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A Platform of Anti-biofilm Assays Suited to the Exploration of Natural Compound Libraries

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Integrated Machine Learning and Structure-Based Virtual Screening Identifies Natural Product Targeting 50S Ribosome

Jixing Liu¹, Henry H Y Tong^1,2, Hang Zheng³

¹Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.

Molecules (Basel, Switzerland)

|November 27, 2025

Summary

This summary is machine-generated.

Researchers identified potent anti-acne compounds by computationally screening a large natural product library targeting the 50S ribosomal subunit of Cutibacterium acnes (C. acnes). Tripterin showed significant antibacterial activity against C. acnes.

Keywords:

50S ribosomal subunit Cutibacterium acnes acne vulgaris docking machine learning natural products tripterin

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Area of Science:

Microbiology and Pharmacology
Computational Chemistry and Drug Discovery

Background:

Acne vulgaris is an inflammatory skin condition where Cutibacterium acnes (C. acnes) plays a key role.
The 50S large ribosomal subunit is crucial for C. acnes protein synthesis and viability, making it a potential therapeutic target.

Purpose of the Study:

To develop and apply an integrated computational-experimental workflow for identifying novel anti-C. acnes compounds.
To discover new antibacterial agents targeting the 50S ribosomal subunit for acne treatment.

Main Methods:

A large natural product library (186,659 compounds) was screened using machine learning quantitative structure-activity relationship (ML-QSAR) models trained on 50S/23S rRNA ligands.
Compounds were further filtered using ADMET properties and structure-based docking against 23S rRNA.
Experimental validation included minimum inhibitory concentration (MIC) assays against C. acnes.

Main Results:

The computational workflow successfully triaged the library, identifying six experimental hits.
Three compounds demonstrated significant anti-C. acnes activity with MICs ≤8 μg/mL.
Tripterin, a pentacyclic triterpenoid, was the most potent hit, with MICs ranging from 0.5-2 μg/mL.

Conclusions:

A multistage computational screening strategy focused on the 50S ribosomal subunit is effective for discovering anti-C. acnes compounds.
This integrated approach efficiently prioritizes natural products with quantifiable antibacterial activity against C. acnes.
Tripterin represents a promising lead compound for developing new acne treatments.