Karalti_2026_Molecules_31_

In this study, the biological activities of extracts obtained from Ramaria obtusissima were optimized using response surface methodology (RSM) and artificial neural networks-genetic algorithm (ANN-GA) approaches, and the chemical and biological profiles of the obtained extracts were evaluated with a holistic approach. Antioxidant potential was determined using FRAP, DPPH, TAS, TOS, and OSI parameters. It was found that the extract optimized with ANN-GA had significantly higher FRAP (242 +/- 3 mg Trolox equivalent/g), TAS (6.64 +/- 0.04 mmol/L), and DPPH (154 +/- 3 mg Trolox equivalent/g) values compared to the RSM extract, while its OSI value was lower. Anticholinesterase activities were evaluated using IC(50) values, and it was determined that the ANN-GA extract exhibited a stronger inhibitory effect on acetylcholinesterase (95 +/- 2 microg/mL) and butyrylcholinesterase (125 +/- 3 microg/mL) compared to the RSM extract. Antiproliferative effects were investigated in A549, MCF-7, and DU-145 cell lines, and a significant and dose-dependent suppression of cell proliferation was observed in all three cell lines, particularly at concentrations of 100 and 200 microg/mL. The chemical profile was determined using LC-MS/MS and GC-MS techniques. Higher levels of phenolic compounds such as gallic acid (6694.5 +/- 4.9 mg/kg), caffeic acid (3374.8 +/- 4.9 mg/kg), and quercetin (1563.1 +/- 2.3 mg/kg) were found in the ANN-GA extract. GC-MS analyses showed that the ANN-GA extract has a richer lipophilic component profile in terms of biologically active fatty acids and ester derivatives. The findings reveal that AI-assisted optimization offers a powerful and effective approach to enhancing the biological efficacy of mushroom-derived natural products.