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.