Related Experiment Video

Updated: Jun 26, 2026

Candida albicans Biofilm Chip CaBChip for High-throughput Antifungal Drug Screening

Candida albicans Biofilm Chip CaBChip for High-throughput Antifungal Drug Screening

Published on: July 18, 2012

Employing machine learning for identifying antifungal compounds against Candida albicans

Dienny Rodrigues de Souza^1,2, Lívia Do Carmo Silva¹, Kleber Santiago Freitas E Silva¹

¹Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Goiás, Goiânia, Brazil.

Future Microbiology

|July 2, 2025

Abstract

Aims:

To evaluate the efficacy of a machine learning approach in developing classification and regression models for antifungal activity against Candida albicans.

Materials & Methods:

Utilized RF, SVM, and LightGBM algorithms to screen the eMolecules® library. Selected 17 virtual hits for in vitro assays.

Results:

Eleven compounds showed activity against C. albicans. Compounds 1 and 17 inhibited C. albicans at 0.51 µM and 0.071 µM, respectively.

Conclusions:

The RF model proved effective for virtual screening, demonstrating the success of the physicochemical classification and regression model in identifying new antifungal molecules against C. albicans.

Keywords:

Candida albicans LightGBM antifungal activity classification model machine learning random forest regression model support vector machine

More Related Videos

Bio-energetics Investigation of Candida albicans Using Real-time Extracellular Flux Analysis

Bio-energetics Investigation of Candida albicans Using Real-time Extracellular Flux Analysis

Published on: March 19, 2019

Quantifying the Antifungal Activity of Peptides Against Candida albicans

Quantifying the Antifungal Activity of Peptides Against Candida albicans

Published on: January 13, 2023

Related Concept Videos

Candidiasis

Candidiasis

Candidiasis is a fungal infection caused by opportunistic species of Candida. It can affect various anatomical sites, including the skin, oral cavity, nails, and genitourinary tract. Among its forms, vaginal candidiasis is the most common type of mucosal infection. It typically results from the overgrowth of Candida albicans in the vaginal mucosa. Under normal conditions, C. albicans exists as a commensal organism within the vaginal microbiota, regulated by the dominance of lactobacilli, which...

Antifungal Agents

Antifungal Agents

Amphotericin B is a broad-spectrum antifungal agent that exploits structural differences between fungal and mammalian cell membranes. Its amphipathic structure—featuring a hydrophobic polyene-lactone ring and a hydrophilic region containing mycosamine and carboxylic acid groups—enables selective binding to ergosterol, a sterol predominantly found in fungal plasma membranes. This selective interaction underlies the drug’s antifungal activity, although weak binding to cholesterol contributes to...

Articles linked to this work by shared authors, journal, and citation graph.

Why next-generation mechanistic models will transform drug discovery: integrating efficacy and safety.

Expert opinion on drug discovery·2026

Design, synthesis, biological evaluation, and in silico characterization of chalcone derivatives as antidiabetic hits.

Scientific reports·2026

Activation of the Complement System: Morphological Differences in the Surface of PIBCA Nanoparticles Coated with Polysaccharides.

ACS nanoscience Au·2026

The hemoglobin-derived peptide LVV-H7 attenuates select behavioral alterations in male spontaneously hypertensive rats.

Neuropeptides·2026

Discovery of an efficacious 2-ethyl-4-phenylthiazole derivative against acute Chagas disease via multiparametric hit-to-lead optimization and in vivo efficacy.

European journal of medicinal chemistry·2026

Th17 cells require the DNA repair sensor xeroderma pigmentosum complementation Group C to control oxidative DNA damage in a murine model.

Nature communications·2026

See all related articles