SYBA

SYBA classifies organic compounds as easy-to-synthesize (ES) or hard-to-synthesize (HS) using a fragment-based Bernoulli naïve Bayes model to assess synthetic accessibility.


Key Features:

  • Fragment-Based Classification: Infers synthetic accessibility from constituent molecular fragments by attributing fragment-level contributions.
  • Bernoulli naïve Bayes Model: Uses a Bernoulli naïve Bayes classifier to model fragment presence/absence for ES versus HS classification.
  • SYBA Scores: Computes SYBA scores by assigning contributions to individual fragments based on their observed frequencies in ES and HS sets.
  • Training and Databases: Trained on easy-to-synthesize molecules from ZINC15 and hard-to-synthesize molecules generated via Nonpher.
  • Comparative Performance: Evaluated against a random forest baseline and the synthetic accessibility methods SAScore and SCScore, with improvements over SAScore and SCScore at their suggested thresholds and marginal improvement over the random forest baseline.
  • Threshold Optimization: Notes that adjusting the SAScore threshold (from 6.0 to -4.5) can produce performance comparable to SYBA, indicating parameter sensitivity.
  • Analytical Utility: Enables dissection of individual fragment contributions to identify molecular parts that influence synthetic accessibility.
  • Implementation: Implemented as a Python-based bioinformatics package.

Scientific Applications:

  • Medicinal Chemistry: Prioritizing and evaluating the synthetic accessibility of candidate molecules in medicinal chemistry workflows.
  • Drug Discovery: Assisting compound selection for synthesis in drug discovery to improve screening-to-synthesis efficiency.
  • Design and Route Optimization: Providing fragment-level insights to guide compound design and optimization of synthetic routes.

Methodology:

SYBA assigns fragment contributions based on observed fragment frequencies in ES molecules from ZINC15 and HS molecules generated by Nonpher, and applies a Bernoulli naïve Bayes classifier on fragment presence/absence to compute compound-level SYBA scores.

Topics

Details

License:
GPL-3.0
Programming Languages:
Python
Added:
1/18/2021
Last Updated:
11/24/2024

Operations

Publications

Voršilák M, Kolář M, Čmelo I, Svozil D. SYBA: Bayesian estimation of synthetic accessibility of organic compounds. Unknown Journal. 2020. doi:10.21203/rs.2.22597/v3.

Voršilák M, Kolář M, Čmelo I, Svozil D. SYBA: Bayesian estimation of synthetic accessibility of organic compounds. Journal of Cheminformatics. 2020;12(1). doi:10.1186/s13321-020-00439-2. PMID:33431015. PMCID:PMC7238540.

PMID: 33431015
PMCID: PMC7238540
Funding: - Ministerstvo Školství, Mládeže a Tělovýchovy: 20/2015, LM2018130, RVO 68378050-KAV-NPUI - Operational Programme Research, Development and Education: CZ.02.1.01/0.0/0.0/16_019/0000785