UCSF

With the growth of biological and chemical datasets and the development of novel computational techniques, applications of artificial intelligence (AI) and machine learning (ML) methods that leverage these datasets to assist experimentalists become more critical than ever. This dissertation presents an overview of commonly used AL/ML tools for molecular biology and introduces two novel tools, as well as details their specific use cases. In Chapter 1, I provide a review of traditional techniques and their machine learning counterparts for ligand- and structure-based drug discovery and protein structure elucidation and design. In Chapter 2, I introduce Metric Ion Classification (MIC), a method for determining the identity of experimentally identified waters and ions in biomolecular structures. MIC builds upon recent advancements in protein-ligand interface representations and metric learning techniques to introduce a novel classification scheme with extensive validation on a variety of experimental structures. In Chapter 3, we present Autoparty, a tool for AI-assisted human-in-the-loop molecule annotation designed to facilitate the manual assessment of virtual screening results. Autoparty uses the principles of active learning to direct chemists toward useful compounds and limit the amount of labor required when evaluating compounds. These applications do not attempt to replace existing techniques; rather, they act in service of scientists to accelerate both structure determination and drug discovery pipelines. This work broadly highlights the utility of these tools and others like them and encourages their adoption alongside classical approaches.