Scaffold-Based Multi-Objective Drug Candidate Optimization

TL;DR

This paper introduces ScaMARS, a scaffold-based graph MCMC framework that efficiently generates drug-like molecules with optimized properties, outperforming traditional methods in diversity and success rate.

Contribution

The paper presents a novel scaffold-focused graph MCMC method capable of multi-property optimization and self-training, improving drug candidate generation over existing approaches.

Findings

Diversity score of 84.6% achieved by ScaMARS

Success rate of 99.5% in property optimization

Enhanced adaptability of MPO in drug design

Abstract

In therapeutic design, balancing various physiochemical properties is crucial for molecule development, similar to how Multiparameter Optimization (MPO) evaluates multiple variables to meet a primary goal. While many molecular features can now be predicted using \textit{in silico} methods, aiding early drug development, the vast data generated from high throughput virtual screening challenges the practicality of traditional MPO approaches. Addressing this, we introduce a scaffold focused graph-based Markov chain Monte Carlo framework (ScaMARS) built to generate molecules with optimal properties. This innovative framework is capable of self-training and handling a wider array of properties, sampling different chemical spaces according to the starting scaffold. The benchmark analysis on several properties shows that ScaMARS has a diversity score of 84.6\% and has a much higher success rate of 99.5\% compared to conditional models. The integration of new features into MPO significantly enhances its adaptability and effectiveness in therapeutic design, facilitating the discovery of candidates that efficiently optimize multiple properties.