ArticleJournal of cheminformatics2020
SYBA: Bayesian estimation of synthetic accessibility of organic compounds.
Article in Journal of cheminformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
- Chemical language models for early-stage drug discovery: applications, pitfalls, and future directions.Journal of computer-aided molecular design · 2026Review
- Design of Synthesizable PROTACs through Synthesis Constrained Generative Model and Reinforcement Learning.JACS Au · 2026Article
- Computational design of low-volatility lubricants for space using interpretable machine learning.Journal of cheminformatics · 2026Article
- Fast and Flexible 3D Molecular Design Framework for Novel Organic Optoelectronic Materials.JACS Au · 2026Article
- An electron-density point-cloud framework for robust protein-ligand interaction prediction.Nature communications · 2026Article
- ChemBang: Expanding the Chemical Space Around Small Molecules.Molecular informatics · 2026Article
- Enabling Synthetically Feasible Molecular Editing in Drug Discovery via Reaction-Regulated Graph-Based Genetic Algorithms.JACS Au · 2026Article
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- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- The Heterocycle Isostere Explorer: A Computational Tool for the Discovery of Novel Aromatic Heterocyclic Isosteres.Journal of medicinal chemistry · 2026Article
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- MolGAN-QRL: a hybrid framework for molecule generation using quantum-enhanced reinforcement learning.Journal of cheminformatics · 2026Article
- A Closed-Loop Hybrid Discovery System of Type I Photosensitizers for Hypoxic Tumor Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
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- RetroScore: graph edit distance-guided retrosynthesis for accessibility scoring with route metrics.Journal of cheminformatics · 2025Article
- From Bits to Bonds: High-Throughput Virtual Screening of Ribonucleic Acid Nanocarriers Using a Combinatorial Approach of Machine Learning and Molecular Dynamics.Journal of the American Chemical Society · 2025Article
- SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models.ACS central science · 2025Article
- SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models.ArXiv · 2025Article
- MolPrice: assessing synthetic accessibility of molecules based on market value.Journal of cheminformatics · 2025Article
- Design of Surfactant Molecules Under Performance Constraints.ACS sustainable chemistry & engineering · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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 random forest, that was utilized as a baseline method, as well as with other two methods for synthetic accessibility assessment: SAScore and SCScore. When used with their suggested thresholds, SYBA improves over random forest classification, albeit marginally, and outperforms SAScore and SCScore. However, upon the optimization of SAScore threshold (that changes from 6.0 to - 4.5), SAScore yields similar results as SYBA. Because SYBA is based merely on fragment contributions, it can be used for the analysis of the contribution of individual molecular parts to compound synthetic accessibility. SYBA is publicly available at https://github.com/lich-uct/syba under the GNU General Public License.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.