UNIQUE: A Framework for Uncertainty Quantification Benchmarking.

Machine learning (ML) models have become key in decision-making for many disciplines, including drug discovery and medicinal chemistry. ML models are generally evaluated prior to their usage in high-stakes decisions, such as compound synthesis or experimental testing. However, no ML model is robust or predictive in all real-world scenarios.

Occurrences of allergenicity to banana pathogenesis-related-10 (PR10) protein variants.

Pathogenesis-related-10 (PR10) proteins play significant roles in plant defence against biotic and abiotic stresses. Recently, two banana PR10 proteins (MaPR10-BeB5 and MaPR10-GNA5) were characterised and shown to exhibit antifungal properties against . In rice, transgenic overexpression of PR10 proteins conferred resistance to pathogen infection and drought tolerance without affecting productivity, highlighting their potential for agricultural applications.

DeepCt: Predicting Pharmacokinetic Concentration-Time Curves and Compartmental Models from Chemical Structure Using Deep Learning.

After initial triaging using in vitro absorption, distribution, metabolism, and excretion (ADME) assays, pharmacokinetic (PK) studies are the first application of promising drug candidates in living mammals. Preclinical PK studies characterize the evolution of the compound's concentration over time, typically in rodents' blood or plasma. From this concentration-time (-) profiles, PK parameters such as total exposure or maximum concentration can be subsequently derived.

Proteomic identification of allergenic proteins in holm oak (Quercus ilex) seeds.

Humans have used Quercus ilex acorns as a staple food since ancient times. Recently, their nutritional and nutraceutical value has revived interest for human consumption. Ensuring their safety as food requires assessing their allergenic potential.