DEVELOPMENT AND VALIDATION OF MACHINE LEARNING METHODS AND TOOLS TO SUPPORT VIRTUAL SCREENING OF ULTRA LARGE CHEMICAL LIBRARIES AND ARTIFICIAL INTELLIGENCE

In recent years there has been an explosion in the size of readily accessible chemical space with both purchasable commercial catalogs and high throughput screening libraries spanning billions of unique small molecules. Leveraging the improved diversity of chemicals and the large number of new biological activity datapoints in tandem with virtual screening methods has been shown to be an effective way to improve the success of the early, hit-identification stages in drug discovery. However, how to effectively navigate and utilize these new and vast datasets in combination with computational tools is still an open problem. Many existing computational tools face challenges when it comes to scaling effectively to handle billions of chemicals. Further, there is a lack of existing best practices and rigorous benchmarks that make assessing the performance of different approaches difficult. Yet, even when the latter two issues are resolved, still a substantial portion of relevant data that could now benefit from these new approaches will remain inaccessible to the public/academic researchers. The limitation of data will also slow down progress in effectively developing better solutions, placing a burden on the whole process. Here we outline the development of new computational workflows for both structure-based and ligand-based virtual screening, accelerated with artificial intelligence to handle continuously growing volumes of data efficiently. We show these methods obtained superior performance compared to brute force or existing approaches, as capable of locating chemical hits with experimentally confirmed desired properties. Further, I outline an open-source data generation framework, alongside a new database, to easily share novel, large scale chemical activity datasets with the community. This new database will be released with a series of new benchmarks and validation approaches, along with new open-source software packages to establish a reproducible and easily comparable assessment of virtual screening performance. In parallel, I use these datasets and open-source framework in a proof-of-concept application to show their value in a drug discovery setting. Overall, this work aims to lay the foundations required to promote development and identification of improved virtual screening methods in the era of big data.