Scaffold-Based Multi-Objective Drug Candidate Optimization

• URL: http://arxiv.org/abs/2301.07175v2
• Date: Tue, 2 Jan 2024 12:49:36 GMT
• Title: Scaffold-Based Multi-Objective Drug Candidate Optimization
• Authors: Agustin Kruel, Andrew D. McNaughton, Neeraj Kumar
• Abstract summary: We introduce a scaffold focused graph-based Markov chain Monte Carlo framework to generate molecules with optimal properties. ScaMARS has a diversity score of 84.6% and has a much higher success rate of 99.5% compared to conditional models.
• Score: 9.53584200550524
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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