Topologically and Geometrically Inspired Machine Learning for Drug Design

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Drug design is immensely time-consuming and expensive. The largest pharmaceutical companies annually spend billions on the research and development of new drugs. A key area of such research is the investigation of biologically active small molecules. Various computer-aided drug design methods have emerged to improve the efficiency of the search for these active small molecules. In particular, shape-based ligand methods, which use the three-dimensional shape of the ligands themselves for predicting activity, are an attractive approach to the task because the shape of a ligand is important for ligand-protein binding. The recent field of topological data analysis (TDA) uses tools from algebraic topology to analyse high-dimensional datasets with many promising recent results. Techniques from TDA are well suited to analysing the shape of point clouds which makes it a natural candidate for shape-based drug discovery. This project aims to take inspiration from computational topology and geometry to create novel shape-based methods for computer-aided drug design. This project will generate simplicial complexes from the point clouds of molecules which will be used to make topological and geometric features. These features will then be fed as inputs to a machine learning pipeline which will be designed to classify active and inactive molecules. Any machine learning pipeline that classifies molecular activity should be invariant under the three-dimensional special Euclidean group, SE(3), since molecular activity is unaffected by the translations and rotations of a molecule. Therefore, this project will take advantage of techniques from the new field of geometric deep learning to create an invariant or equivariant classification pipeline. Data fusion, combining geometric features with chemical descriptors, will also be investigated as a potential way to improve the predictive power of the classification method. The popular DUD-E and the more recent LIT-PCBA virtual screening benchmarking data sets will be used to validate the created methods.This research is aligned with the following EPSRC research areas: artificial intelligence technologies, geometry and topology, statistics and applied probability.This work will be done in collaboration with Oxford Drug Design.