Abstract | About the presenter | Request Recording

Date

Date: Thursday 27th February 2025
Time: 2pm GMT / 3pm CET / 9am ET
Duration: 45 minutes
Format: Webinar

Abstract

Bioisostere replacement is a powerful and popular tool used to optimize the potency and selectivity of candidate molecules in drug discovery. Selecting the right bioisosteres to invest resources in for synthesis and subsequent optimization is key to an efficient drug discovery project. In this retrospective study, we used human aldose reductase inhibitors to demonstrate an active learning workflow that prioritizes molecules from a large pool of bioisostere replacements generated by Spark¹. This workflow combines two rigorous computational approaches: 3D-quantitative structure activity relationships (3D-QSAR) with shape and electrostatic descriptors², and free energy perturbation (FEP) for binding free energy calculations in Flare¹. This workflow can rapidly locate the strongest-binding bioisosteric replacements with a relatively modest computational cost (a total of only 16% of the candidate pool was processed with FEP requiring 20% or even less GPU hours than if FEP were to include all candidates). The ROC-AUC for selection of known actives in 80 top-ranked candidates improved to 0.88 from 0.64, and the top picks were enriched with highly potent ALR2 inhibitors, including the well-known clinical candidate Zopolrestat developed by Pfizer^3,4.

Figure 1: Graphical overview of the active learning workflow showing the starter molecule (PF-cmp126 with the region selected for R-group replacement highlighted) and one of the most potent lead molecules (Zopolrestat) identified by this workflow. 2D representation, 3D molecular electrostatic potential and field points of the molecules are shown (colour code: Red for positive, blue for negative, yellow for shape and orange for hydrophobic).

References

1. Flare™, Cresset®, Litlington, Cambridgeshire, UK, https://www.cresset-group.com/software/flare/; Spark^TM. https://www.cresset-group.com/software/spark/
2. Cheeseright, T., et al., Molecular field extrema as descriptors of biological activity: definition and validation. J Chem Inf Model., 2006, 46, 2, 665-6 DOI: 10.1021/ci050357s.
3. Mylari, B.L., et al., Novel, potent aldose reductase inhibitors: 3,4-dihydro-4-oxo-3-[[5-(trifluoromethyl)-2-benzothiazolyl] methyl]-1-phthalazineacetic acid (zopolrestat) and congeners. J Med Chem., 1991, 34, 1, 108-1 DOI: 10.1021/jm00105a018.
4. Mylari, B.L., et al., Potent, orally active aldose reductase inhibitors related to zopolrestat: surrogates for benzothiazole side chain. J Med Chem., 1992, 35, 3, 457-4 DOI: 10.1021/jm00081a006.

About the presenter

Venkata Ramaswamy
Senior Computational Chemistry Developer

Venkat completed his PhD as a Marie Skłodowska-Curie Fellow within the ITN ‘Translocation’ project at the University of Cagliari, Italy, in 2017. Subsequently, he held a couple of postdoctoral positions before joining Cresset. His professional experience encompasses the application of various molecular modelling and structural bioinformatics techniques to investigate protein structure-function-dynamics relationship and protein-ligand interactions, which are pivotal for early-stage drug design and discovery. In his current role as a Senior Computational Chemistry Developer at Cresset, Venkat is responsible for developing and evaluating new computational methods for Cresset’s ligand-based and structure-based software solutions.