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Molecular descriptors and fingerprints serve as mathematical representations of chemical compounds, enabling computational models to predict properties such as bioactivity, toxicity, and solubility.
The software is used widely in drug discovery, materials science, and environmental chemistry, allowing researchers to process large chemical datasets, extract key molecular features, and integrate results into machine learning workflows.
Features
AlvaDesc supports the calculation of descriptors and fingerprints across multiple categories:
0D–3D molecular descriptors: including Constitutional indices, topological indices, connectivity indices, geometrical descriptors, pharmacophore descriptors, charge descriptors, and more.
KNIME and Python integration: alvaDesc can be integrated into cheminformatics workflows using the KNIME analytics platform and Python scripting.
Scientific Contributions
AlvaDesc has been referenced in numerous peer-reviewed scientific publications, particularly in QSAR and Quantitative Structure-Property Relationship (QSPR) research