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

Floodplains Provide Important Amphibian Habitat Despite Multiple Ecological Threats

Floodplain ponds and wetlands are productive and biodiverse ecosystems, yet they face multiple threats including altered hydrology, land use change, and non-native species. Protecting and restoring important floodplain ecosystems requires understanding how organisms use these habitats and respond to altered environmental conditions. We developed Bayesian models to evaluate occupancy of six amphibian species across 103 off-channel aquatic habitats in the Chehalis River floodplain, Washington State, USA. The basin has been altered by changes in land use, reduced river–wetland connections, and the establishment of non-native American bullfrogs (Rana catesbeiana = Lithobates catesbeianus) and centrarchid fishes, all of which we hypothesized could influence native amphibian occupancy. Despite potential threats, the floodplain habitats had relatively high rates of native amphibian occupancy, particularly when compared to studies from non-floodplain habitats within the species’ native ranges. The biggest challenge for native amphibians appears to be non-native centrarchid fishes, which strongly reduced occupancy of two native amphibians: the northern red-legged frog (Rana aurora) and the northwestern salamander (Ambystoma gracile). Emergent vegetative cover increased occupancy probability for all five native amphibian species, indicating that plant management may offer a strategy to counter the negative effect of centrarchids by providing refuge from predation. We found that temporary and permanent hydroperiod sites supported different species; hence, both should be conserved on the landscape. Lastly, human-created and natural ponds had similar amphibian occupancy patterns, suggesting that pond construction offers a viable strategy for adding habitats to the floodplain landscape. Overall, floodplain ponds and wetlands provide important amphibian habitat, and we offer management strategies that will bolster amphibian occupancy in an altered floodplain landscape

Holgerson_Ecosphere_Dataset

This dataset represents naive detection of six amphibian species from ponds and wetlands within the Chehalis River Basin, Washington State, USA. In addition to amphibian observations, the dataset includes environmental covariates, such as fish presence, forested land use, emergent vegetation, and day of year

Accuracy of climate change predictions using high resolution simulations as surrogates of truth

How accurate are predictions of climate change from a model which is biased against contemporary observations? If a model bias can be thought of as a state-independent linear offset, then the signal of climate change derived from a biased climate model should not be affected substantially by that model's bias. By contrast, if the processes which cause model bias are highly nonlinear, we could expect the accuracy of the climate change signal to degrade with increasing bias. Since we do not yet know the late 21st Century climate change signal, we cannot say at this stage which of these two paradigms describes best the role of model bias in studies of climate change. We therefore study this question using time-slice projections from a global climate model run at two resolutions - a resolution typical of contemporary climate models and a resolution typical of contemporary numerical weather prediction - and treat the high-resolution model as a surrogate of truth, for both 20th and 21st Century climate. We find that magnitude of the regionally varying model bias is a partial predictor of the accuracy of the regional climate change signal for both wind and precipitation. This relationship is particularly apparent for the 850 mb wind climate change signal. Our analysis lends some support to efforts to weight multi-model ensembles of climate change according to 20th Century bias, though note that the optimal weighting appears to be a nonlinear function of bias. Copyright © 2011 by the American Geophysical Union

Modelling Freshwater Resources at the Global Scale: Challenges and Prospects

Quantification of spatially and temporally resolved water flows and water storage variations for all land areas of the globe is required to assess water resources, water scarcity and flood hazards, and to understand the Earth system. This quantification is done with the help of global hydrological models (GHMs). What are the challenges and prospects in the development and application of GHMs? Seven important challenges are presented. (1) Data scarcity makes quantification of human water use difficult even though significant progress has been achieved in the last decade. (2) Uncertainty of meteorological input data strongly affects model outputs. (3) The reaction of vegetation to changing climate and CO2 concentrations is uncertain and not taken into account in most GHMs that serve to estimate climate change impacts. (4) Reasons for discrepant responses of GHMs to changing climate have yet to be identified. (5) More accurate estimates of monthly time series of water availability and use are needed to provide good indicators of water scarcity. (6) Integration of gradient-based groundwater modelling into GHMs is necessary for a better simulation of groundwater-surface water interactions and capillary rise. (7) Detection and attribution of human interference with freshwater systems by using GHMs are constrained by data of insufficient quality but also GHM uncertainty itself. Regarding prospects for progress, we propose to decrease the uncertainty of GHM output by making better use of in situ and remotely sensed observations of output variables such as river discharge or total water storage variations by multi-criteria validation, calibration or data assimilation. Finally, we present an initiative that works towards the vision of hyper resolution global hydrological modelling where GHM outputs would be provided at a 1-km resolution with reasonable accuracy