Isolation, structural characterization, and anticancer potential of a novel glycolipid biosurfactant S1B with AI-driven medium optimization

Glycolipid biosurfactants (BSs) are multifunctional biomolecules with potential applications in therapeutics and industry due to their biocompatibility and biodegradability. In this study, we report the isolation and characterization of a novel glycolipid biosurfactant from a Bacillus species, with emphasis on its anticancer properties and production optimization using a machine learning (ML) algorithm. Water samples from the Ganga River were screened for biosurfactant-producing strains, and the most efficient isolate was cultivated under optimized conditions. The purified biosurfactant was structurally characterized using Fourier-transform infrared (FTIR) spectroscopy, proton and carbon nuclear magnetic resonance (¹H and ¹³C NMR), and liquid chromatography-mass spectrometry (LC-MS), confirming glycolipid moieties. A multilayer perceptron artificial neural network (MLP-ANN) model was employed to optimize medium composition and growth conditions, resulting in improved biosurfactant yield. The findings highlight both the anticancer activity and production efficiency of the newly identified glycolipid biosurfactant, supporting its potential in biomedical and biotechnological applications.