Modelling trace metal extractability and solubility in French forest soils by using soil properties

Soil/solution partitioning of trace metals (TM: Cd, Co, Cr, Cu, Ni, Sb, Pb and Zn) has been investigated in six French forest sites that have been subjected to TM atmospheric inputs. Soil profiles have been sampled and analysed for major soil properties, and CaCl2-extractable and total metal content. Metal concentrations (expressed on a molar basis) in soil (total), in CaCl2 extracts and soil solution collected monthly from fresh soil by centrifugation, were in the order: Cr > Zn > Ni > Cu > Pb > Co > Sb > Cd , Zn > Cu > Pb = Ni > Co > Cd > Cr and Zn > Ni > Cu > Pb > Co > Cr > Cd > Sb , respectively. Metal extractability and solubility were predicted by using soil properties. Soil pH was the most significant property in predicting metal partitioning, but TM behaviour differed between acid and non-acid soils. TM extractability was predicted significantly by soil pH for pH < 6, and by soil pH and Fe content for all soil conditions. Total metal concentration in soil solution was predicted well by soil pH and organic carbon content for Cd, Co, Cr, Ni and Zn, by Fe content for Cu, Cr, Ni, Pb and Sb and total soil metal content for Cu, Cr, Ni, Pb and Sb, with a better prediction for acidic conditions (pH < 6). At more alkaline pH conditions, solute concentrations of Cu, Cr, Sb and Pb were larger than predicted by the pH relationship, as a consequence of association with Fe colloids and complexing with dissolved organic carbon. Metal speciation in soil solutions determined by WHAM-VI indicated that free metal ion (FMI) concentration was significantly related to soil pH for all pH conditions. The FMI concentrations of Cu and Zn were well predicted by pH alone, Pb by pH and Fe content and Cd, Co and Ni by soil pH and organic carbon content. Differences between soluble total metal and FMI concentrations were particularly large for pH < 6. This should be taken into account for risk and critical load assessment in the case of terrestrial ecosystems

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery