MeFSAT

MeFSAT catalogs 1,830 non-redundant secondary metabolites from 184 medicinal fungi and provides chemical structures, computed physicochemical properties, drug-likeness scores, predicted ADMET properties, molecular descriptors, and predicted human protein targets to support natural product-based drug discovery.

Key Features:

• Curation and data sources: Manual curation and literature mining from over 900 published research articles and six specialized books on medicinal fungi.
• Coverage: Records for 184 medicinal fungi species and 1,830 secondary metabolites compiled into a non-redundant library.
• Chemical structures: Chemical structures are cataloged for all listed secondary metabolites.
• Computed physicochemical properties: Physicochemical properties are computed for each compound.
• Drug-likeness scoring: Multiple scoring schemes were applied to identify a subset of 228 drug-like compounds.
• Predicted ADMET properties: Absorption, Distribution, Metabolism, Excretion, and Toxicity properties are predicted for cataloged compounds.
• Molecular descriptors: Molecular descriptors are computed for structure-based analyses.
• Predicted human protein targets: Potential human target proteins are predicted for secondary metabolites.
• Chemical similarity networks: Chemical similarity networks were constructed to assess chemical diversity and relationships among compounds.
• Stereochemical and shape complexity analysis: Analysis indicates high stereochemical and shape complexity comparable to other natural product libraries.

Scientific Applications:

• Natural product–based drug discovery: Prioritization of fungal secondary metabolites with drug-like properties for pharmaceutical research.
• Chemical space exploration: Identification of diverse scaffolds and novel compounds through chemical similarity networks.
• Target identification: Generation of hypotheses for compound–protein interactions using predicted human protein targets.
• Structure–activity relationship (SAR) and lead optimization: Use of computed physicochemical properties and molecular descriptors to support SAR analyses and lead selection.
• Comparative natural product analysis: Evaluation of stereochemical and shape complexity relative to other natural product libraries.

Methodology:

Manual literature mining and curation from >900 articles and six books; compilation of a non-redundant compound library; computation of chemical structures, physicochemical properties, and molecular descriptors; prediction of ADMET properties and potential human protein targets; application of multiple drug-likeness scoring schemes; and construction of chemical similarity networks with stereochemical and shape complexity analysis.