
applications of organic chemistry. Approaches to the reaction prediction problems can be …

Proposing reasonable mechanisms and predicting the course of chemical reactions is
important to the practice of organic chemistry. Approaches to reaction prediction have …

This chapter reviews the use of molecular fingerprints for chemical similarity searching. The
fingerprints encode the presence of 2D substructural fragments in a molecule, and the …

Graph-structured data are an integral part of many application domains, including
chemoinformatics, computational biology, neuroimaging, and social network analysis. Over …