Molecular Fingerprint

Fig. 1

A molecular fingerprint is a unique pattern or representation of a molecule’s chemical structure and properties. It is used in cheminformatics and computational chemistry for tasks such as molecular similarity analysis, virtual screening and drug discovery. Molecular fingerprints encode information about a molecule’s atoms, bonds and various structural features, allowing for efficient comparisons and predictions in the field of molecular modeling and drug design.

The use of molecular fingerprints in cheminformatics and computational chemistry is a fundamental and versatile approach for representing chemical compounds. Molecular fingerprints are essentially a series of bits, often in binary form, that encode the presence or absence of specific chemical substructures or molecular properties in a molecule. These fingerprints serve as a compact and informative representation of molecules and are extensively used in various applications, ranging from similarity searching and clustering to quantitative structure-activity relationship (QSAR) modeling and virtual screening in drug discovery.

The Importance of Molecular Fingerprints:

Molecular fingerprints are essential tools in cheminformatics and computational chemistry for several reasons:

1. Dimensionality Reduction: Chemical compounds can be highly complex, with numerous atoms and bonds. Molecular fingerprints condense this complexity into a binary or numerical format, reducing the dimensionality of the data. This makes it more feasible to compare, analyze and model large chemical datasets.
2. Chemical Similarity: Molecular fingerprints are widely used for assessing the similarity between chemical compounds. By comparing the fingerprints of different molecules, researchers can identify structurally similar compounds, which is crucial in tasks such as lead compound identification and virtual screening.
3. Substructure Analysis: Researchers often need to identify specific chemical substructures within molecules. Molecular fingerprints are designed to encode the presence or absence of predefined substructures or molecular fragments, enabling efficient substructure searching.
4. Quantitative Structure-Activity Relationship (QSAR) Modeling: QSAR models aim to predict the biological or chemical activity of compounds based on their structural features. Molecular fingerprints, which capture molecular structure, are key input variables for QSAR modeling.
5. Database Searching: Molecular fingerprints facilitate the rapid searching of chemical databases. By representing molecules as fingerprints, it becomes possible to quickly identify compounds with desired structural or functional characteristics.
6. Clustering and Diversity Analysis: Researchers use molecular fingerprints to group compounds into clusters based on structural similarities. This helps in exploring chemical space, identifying diverse compound sets and designing compound libraries.
7. Machine Learning: Molecular fingerprints can be used as features in machine learning algorithms for various predictive modeling tasks, including toxicity prediction, property estimation and compound classification.

Molecular fingerprints are diverse and can be categorized into several types based on their generation methods and applications in cheminformatics and computational chemistry. Here are some of the various types of molecular fingerprints:

ECFP (Extended Connectivity Fingerprint): ECFP fingerprints capture molecular features based on atom connectivity. They are widely used for similarity searching and compound clustering.

MACCS Keys: The MACCS (Molecular ACCess System) keys are a set of structural keys used for substructure searching and similarity analysis. They encode specific chemical substructures or patterns.

RDKit Fingerprint: RDKit provides a variety of fingerprints, including the Morgan fingerprint and the Atom Pair fingerprint. These capture different aspects of molecular structure and can be used for diverse cheminformatics tasks.

PubChem Fingerprint: Developed by the PubChem project, these fingerprints encode chemical features of molecules. They are widely used for chemical similarity searching in large compound databases.

Daylight Fingerprint: These fingerprints, developed by the Daylight Chemical Information Systems, are based on the presence or absence of certain substructures and functional groups in a molecule.

Circular Fingerprints (Circular Morgan Fingerprints): These fingerprints capture information about the environment around each atom in a molecule, resulting in circular patterns. They are effective for similarity analysis and compound clustering.

Substructure Keys: These fingerprints encode the presence or absence of specific chemical substructures. They are useful for identifying molecules containing certain structural motifs.

Chemical Hashed Fingerprints: These fingerprints use a hashing algorithm to represent molecular substructures as binary bits. They are efficient for fast searching and comparisons.

2D vs. 3D Fingerprints: Some fingerprints are designed to capture three-dimensional structural information, which is important for activities like molecular docking and conformational analysis.

Hybrid Fingerprints: These fingerprints combine multiple types of information, such as substructure keys and physicochemical properties, to provide a comprehensive representation of molecules.

Descriptors-Based Fingerprints: Instead of structural information, these fingerprints use molecular descriptors (quantitative properties) like molecular weight, LogP and polar surface area as features.

Topological Torsion Fingerprints: These fingerprints capture information about the topological relationships between bonds in a molecule. They are useful for quantitative structure-activity relationship (QSAR) modeling.

Pharmacophore Fingerprints: Pharmacophore fingerprints encode the essential features of a molecule required for binding to a specific biological target. They are used in drug design and virtual screening.

Choosing the Right Molecular Fingerprint:

Selecting the appropriate molecular fingerprint is a critical decision in cheminformatics and computational chemistry. The choice depends on several factors:

1. Research Objective: Consider the specific goal of the analysis. Are you performing similarity searching, substructure searching, QSAR modeling, or another task? Different fingerprints are optimized for different objectives.
2. Nature of Compounds: The type of chemical compounds being analyzed matters. Some fingerprints may be more suitable for small organic molecules, while others are designed for large biomolecules like proteins or nucleic acids.
3. Dataset Size: The size of your dataset can influence the choice of fingerprint. Some fingerprints are more computationally intensive than others, so efficiency may be a consideration.
4. Chemical Diversity: If your dataset contains diverse chemical structures, you may need a fingerprint that balances sensitivity to structural variations with robustness.
5. Performance Benchmarking: Evaluate the performance of different fingerprints on your specific dataset. Conduct benchmarking experiments to determine which fingerprint type yields the best results for your application.
6. Software and Tools: Consider the availability of software and tools that support your chosen fingerprint. Some fingerprints may be readily available in popular cheminformatics libraries.
7. Domain Knowledge: Familiarity with the chemical domain and the specific requirements of the project can influence choice of fingerprint.

In conclusion, molecular fingerprints are invaluable tools in cheminformatics and computational chemistry. They enable the representation of complex chemical compounds in a concise format, facilitating tasks such as similarity searching, substructure analysis and predictive modeling. Researchers must carefully select the appropriate fingerprint type based on their research objectives, the nature of their compounds and other relevant factors to ensure the success of their analyses and investigations in the field of chemistry and drug discovery.

Reference :

1. Bender A, Glen RC. Molecular similarity: a key technique in molecular informatics. Org Biomol Chem. 2004 Nov 21;2(22):3204–18. doi: 10.1039/B409813G. Epub 2004 Oct 14.
2. Cereto-Massagué A, Ojeda MJ, Valls C, Mulero M, Garcia-Vallvé S, Pujadas G. Molecular fingerprint similarity search in virtual screening. Methods. 2015 Jan;71:58–63. doi: 10.1016/j.ymeth.2014.08.005. Epub 2014 Aug 15.
3. Mellor CL, Marchese Robinson RL, Benigni R, Ebbrell D, Enoch SJ, Firman JW, Madden JC, Pawar G, Yang C, Cronin MTD. Molecular fingerprint-derived similarity measures for toxicological read-across: Recommendations for optimal use. Regul Toxicol Pharmacol. 2019 Feb;101:121–134. doi: 10.1016/j.yrtph.2018.11.002. Epub 2018 Nov 20.
4. Vogt M, Bajorath J. Predicting the performance of fingerprint similarity searching. Methods Mol Biol. 2011;672:159–73. doi: 10.1007/978–1–60761–839–3_6.
5. Muegge I, Mukherjee P. An overview of molecular fingerprint similarity search in virtual screening. Expert Opin Drug Discov. 2016;11(2):137–48. doi: 10.1517/17460441.2016.1117070. Epub 2015 Dec 4.