Pairwise Interaction Field Neural Networks For Drug Discovery

Automatically mapping small, drug-like molecules into their biological activity is an open problem in chemioinformatics. Numerous approaches to solve the problem have been attempted, which typically rely on different machine learning tools and, critically, depend on the how a molecule is represented (be it as a one-dimensional string, a two-dimensional graph, its three-dimensional structure, or a feature vector of some kind). In fact arguably the most critical bottleneck in the process is how to encode the molecule in a way that is both informative and can be dealt with by the machine learning algorithms downstream. Recently we have introduced an algorithm which entirely does away with this complex, error-prone and time-consuming encoding step by automatically finding an optimal code for a molecule represented as a twodimensional graph. In this report we introduce a model which we have recently developed (Neural Network Pairwise Interaction Fields) to extend this same approach to molecules represented as their three-dimensional structures. We benchmark the algorithm on a number of public data sets. While our tests confirm that three-dimensional representations are generally less informative than two-dimensional ones (possibly because the former are generally the result of a prediction process, and as such contain noise), the algorithm we introduce compares well with the state of the art in 3D-based prediction, in spite of not requiring any prior knowledge about the domain, or prior encoding of the molecule.