Structure Activity Relationships (SAR) and Pharmacophore Discovery Using Inductive Logic Programming (ILP)

Abstract

AbstractThe application of Inductive Logic Programming (ILP), a form of machine learning, to derive structure activity relationships (SAR) and to discover pharmacophores is reported. The ILP approach was initially applied to model 1D SARs in terms of the attributes of the molecules. Subsequently 2D ILP SARs were developed describing chemical connectivity. Finally ILP has been used to model 3D SARs in which the conformation of the pharmacophore can be described. ILP has advantages over many other widely used methods as it can reason with relations and hence discover chemical substructures and 3D features without these aspects having been explicitly encoded prior to learning. In particular, there is no requirement for a structural superposition. Additionally, the results of ILP provide chemical descriptions that can readily be understood by a medicinal chemist. In several trials, ILP‐based SARs have been shown to be significantly more accurate than widely‐used methods.