$\mathcal{G}$-SELC: Optimization by sequential elimination of level combinations using genetic algorithms and Gaussian processes

TL;DR

The paper introduces $\\mathcal{G}$-SELC, a novel optimization method combining genetic algorithms and Gaussian process modeling to efficiently identify promising compounds in drug discovery, outperforming previous approaches.

Contribution

It develops a new algorithm that integrates Gaussian process modeling with genetic algorithms to improve search efficiency in compound optimization tasks.

Findings

Successfully applied to test functions demonstrating improved efficiency.

Achieved better identification of optimal compounds in pharmaceutical data.

Outperformed previous methods like SELC in experimental evaluations.

Abstract

Identifying promising compounds from a vast collection of feasible compounds is an important and yet challenging problem in the pharmaceutical industry. An efficient solution to this problem will help reduce the expenditure at the early stages of drug discovery. In an attempt to solve this problem, Mandal, Wu and Johnson [Technometrics 48 (2006) 273--283] proposed the SELC algorithm. Although powerful, it fails to extract substantial information from the data to guide the search efficiently, as this methodology is not based on any statistical modeling. The proposed approach uses Gaussian Process (GP) modeling to improve upon SELC, and hence named $\mathcal{G}$-SELC. The performance of the proposed methodology is illustrated using four and five dimensional test functions. Finally, we implement the new algorithm on a real pharmaceutical data set for finding a group of chemical compounds with optimal properties.