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Voršilák2020 - Bayesian estimation of synthetic accessibility of organic compounds

ABSTRACT: SYBA uses a fragment-based approach to classify whether a molecule is easy or hard to synthesize, and it can also be used to analyze the contribution of individual fragments to the total synthetic accessibility. The easy-to-synthesize dataset is an extract of the ZINC purchasable compounds, and the hard-to-synthesize dataset is generated using a Nonpher approach (introducing small molecular perturbations to transform molecules into more complex compounds). The fragments are calculated with ECFP8 descriptors, and independence between fragments is assumed. Model Type: Predictive machine learning model. Model Relevance: Prediction of synthetic accessibility Model Encoded by: Miquel Duran-Frigola (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos7pw8

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2407180002 | BioModels | 2024-07-18

REPOSITORIES: BioModels

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SYBA: Bayesian estimation of synthetic accessibility of organic compounds.

Voršilák Milan M Kolář Michal M Čmelo Ivan I Svozil Daniel D

Journal of cheminformatics 20200520 1

SYBA (SYnthetic Bayesian Accessibility) is a fragment-based method for the rapid classification of organic compounds as easy- (ES) or hard-to-synthesize (HS). It is based on a Bernoulli naïve Bayes classifier that is used to assign SYBA score contributions to individual fragments based on their frequencies in the database of ES and HS molecules. SYBA was trained on ES molecules available in the ZINC15 database and on HS molecules generated by the Nonpher methodology. SYBA was compared with a ran ...[more]

PMID: 33431015

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