Optibrium, a leading developer of software and AI solutions for molecular design today announced the publication of a peer-reviewed study in Journal of Computer-Aided Molecular Design, ‘From UK-2A to florylpicoxamid: Active learning to identify a mimic of a macrocyclic natural product’. The paper demonstrates the successful application of the QuanSA (Quantitative Surface-field Analysis) method, part of Optibrium’s BioPharmics platform for 3D molecular design, to accelerate the lead optimization of a complex macrocyclic natural product during agrochemical development. By significantly reducing the number of synthetic steps required during optimization, the study supports the commercial viability of complex macrocyclic compounds.
Optibrium’s QuanSA method uses an active learning approach that combines two types of molecular selection—the first identifies compounds predicted to be most active, and the second identifies compounds predicted to be most informative for lead optimization. The method has broad applications in lead optimization where scaffold replacements are needed, from agrochemical development to small molecule and macrocyclic ligand design and discovery. In the study together with a leading agriculture company, Optibrium explored how this approach could provide a more efficient route to finding new agrochemicals (e.
g., for crop protection) by reducing the number of compounds requiring synthesis. Florylpicoxamid (FPX) is a mimic of a macrocyclic natural pr.
