Expanding Chemical Representation with k-mers and Fragment-based Fingerprints for Molecular Fingerprinting

Autor:	Ali, Sarwan, Chourasia, Prakash, Patterson, Murray
Rok vydání:	2024
Předmět:	Quantitative Biology - Biomolecules Computer Science - Machine Learning Physics - Chemical Physics
Zdroj:	SimBig2023
Druh dokumentu:	Working Paper
Popis:	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. Comment: 12 Pages, 3 tables, Accepted at SimBig2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.19844 Zobrazit plný text záznamu View this record from Arxiv