AlvaDesc[1][2] is a commercial software application for the calculation and analysis of molecular descriptors, fingerprints, and structural patterns. Developed by Alvascience, alvaDesc is used in cheminformatics and quantitative structure–activity relationship (QSAR) modeling to numerically describe molecular structures, aiding in chemical property prediction and machine learning applications.[3][4]

Overview

Molecular descriptors and fingerprints[5][6] serve as mathematical representations of chemical compounds, enabling computational models to predict properties such as bioactivity, toxicity, and solubility.

Features

AlvaDesc supports the calculation of molecular descriptors and fingerprints across multiple categories:

See also

References

  1. ^ Mauri, Andrea (2020). "alvaDesc: A Tool to Calculate and Analyze Molecular Descriptors and Fingerprints". Methods in Pharmacology and Toxicology. New York, NY: Springer US. pp. 801–820. doi:10.1007/978-1-0716-0150-1_32. ISBN 978-1-0716-0149-5. ISSN 1557-2153. S2CID 213896490.
  2. ^ Mauri, Andrea; Bertola, Matteo (2022). "Alvascience: A New Software Suite for the QSAR Workflow Applied to the Blood–Brain Barrier Permeability". International Journal of Molecular Sciences. 23 (12882): 12882. doi:10.3390/ijms232112882. PMC 9655980. PMID 36361669.
  3. ^ Scienomics alvaDesc Plugin, SCIENOMICS LLC, Atlanta, Georgia (USA)
  4. ^ OCHEM alvaDesc Integration, Online Chemical Database with modeling environment
  5. ^ Todeschini, Roberto; Consonni, Viviana (2000). Handbook of Molecular Descriptors. Methods and Principles in Medicinal Chemistry. Wiley. doi:10.1002/9783527613106. ISBN 978-3-527-29913-3.
  6. ^ Todeschini, R., & Consonni, V. (2009). Molecular Descriptors for Chemoinformatics. Molecular Descriptors for Chemoinformatics (Vol. 41). Wiley. https://doi.org/10.1002/9783527628766
  7. ^ List of molecular descriptors calculated by alvaDesc, Alvascience srl, Lecco, ITALY
  8. ^ Rogers, D., & Hahn, M. (2010). Extended-connectivity fingerprints. Journal of Chemical Information and Modeling, 50(5), 742–754. https://doi.org/10.1021/ci100050t
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