From Optibrium Ltd.Jul 2 2024 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’.
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.
Florylpicoxamid is a mimic of a macrocyclic natural product, UK-2A, originally identified through a stepwise deconstruction method that required thousands of synthetic analogues alongside in vitro and in planta assays. Using the QuanSA method, the binding metabolic form of FPX was successfully identified within five rounds of compound selection and model refinement, reducing the total number of required synthetic analogues by a factor of ten.
Purely ligand-based affinity prediction is challenging, with the presence of macrocycles compounding the complexity. We are excited to show how machine learning can build physically meaningful models for lead optimization and how Optibrium’s QuanSA method, using an active learning strategy, can be applied to real-world molecular design.
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