Topic Models with Relational Features for Drug Design

Abstract

To date, ILP models in drug design have largely focussed on models in first-order logic that relate two- or three-dimensional molecular structure of a potential drug (a ligand) to its activity (for example, inhibition of some protein). In modelling terms: (a) the models have largely been logic-based (although there have been some attempts at probabilistic models); (b) the models have been mostly of a discriminatory nature (they have been mainly used for classification tasks); and (c) data for concepts to be learned are usually provided explicitly: “hidden” or latent concept learning is rare. Each of these aspects imposes certain limitations on the use of such models for drug design. Here, we propose the use of “topic models”—correctly, hierarchical Bayesian models—as a general and powerful modelling technique for drug design. Specifically, we use the feature-construction cabilities of a general-purpose ILP system to incorporate complex relational information into topic models for drug-like molecules. Our main interest in this paper is to describe computational tools to assist the discovery of drugs for malaria. To this end, we describe the construction of topic models using the GlaxoSmithKline Tres Cantos Antimalarial TCAMS dataset. This consists of about 13,000 inhibitors of the 3D7 strain of P. falciparum in human erythrocytes, obtained by screening of approximately 2 million compounds. We investigate the discrimination of molecules into groups (for example, “more active” and “less active”). For this task, we present evidence that suggests that when it is important to maximise the detection of molecules with high activity (“hits”), topic-based classifiers may be better than those that operate directly on the feature-space representation of the molecules. Besides the applicability for modelling anti-malarials, an obvious utility of topic-modelling as a technique of reducing the dimensionality of ILP-constructed feature spaces is also apparent.