The representation of molecules has been a central focus in chemistry throughout history, evolving from traditional structural diagrams depicting atoms and bonds, to advanced machine-readable notations essential for cheminformatics and drug discovery.
This review explores various chemical representations, including structural encodings such as molecular graphs, and linear notations like SMILES, and molecular fingerprints. It examines the strengths and limitations of these representations, and highlights the development of computer-readable formats that facilitate efficient digital storage, querying, and visualisation of chemical compounds. 1 Additionally, the review presents case studies on widely used molecular representations and descriptors, including Molecular Access System keys (MACCS-keys) fingerprints, Avalon fingerprints, and Morgan fingerprints, as well as their applications in compound querying and clustering, such as Taylor-Butina clustering.
1.1 Molecular Graph Theory
1.1.1 Introduction To The Molecular Graph Representation
1.1.2 Mathematical Defintion of a Graph
1.1.3 Graph Traversal Algorithms
1.1.4 Molecular Graph Reprentations
1.1.5 Advantages of Molecular Graph Representations
1.1.6 Disadvantages of Molecular Graph Representations
1.1.7 Molecular Graphs in AI-Driven Small Molecule Drug Discovery
1.1.8 References
1.2 Molecular Descriptors
1.2.1 Introduction to Molecular Descriptors
1.2.2 Molecular Fingerprints
1.2.3 Key-Based Molecular Fingerprints - MACCS Keys
1.2.4 Hash-Based Molecular Fingerprints - Daylight Fingerprint & ECFPs
1.2.5 Advantages & Applications of Molecular Fingerprints
1.2.6 Molecular Fingerprints in Machine Learning
1.2.7 References
Additionally, each section of the literature review will have accompanying Python exercises related to the topic in question.
[1] David, L. et al. (2020) ‘Molecular representations in AI-Driven Drug Discovery: A review and practical guide’, Journal of Cheminformatics, 12(1).