Platforms in Computation/Data Science, Chemistry, and Biology for Pursuits in Chemical Biology & Drug Discovery
Infectious diseases are responsible for millions of new cases and deaths per year. The continued spread of drug resistance, both in terms of geography and extent of resistance to approved therapies, represents a global health pandemic. To address this issue, we have formulated and attempted to answer key questions in computation/data science, chemical biology, and drug discovery:
- Can target-based approaches still be viable in an era of high-throughput whole-cell phenotypic screening?
- Can existing big data be leveraged to computationally predict new anti-infective chemotypes and evolve their properties?
- Can we better understand drug fate within the targeted cell?
Through our efforts to address these questions, we have developed and subsequently deployed platforms in machine learning/data science, chemistry, and mechanistic biology. This interdisciplinary approach has led us to: a) construct, validate, and apply machine learning models as engines for molecular discovery and optimization, b) discover novel anti-infective small molecules and evolve them to useful chemical probes of biology and preclinical drug candidates of translational significance, c) validate novel drug targets, and d) quantitatively discern how a drug and its intracellularly-derived metabolites accumulate within the targeted microbe and/or mammalian cell and tie this knowledge to drug efficacy, mechanism of action, and/or mechanism of resistance. This seminar will discuss recent advances in each of these realms while also disclosing the extension of our intracellular drug accumulation/metabolism platform to oncology.
Hosted by Professor Lawrence Williams