International Journal of Cheminformatics

International Journal of Cheminformatics is a peer-reviewed Online journal launched in 2023 that aims to serve as a platform for the propagation of innovative ideas and research in all areas of Cheminformatics. All manuscripts undergo a rigorous peer-review process. It is designed to create interest among researchers in this field, which deals with the use of computer and information techniques applied to a wide range of problems in the field of chemistry. The main functions of this journal are in the areas of topology, chemical graph theory, information retrieval, and data mining in the chemical field. Cheminformatics can also be used in chemical and allied industries in several other forms. The journal aims to publish original, high-quality papers that are peer-reviewed by our expert editorial team to ensure the publication of only good-quality papers.

• Chemical databases and data curation: database architecture for chemical structures, curation and standardisation workflows, structure normalisation and deduplication, data quality assessment, FAIR data practices, and integration across public resources including PubChem, ChEMBL, ZINC, DrugBank, and the Protein Data Bank.
• Molecular representation and descriptors: line notations and structural formats, molecular fingerprints and bit-string encodings, topological and connectivity indices, three-dimensional and pharmacophoric descriptors, tautomer and protonation state handling, and descriptor selection methods.
• Structure search and similarity: substructure and maximum common subgraph search, similarity and diversity metrics, kernel methods for molecular comparison, scaffold analysis and scaffold hopping, clustering of chemical libraries, and retrieval effectiveness evaluation.
• QSAR and QSPR modelling: model construction and variable selection, applicability domain definition, internal and external validation, quantitative structure–property prediction, and mechanistic interpretation of structure–activity relationships.
• Machine learning and artificial intelligence for chemistry: supervised and unsupervised learning on chemical data, deep learning architectures for molecular property prediction, generative models for molecular design, active learning and reinforcement learning in chemical search, transfer learning on sparse datasets, and model interpretability.
• Molecular modelling and simulation: molecular mechanics and dynamics, quantum chemical and density functional calculations, conformational sampling, binding free energy estimation, protein homology modelling, and GPU-accelerated and distributed computation.
• Computer-aided drug design and virtual screening: structure-based and ligand-based design, molecular docking and scoring function development, pharmacophore modelling, target and off-target prediction, drug repurposing, lead identification and optimisation, and fragment-based approaches.
• ADMET and pharmacokinetic prediction: solubility and permeability estimation, partition coefficient calculation, metabolism and transporter prediction, toxicity and toxicokinetic modelling, physiologically based pharmacokinetic simulation, and computational alerts for liabilities.
• Computer-assisted synthesis and structure elucidation: retrosynthetic analysis and route prediction, reaction outcome and yield prediction, automatic reaction mechanism generation, computer-assisted structure elucidation from spectroscopic data, and reaction database construction.
• Chemical graph theory and algorithms: molecular topology, graph-theoretical invariants, structure enumeration and generation, canonical labelling and isomorphism, frequent subgraph mining on chemical graphs, and complexity and performance of cheminformatics algorithms.
• Cheminformatics infrastructure and standards: open-source toolkits and libraries, workflow and pipeline systems, data exchange formats and ontologies, reproducibility and benchmarking practices, and interoperability between chemical and biological data resources.
• Chemical text and patent informatics: chemical named entity recognition, structure extraction from literature and patents, patent landscaping for chemical innovation, intellectual property mining, and bibliometric analysis of chemical research.