Deep neural networks for pose validation, affinity prediction, and input attribution

The search for drug molecules which bind strongly to specific proteins is an integral part of the drug discovery process. To this end, virtual screening algorithms which aim to screen a large number of potential binders in silico have been developed. These use scoring functions to assess the probability that a computationally predicted binding pose is correct, and to predict the binding affinity. More recently, research has turned to deep learning-based scoring functions which use binding data to build a model of binding behaviour; this is the main subject of this thesis.
The first chapter is an introduction to the concepts and literature which are relevant to the subsequent chapters. This includes fragment-based drug discovery, virtual screening, and machine learning methods in virtual screening. It also touches on the problem of input attribution, where importance is assigned to the atoms or bonds in the input to deep learning- based scoring functions, as well as the problem of machine learning algorithms learning to classify based on dataset biases rather than learning the physical interactions which govern protein-ligand binding.
A publication on convolutional neural networks for virtual screening makes up most of the second chapter. The problem of learning training set biases rather than physical interactions is explored using several experiments, and a method of dataset augmentation to combat this effect is proposed. A carefully curated validation set, constructed separately from any training data, is used to show increased use of protein information in classification decisions; input attribution is used on several case studies to show the same.
The third chapter concerns the design and engineering decisions involved in PointVS. This is a software package for rapid prototyping and testing of graph neural networks for pose classification and affinity prediction. It includes a variety of scripts for auxiliary tasks such as dataset generation, input attribution visualisation and logging, and has been used by another member of the Oxford Protein Informatics Group for a paper which is briefly described.
PointVS forms the basis of another publication, under review at the time of writing; this makes up the fourth chapter. In collaboration with another student, graph neural networks are used for pose classification and affinity prediction, with training and testing sets set up carefully to avoid information leakage. PointVS is compared to several other methods, achieving competitive performance. Attribution scores from PointVS are converted to protein hotspot maps, which are used as input into a generative model for fragment elaboration. This out-performs the results using standard physics-derived hotspot maps, which is evidence that the graph neural networks can pick out important protein-ligand interactions.
Finally, we take a more macroscopic view of the field of machine learning- based scoring functions. We conclude that while promising, there are several obstacles that must be overcome in order to train models which truly ‘understand’ the universe of protein-ligand binding. We propose that an input attribution ground truth test set as an area for possible further study, and identify a possible method to generate one. We conclude that many reported improvements of machine learning-based scoring functions over their physics-inspired predecessors are over-estimated, and that a more dynamic view of binding which takes water into account explicitly is required.

Keywords: data augmentation
molecule
graph
deep learning
chemistry
biology
molecular
neural networks
gnn equivariant
Advisor: Deane, C
Von Delft, F