Inverse Mechanistic Discovery: Using Computation to Predict and Design Unconventional Organocatalytic Reaction Pathways

Create an inverse discovery computational platform that first selects interesting but underutilized substrates and maps their entire potential energy surfaces using high-level quantum chemistry to identify all possible bond-forming and bond-breaking events, including kinetically inaccessible but thermodynamically favorable pathways. Then, use catalyst design algorithms to propose virtual organocatalysts tailored to lower activation barriers for these unconventional pathways. This approach shifts computational chemistry from reaction optimization to proactive discovery, enabling systematic design of new reaction types and expanding the synthetic toolbox beyond known transformations.

References:

1. Catalyst design within asymmetric organocatalysis. Iñigo Iribarren, Marianne Rica Garcia, Cristina Trujillo (2022). WIREs Computational Molecular Science.