cheminformatics

Cheminformatics, or chemical informatics, applies computer and information techniques to solve chemical problems. It covers the representation, storage, retrieval, analysis, and prediction of chemical data. Core tasks include managing structures, descriptors, and metadata; performing similarity searches; and building models that relate molecular features to properties or activities.

Molecular representations include SMILES strings, InChI identifiers, and formats such as Molfile. From these, descriptors and fingerprints (for example MACCS or Daylight) enable quantitative structure–activity relationship (QSAR) modeling and virtual screening. Techniques span statistics and machine learning, from regression and classification to modern deep learning, used for property prediction, de novo design, and toxicity assessment. Quantum chemistry can provide accurate property estimates, integrated into broader workflows.

Public databases such as PubChem, ChEMBL, ChEBI, and the Protein Data Bank are central, along with proprietary catalogs. Standards and interoperability are supported by formats and identifiers such as SMILES, InChI, SDF/MOL files.

Software ecosystems include RDKit, Open Babel, CDK, and cheminformatics components in KNIME and pipelines built with Python or R. Workflows typically combine data curation, descriptor calculation, model training and validation, and deployment, enabling drug discovery, materials science, and environmental chemistry.

Challenges include data quality and provenance, reproducibility, interpretability of ML models, and licensing. The field continues to evolve with advances in AI, generative models for molecule design, and automated high-throughput screening.