Cheminformatics and Molecular Data Science
Cheminformatics and molecular data science turn chemical structure into computable knowledge. They connect molecules, identifiers, databases, descriptors, fingerprints, reactions, assays, spectra, properties, bioactivity records, similarity metrics, machine-learning models, and reproducible workflows into a practical science of chemical information. This article introduces cheminformatics through molecular representation, SMILES, InChI, molecular graphs, descriptors, fingerprints, Tanimoto similarity, chemical space, compound databases, PubChem, ChEMBL, structure standardization, assay data, QSAR, machine learning, data leakage, validation, reaction data, materials data, FAIR principles, provenance, uncertainty, responsible molecular prediction, and reproducible molecular data workflows.









