FragExplorer: GRID-Based Fragment Growing and Replacement

Abstract

Understanding which chemical modifications can be made to known ligands is a key aspect of structure-based drug design and one that was pioneered by the software GRID. We developed FragExplorer with the explicit aim of showing GRID users which fragments would best match the GRID molecular interaction fields in a protein binding site, given a bound ligand as a starting point. Users can grow ligands or replace existing moieties; the R-Group Exploration mode identifies all potential R-Groups and searches for replacements automatically; the Scaffold Exploration mode does the same for all potential scaffolds. For a ligand with three points of variation, R-Group Exploration will typically explore a chemical space of 10 16 potential molecules; including Scaffold Exploration increases this to 10 22 . FragExplorer was designed to be integrated within an interactive 3D Editor/Designer; therefore, the speed of computation was an important consideration; a typical fragment search takes 20 seconds. In a fragment reprediction test, FragExplorer demonstrates an overall fragment retrieval rate of 55%, increasing to 69% for smaller fragments. At a 90% substructural match, the retrieval rate increases to ∼80%. We also show how the approach could have been used to hop from olmesartan to azilsartan or to optimize a p38 MAP kinase lead to a compound that bears similarity to a known nanomolar inhibitor.

An Open-Source Molecular Builder and Free Energy Preparation Workflow

“…The physics-based molecular mechanics-generalized Born with surface area (MM-GBSA) was then used to provide a more accurate score. Further, more recent, examples include FragExplorer, 9 which aims to grow or replace fragments to optimise molecular interaction fields generated by the GRID software, 10 DeepFrag, 11 which predicts appropriate fragment additions using a deep convolutional neural network trained on thousands of known protein-ligand complexes, and DEVELOP, 12 which uses deep generative models to output 3D molecules conditional on provided phamacophoric features of the binding site. However, the employed approximate physics-or knowledgebased approaches to scoring the designs will limit to some extent their ability to predict and optimise binding affinity.…”

An Open-Source Molecular Builder and Free Energy Preparation Workflow

“…The physics-based molecular mechanics-generalized Born with surface area (MM-GBSA) was then used to provide a more accurate score. Further, more recent, examples include FragExplorer, 9 which aims to grow or replace fragments to optimise molecular interaction fields generated by the GRID software, 10 DeepFrag, 11 which predicts appropriate fragment additions using a deep convolutional neural network trained on thousands of known protein-ligand complexes, and DEVELOP, 12 which uses deep generative models to output 3D molecules conditional on provided phamacophoric features of the binding site. However, the employed approximate physics-or knowledgebased approaches to scoring the designs will limit to some extent their ability to predict and optimise binding affinity.…”

An open-source molecular builder and free energy preparation workflow

“…The physics-based molecular mechanics-generalised Born with surface area (MM-GBSA) was then used to provide a more accurate score. Further, more recent, examples include FragExplorer 9 , which aims to grow or replace fragments to optimise molecular interaction fields generated by the GRID software 10 , DeepFrag 11 , which predicts appropriate fragment additions using a deep convolutional neural network trained on thousands of known protein-ligand complexes, and DEVELOP 12 , which uses deep generative models to output 3D molecules conditional on provided phamacophoric features of the binding site. However, the employed approximate physics-or knowledge-based approaches to scoring the designs will limit to some extent their ability to predict and optimise binding affinity.…”