Wood-loving magic mushrooms from Australia are saprotrophic invaders in the Northern Hemisphere.

Magic mushrooms are fungi that produce psilocybin, an entheogen with long-term cultural use and a breakthrough compound for treatment of mental health disorders. Fungal populations separated by geography are candidates for allopatric speciation, yet species connectivity typically persists because there is minimal divergence at functional parts of mating compatibility genes. We studied whether connectivity is maintained across populations of a widespread species complex of magic mushrooms that has infiltrated the Northern Hemisphere from a hypothesised centre of origin in Australasia.

Balancing Molecular Size, Activity, Permeability, and Other Properties: Drug Candidates in the Context of Their Chemical Structure Optimization.

Chemical structure optimization is a vital part of early drug discovery projects. Starting with compounds that show activity on the target of interest, the chemical structures are subsequently optimized toward a development candidate (DC) molecule with the best chances of clinical success. However, the DCs in the context of such optimization programs, as well as detailed characterization of major limiting factors, have not been investigated in detail so far.

() . in Australia: Neither a nor a .

is described as a new porphyrellus-like genus of to accommodate , a dark brown to dull lilac/violet, or rarely, nearly black bolete with a series of oxidation reactions progressing from blue to red then nearly black and a dark brown spore deposit. Idiosyncratic blue-green pigment encrustations (cyanogranules) and a similarly colored reaction of the hyphae located on pileus and stipe surfaces are also diagnostic. Phylogenetic analyses of nuclear large-subunit rDNA (nrLSU), translation elongation factor 1-alpha (), and the second largest subunit of RNA polymerase II () infer as a unique generic lineage with two species, one of which is newly described (.

Prediction of Small-Molecule Developability Using Large-Scale ADMET Models.

Early assessment of the potential of a series of compounds to deliver a drug is one of the major challenges in computer-assisted drug design. The goal is to identify the right chemical series of compounds out of a large chemical space to then subsequently prioritize the molecules with the highest potential to become a drug. Although multiple approaches to assess compounds have been developed over decades, the quality of these predictors is often not good enough and compounds that agree with the respective estimates are not necessarily druglike.

MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information.

Federated multipartner machine learning has been touted as an appealing and efficient method to increase the effective training data volume and thereby the predictivity of models, particularly when the generation of training data is resource-intensive. In the landmark MELLODDY project, indeed, each of ten pharmaceutical companies realized aggregated improvements on its own classification or regression models through federated learning. To this end, they leveraged a novel implementation extending multitask learning across partners, on a platform audited for privacy and security.