Expanding Chemical Representation with k-mers and Fragment-based Fingerprints for Molecular Fingerprinting
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917625176522752 |
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| author | Ali, Sarwan Chourasia, Prakash Patterson, Murray |
| author_facet | Ali, Sarwan Chourasia, Prakash Patterson, Murray |
| contents | This study introduces a novel approach, combining substruct counting, $k$-mers, and Daylight-like fingerprints, to expand the representation of chemical structures in SMILES strings. The integrated method generates comprehensive molecular embeddings that enhance discriminative power and information content. Experimental evaluations demonstrate its superiority over traditional Morgan fingerprinting, MACCS, and Daylight fingerprint alone, improving chemoinformatics tasks such as drug classification. The proposed method offers a more informative representation of chemical structures, advancing molecular similarity analysis and facilitating applications in molecular design and drug discovery. It presents a promising avenue for molecular structure analysis and design, with significant potential for practical implementation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19844 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Expanding Chemical Representation with k-mers and Fragment-based Fingerprints for Molecular Fingerprinting Ali, Sarwan Chourasia, Prakash Patterson, Murray Biomolecules Machine Learning Chemical Physics This study introduces a novel approach, combining substruct counting, $k$-mers, and Daylight-like fingerprints, to expand the representation of chemical structures in SMILES strings. The integrated method generates comprehensive molecular embeddings that enhance discriminative power and information content. Experimental evaluations demonstrate its superiority over traditional Morgan fingerprinting, MACCS, and Daylight fingerprint alone, improving chemoinformatics tasks such as drug classification. The proposed method offers a more informative representation of chemical structures, advancing molecular similarity analysis and facilitating applications in molecular design and drug discovery. It presents a promising avenue for molecular structure analysis and design, with significant potential for practical implementation. |
| title | Expanding Chemical Representation with k-mers and Fragment-based Fingerprints for Molecular Fingerprinting |
| topic | Biomolecules Machine Learning Chemical Physics |
| url | https://arxiv.org/abs/2403.19844 |