Automated Molecular Design in BRADSHAW, Applied to the Optimization of ERAP1 Inhibitors

Generative design and machine learning are increasingly
prevalent
in medicinal chemistry. To pilot the comprehensive use of automated
molecular design on a project, the BRADSHAW platform was used to optimize
a series of inhibitors of Endoplasmic Reticulum Aminopeptidase 1 (ERAP1),
an emerging target in cancer immunotherapy and autoimmune diseases.
Through four consecutive iterations applying in silico molecular generation,
property prediction and filtering, we conducted a multiparameter optimization
of potency, physicochemical properties and pharmacokinetics. Continuous
refinement of Machine Learning (ML) models led to improved scoring
accuracy and compound quality, culminating in identification of in
vitro and in vivo tool molecules. We also discuss our reflections
on the pilot and integration of automated design into medicinal chemistry
projects, including observations of the human factors resulting from
increased use of computational design, and recommendations for future
projects.