Deep learning applications in structure-based drug discovery

Thesis

In recent years, machine learning and deep learning applications have permeated all fields of science thanks to rapid algorithmic advances and computer hardware developments. A very active area of research is the use of deep learning in structure-based drug design, where the goal is to design effective drugs against a pharmacological target of interest.

In this work, we explored the use of deep learning in the early stages of drug discovery. In particular, we focussed on structure-based virtual screening, binding affinity prediction, and de novo drug design.

First, we enabled docking with flexible residues within Gnina, a state-of-the- art docking software based on convolutional neural networks, and we performed a large-scale cross-docking study of such methodology, outlining its strengths and weaknesses.

Second, we extracted the convolutional neural network scoring function from the docking software into a standalone package for fast prototyping. With the new software at hand, we explored different annotations for supervised learning to improve the convolutional neural network scoring function for docking with flexible residues.

Third, we developed a novel scoring function for binding affinity prediction based on a successful deep learning architecture used to develop machine learning force fields.

Finally, we carefully evaluate a generative model for de novo design for the application in industrial drug discovery pipelines. We outline the weaknesses of the method and the problems with current evaluations of generative models.

Authors: Meli, R

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: drug design; structure based drug design; neural networks; convolutional neural networks; drug discovery; deep learning; sbdd; machine learning
• Subjects: Pharmaceutical chemistry; Computational chemistry; Ensemble learning (Machine learning); Supervised learning (Machine learning); Computational biology; Chemistry, Physical and theoretical; Structure-activity relationships (Biochemistry); QSAR (Biochemistry); Deep learning (Machine learning); Ligand binding (Biochemistry)