5/29/2023 0 Comments Chemdoodle support![]() Our analysis demonstrates that GASA achieved remarkable performance in distinguishing similar molecules compared with other methods and had a broader applicability domain. GASA was extensively evaluated and compared with two descriptor-based machine learning methods (random forest, RF eXtreme gradient boosting, XGBoost) and four existing scores (SYBA: SYnthetic Bayesian Accessibility SCScore: Synthetic Complexity score RAscore: Retrosynthetic Accessibility score SAscore: Synthetic Accessibility score). The sampling around the hypothetical classification boundary was used to improve the ability of GASA to distinguish structurally similar molecules. ![]() GASA is a graph neural network (GNN) architecture that makes self-feature deduction by applying an attention mechanism to automatically capture the most important structural features related to synthetic accessibility. In this study, we proposed a data-driven interpretable prediction framework called GASA (Graph Attention-based assessment of Synthetic Accessibility) to evaluate the synthetic accessibility of small molecules by distinguishing compounds to be easy- (ES) or hard-to-synthesize (HS). Several expert-crafted scoring methods and descriptor-based quantitative structure-activity relationship (QSAR) models have been developed for synthetic accessibility assessment, but their practical applications in drug discovery are still quite limited because of relatively low prediction accuracy and poor model interpretability. This demonstrates an application of our database in choosing likely-functional residues for mutagenesis studies aimed at understanding or changing sesquiterpene synthase product specificity.Īccurate estimation of the synthetic accessibility of small molecules is needed in many phases of drug discovery. Finally, we present a case describing mutational studies on residues altering product specificity, for which we analyzed conservation in our database. This analysis indicated regions of terpene synthase sequence space which so far are unexplored experimentally. In addition, putative terpene synthase sequences were obtained from various resources and compared with the annotated sesquiterpene synthases. The annotated enzyme sequences allowed for an analysis of terpene synthase motifs, leading to the extension of one motif and recognition of a variant of another. To address this, we have gathered 262 plant sesquiterpene synthase sequences with experimentally characterized products. ![]() A comprehensive analysis of these enzymes in terms of product specificity has been hampered by the lack of a centralized resource of sufficient functionally annotated sequence data. These in turn are produced by a diverse range of sesquiterpene synthases. Plants exhibit a vast array of sesquiterpenes, organic hydrocarbons which often function as herbivore-repellents or pollinator-attractants. This demonstrates an application of our database in choosing likely-functional residues for mutagenesis studies aimed at understanding or changing sesquiterpene synthase product specificity. ![]() ![]() Plants exhibit a vast array of sesquiterpenes, C15 hydrocarbons which often function as herbivore-repellents or pollinator-attractants. ![]()
0 Comments
Leave a Reply. |