Bayesian Optimization in Drug Discovery

Colliandre, Lionel; Muller, Christophe

doi:10.1007/978-1-0716-3449-3_5

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2024

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Cited by 3 publications

References 99 publications

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Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization

Shoyeb Raihan,

Khosravi,

Das

et al. 2024

Manufacturing Letters

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Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization

Shoyeb Raihan,

Khosravi,

Das

et al. 2024

Manufacturing Letters

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The amorphous state as a frontier in computational materials design

Liu,

Madanchi,

Anker

et al. 2024

Nat Rev Mater

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Comprehensive 4D-QSAR Analysis for Predicting the Biological Activity of Quinazoline: Integrating Electron Conformational and Genetic Algorithm Approaches through a Unified Framework to Uncover Essential Pharmacophoric Features

Şahin,

Sarıpınar

2024

Preprint

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To unravel the intricate connection between the molecular structure and bioactivity within a series of quinazolines, our investigation adopted a synergistic methodology that employed a genetic algorithm (GA) in tandem with four-dimensional quantitative structure-activity relationship (4D-QSAR) modeling. Rather than relying on a singular conformation, our model construction represented each compound with a set of conformers. The geometric and electronic structure attributes for every atom and bond in each molecule were computed and organized into an electron–conformational matrix of contiguity (ECMC). The electron conformational submatrix of activity (ECSA) was derived through a comparative analysis of these matrices. For the series of quinazolines, we developed a pharmacophore model based on chemical properties utilizing the EMRE software package. Employing a genetic algorithm, we identified crucial variables to predict theoretical activity. The training set, consisting of 41 compounds, was used to develop 4D-QSAR models, and their predictive capacity was evaluated by including an additional 20 compounds in the test set. The model, incorporating the top twelve parameters, exhibited satisfactory performance. To further scrutinize the contribution of each descriptor to biological activity within the EC–GA model, the E statistics technique was applied.

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Bayesian Optimization in Drug Discovery

Cited by 3 publications

References 99 publications

Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization

Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization

The amorphous state as a frontier in computational materials design

Comprehensive 4D-QSAR Analysis for Predicting the Biological Activity of Quinazoline: Integrating Electron Conformational and Genetic Algorithm Approaches through a Unified Framework to Uncover Essential Pharmacophoric Features

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