Physical model induction with QuanSA™: Affinity prediction that is synergistic with simulation-based methods
The QuanSA method To define a ‘pocket field’, an initial alignment of all training molecules is constructed and function parameters…
Rapid AI generation of optimised compound designs, guided by user interaction
Pairing AI with human expertise We present a novel AI compound optimisation system, designed to include human oversight as a…
Macrocyclic peptide optimisation: Integrating computational approaches with biophysical data
Introduction Using the integrated set of computational methods within the BioPharmics™ Platform, macrocycles can be effectively modelled for lead optimisation.…
Combining deep learning with multi-parameter optimisation to predict compounds with selective activity against a broadleaf weed species
Introduction The emergence of resistance and increased stringency of regulatory requirements have created a need for new agrochemicals. The long…
Predicting reactivity to drug metabolism: beyond CYPs
Introduction Predicting sites of metabolism (SoM) enable chemists to be more efficient in optimising the structure of new chemical entities…
N- and S-oxidation model of the flavin-containing monooxygenases
Introduction Existing computational models of drug metabolism are heavily focused on predicting oxidation by cytochrome P450 (CYP) enzymes, because of…
A novel scoring profile for the design of antibacterials active against gram-negative bacteria
Introduction The increasing occurrence of multidrug-resistant bacteria is one of the major global threats to human health. Design of new…
