August 7, 2025

Computational Drug Likeness Profiling and Chemical Space Analysis of Bioactive Microbial Secondary Metabolites via RDKit, PCA, and Tanimoto-Based Fingerprint Clustering

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YKYathu Krishna Y K

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Implication

In silico analysis evaluates drug likeness and similarity of nine microbial metabolites, suggesting robust profiling is achievable using open-source tools.

Key Points

This research focuses on the drug-likeness of nine diverse microbial secondary metabolites using computational methods.
Five out of nine compounds satisfied drug-likeness criteria based on Lipinski's and Veber's rules, while larger antibiotics failed.
Principal component analysis revealed the distribution of metabolites in chemical space, highlighting structural differences.
The approach combines various cheminformatics tools, making it accessible for educational and low-resource settings.

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Yathu Krishna Y K (2025) studied this question.

synapsesocial.com/papers/689a0fa0e6551bb0af8d1a57 https://doi.org/10.26434/chemrxiv-2025-qdp8w

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Implication

In silico analysis evaluates drug likeness and similarity of nine microbial metabolites, suggesting robust profiling is achievable using open-source tools.

Computational Drug Likeness Profiling and Chemical Space Analysis of Bioactive Microbial Secondary Metabolites via RDKit, PCA, and Tanimoto-Based Fingerprint Clustering

Discussion

Member takes

Implication

Key Points

Cite This Study

Computational Drug Likeness Profiling and Chemical Space Analysis of Bioactive Microbial Secondary Metabolites via RDKit, PCA, and Tanimoto-Based Fingerprint Clustering

Discussion

Member takes

Implication

Key Points

Cite This Study

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