ArticleJournal of cheminformatics2017
Nonpher: computational method for design of hard-to-synthesize structures.
Article in Journal of cheminformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- MolPrice: assessing synthetic accessibility of molecules based on market value.Journal of cheminformatics · 2025Article
- DeepSA: a deep-learning driven predictor of compound synthesis accessibility.Journal of cheminformatics · 2023Article
- Spacial Score─A Comprehensive Topological Indicator for Small-Molecule Complexity.Journal of medicinal chemistry · 2023Article
- A molecule perturbation software library and its application to study the effects of molecular design constraints.Journal of cheminformatics · 2023Article
- Magicmol: a light-weighted pipeline for drug-like molecule evolution and quick chemical space exploration.BMC bioinformatics · 2023Article
- Neural Networks in the Design of Molecules with Affinity to Selected Protein Domains.International journal of molecular sciences · 2023Article
- Critical assessment of synthetic accessibility scores in computer-assisted synthesis planning.Journal of cheminformatics · 2023Article
- Prediction of Compound Synthesis Accessibility Based on Reaction Knowledge Graph.Molecules (Basel, Switzerland) · 2022Article
- Drug design by machine-trained elastic networks: predicting Ser/Thr-protein kinase inhibitors' activities.Molecular diversity · 2021Article
- Profiling and analysis of chemical compounds using pointwise mutual information.Journal of cheminformatics · 2021Article
- SYBA: Bayesian estimation of synthetic accessibility of organic compounds.Journal of cheminformatics · 2020Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
In cheminformatics, machine learning methods are typically used to classify chemical compounds into distinctive classes such as active/nonactive or toxic/nontoxic. To train a classifier, a training data set must consist of examples from both positive and negative classes. While a biological activity or toxicity can be experimentally measured, another important molecular property, a synthetic feasibility, is a more abstract feature that can't be easily assessed. In the present paper, we introduce Nonpher, a computational method for the construction of a hard-to-synthesize virtual library. Nonpher is based on a molecular morphing algorithm in which new structures are iteratively generated by simple structural changes, such as the addition or removal of an atom or a bond. In Nonpher, molecular morphing was optimized so that it yields structures not overly complex, but just right hard-to-synthesize. Nonpher results were compared with SAscore and dense region (DR), other two methods for the generation of hard-to-synthesize compounds. Random forest classifier trained on Nonpher data achieves better results than models obtained using SAscore and DR data.
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