ArticleJournal of cheminformatics2018
Spectrophores as one-dimensional descriptors calculated from three-dimensional atomic properties: applications ranging from scaffold hopping to multi-target virtual screening.
Article in Journal of cheminformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Prediction of Ligand Binding to Transthyretin Using Machine Learning Algorithms and Low-Dimensional Molecular Descriptors: A Tox24 Challenge Study.Chemical research in toxicology · 2026Article
- Evaluation of single-template ligand-based methods for the discovery of small-molecule nucleic acid binders.Briefings in bioinformatics · 2025Article
- A Review of Machine Learning and QSAR/QSPR Predictions for Complexes of Organic Molecules with Cyclodextrins.Molecules (Basel, Switzerland) · 2024Review
- Combatting over-specialization bias in growing chemical databases.Journal of cheminformatics · 2023Article
- Ligand-Based Discovery of a Small Molecule as Inhibitor of α-Synuclein Amyloid Formation.International journal of molecular sciences · 2022Article
- Novel Efficient Multistage Lead Optimization Pipeline Experimentally Validated for DYRK1B Selective Inhibitors.Journal of medicinal chemistry · 2022Article
- A multi-reference poly-conformational method forPeerJ · 2022Article
- Generative chemistry: drug discovery with deep learning generative models.Journal of molecular modeling · 2021Review
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Authors and funding
6 authors.
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Abstract
Spectrophores are novel descriptors that are calculated from the three-dimensional atomic properties of molecules. In our current implementation, the atomic properties that were used to calculate spectrophores include atomic partial charges, atomic lipophilicity indices, atomic shape deviations and atomic softness properties. This approach can easily be widened to also include additional atomic properties. Our novel methodology finds its roots in the experimental affinity fingerprinting technology developed in the 1990's by Terrapin Technologies. Here we have translated it into a purely virtual approach using artificial affinity cages and a simplified metric to calculate the interaction between these cages and the atomic properties. A typical spectrophore consists of a vector of 48 real numbers. This makes it highly suitable for the calculation of a wide range of similarity measures for use in virtual screening and for the investigation of quantitative structure-activity relationships in combination with advanced statistical approaches such as self-organizing maps, support vector machines and neural networks. In our present report we demonstrate the applicability of our novel methodology for scaffold hopping as well as virtual screening.
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