Scaffold-Induced Molecular Graph (SIMG): Effective Graph Sampling Methods for High-Throughput Computational Drug Discovery

TL;DR

This paper introduces Scaffold-Induced Molecular Graph (SIMG), a novel graph sampling method that leverages scaffold-based network design to improve high-throughput computational drug discovery by focusing on local chemical neighborhoods.

Contribution

It formalizes scaffold-based drug discovery into a network framework and demonstrates its utility using SARS-CoV-2 and JAK2 data for efficient chemical space exploration.

Findings

Effective scaffold-based network sampling improves chemical space navigation

Utilizes docking and assay data to validate the approach

Offers a potential solution to chemical space enumeration challenges

Abstract

Scaffold based drug discovery (SBDD) is a technique for drug discovery which pins chemical scaffolds as the framework of design. Scaffolds, or molecular frameworks, organize the design of compounds into local neighborhoods. We formalize scaffold based drug discovery into a network design. Utilizing docking data from SARS-CoV-2 virtual screening studies and JAK2 kinase assay data, we showcase how a scaffold based conception of chemical space is intuitive for design. Lastly, we highlight the utility of scaffold based networks for chemical space as a potential solution to the intractable enumeration problem of chemical space by working inductively on local neighborhoods.