Computer-aided Synthesis and Diversification of a Selective SQR Inhibitor

With the support of the Chemical Synthesis and Chemical Mechanism, Function, and Properties Programs in the Division of Chemistry, Professor Ryan Shenvi of The Scripps Research Institute is studying the chemical synthesis and modification of new anti-fungal and anti-malarial medicines based on siccanin, a substance produced by a mold called Helminthosporium siccans Drechsler. The strategy of synthetically altering a naturally-occurring compound to become a human medicine has become common since the beginning of the 20th century. However, many natural product molecules are very complex, and it is difficult to know what chemical structural changes to make and how to make them. Traditional “guess-and-check” synthetic investigations are common, but are costly in terms of labor and resources and can lead to undesired outcomes. The Shenvi laboratory is developing an integrated experimental and computational workflow towards synthetic strategies for complex molecules to avoid trial-and-error approaches. In the current project, they are adapting the anti-fungal compound siccanin to become a human therapeutic by predicting which chemical characteristics are needed and which chemical reactions are required to install desired properties. Aligned with these research goals, Prof. Shenvi and his research team are also continuing to advance their chemistry education game, Synthordle, which allows chemistry students to practice their growing know-how in a Wordle-like format using problems that range from easy (college-level) to advanced degree-levels of difficulty. Pattern recognition is fast but does not have the resolution to distinguish molecular features that lead to success or failure in a chemical reaction. Quantum mechanical calculations are slow, but can identify reactivity differences that are unclear to both computer-assisted synthesis planning (CASP) and human practitioners. To investigate a compromise, the Shenvi group is creating libraries of CASP intermediates that can be triaged by reaction prediction to build a general predictive platform for the synthesis of antifungal meroterpenoid analogs of siccanin that improve the molecular properties of the target. These activities are also providing training opportunities for graduate, undergraduate, and high school students in chemical synthesis, computational chemistry, and computer-assisted synthesis planning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.