“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 optimisation and how Optibrium’s QuanSA method, using an active learning strategy, can be applied to real-world molecular design.”
Congratulations to Ann Cleves Jain, sharing her excitement above, and all the researchers involved in our new Open Access publication, now out in the Journal of Computer-Aided Molecular Design.
They demonstrate the application of QuanSA for accelerated lead optimisation. Using an active learning approach, QuanSA enabled the successful, efficient identification of a mimic of a macrocyclic natural product.
With QuanSA, far fewer synthetic steps were required, showing its potential to dramatically reduce costs and increase efficiency in macrocycle discovery projects.
Learn more in our press release: https://lnkd.in/eRwz347c
Or by reading the full paper: https://lnkd.in/eimkbspS
#Macrocycles #CropProtection #3DDesign #ActiveLearning #MolecularDesign #OpenAccess
Broker at Brown Harris Stevens
4wawesome 😄😄