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Machine learning models are developed for the mutagenic potential of organic compounds and applied to predict non-sugar sweeteners.

Degraders are an increasingly important sub-modality of small molecules as illustrated by an ever-expanding number of publications and clinical candidate molecules in human trials. Nevertheless, th...

This study analyzed chemical metabolites in composite flours (CF) produced from coffee pulp, plantain rachis, and rejected plantain, as well as their changes induced by extrusion. Seventy-two metabolites were identified by gas chromatography–time-of-flight mass spectrometry (GC-QTOF-MS) analysis in an HP-5ms capillary column. Results revealed that extrusion induced a selective modification of the metabolites. Some antinutritional compounds (caffeine, deoxyglucose, and galactosamine) were reduced. These effects contributed to improving the nutritional and functional quality of the CF. The chromatographic information from a dataset of 265 molecules was used to develop a conformation-independent quantitative structure-retention relationship (QSRR) model with good predictive capability (Q2 = 0.945), which was subsequently applied to estimate the retention index of the metabolites. This work provided an approach for metabolite profiling of agro-industrial CF and for predicting the chromatographic properties of new metabolites while advancing the valorization of coffee and plantain byproducts.

Birds occupy a major portion of the ecology and are considered a valuable species. In this modern era, the application of pesticides has increased and caused very severe harmful consequences to variou...

In this chapter we will present alvaDesc, a software to calculate and analyze molecular descriptors and fingerprints. Molecular descriptors and fingerprints play an essential role in quantitative stru...

alvaDescオンライン講習会 基礎コース（2025年12月）のニュースです。