GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics

Many contemporary cheminformatics methods, including computer-aided de novo drug design, hold promise to significantly accelerate and reduce the cost of drug discovery. Thanks to this attractive outlook, the field has thrived and in the past few years has seen an especially significant growth, mainly due to the emergence of novel methods based on deep neural networks. This growth is also apparent in the development of novel de novo drug design methods with many new generative algorithms now available. However, widespread adoption of new generative techniques in the fields like medicinal chemistry or chemical biology is still lagging behind the most recent developments. Upon taking a closer look, this fact is not surprising since in order to successfully integrate the most recent de novo drug design methods in existing processes and pipelines, a close collaboration between diverse groups of experimental and theoretical scientists needs to be established. Therefore, to accelerate the adoption of both modern and traditional de novo molecular generators, we developed Generator User Interface (GenUI), a software platform that makes it possible to integrate molecular generators within a feature-rich graphical user interface that is easy to use by experts of diverse backgrounds. GenUI is implemented as a web service and its interfaces offer access to cheminformatics tools for data preprocessing, model building, molecule generation, and interactive chemical space visualization. Moreover, the platform is easy to extend with customizable frontend React.js components and backend Python extensions. GenUI is open source and a recently developed de novo molecular generator, DrugEx, was integrated as a proof of principle. In this work, we present the architecture and implementation details of GenUI and discuss how it can facilitate collaboration in the disparate communities interested in de novo molecular generation and computer-aided drug discovery.Medicinal Chemistr

Morphometric analysis of subcortical structures in progressive supranuclear palsy: In vivo evidence of neostriatal and mesencephalic atrophy

Progressive supranuclear palsy (PSP) is a neurodegenerative disease characterized by gait and postural disturbance, gaze palsy, apathy, decreased verbal fluency and dysexecutive symptoms, with some of these clinical features potentially having origins in degeneration of frontostriatal circuits and the mesencephalon. This hypothesis was investigated by manual segmentation of the caudate and putamen on MRI scans, using previously published protocols, in 15 subjects with PSP and 15 healthy age-matched controls. Midbrain atrophy was assessed by measurement of mid-sagittal area of the midbrain and pons. Shape analysis of the caudate and putamen was performed using spherical harmonics (SPHARM-PDM, University of North Carolina). The sagittal pons area/midbrain area ratio (P/M ratio) was significantly higher in the PSP group, consistent with previous findings. Significantly smaller striatal volumes were found in the PSP group - putamina were 10% smaller and caudate volumes were 17% smaller than in controls after controlling for age and intracranial volume. Shape analysis revealed significant shape deflation in PSP in the striatum, compared to controls; with regionally significant change relevant to frontostriatal and corticostriatal circuits in the caudate. Thus, in a clinically diagnosed and biomarker-confirmed cohort with early PSP, we demonstrate that neostriatal volume and shape are significantly reduced in vivo. The findings suggest a neostriatal and mesencephalic structural basis for the clinical features of PSP leading to frontostriatal and mesocortical-striatal circuit disruption. (C) 2011 Elsevier Ireland Ltd. All rights reserved

Advances and Challenges in Computational Target Prediction

Target deconvolution is a vital initial step in preclinical drug development to determine research focus and strategy. In this respect, computational target prediction is used to identify the most probable targets of an orphan ligand or the most similar targets to a protein under investigation. Applications range from the fundamental analysis of the mode-of-action over polypharmacology or adverse effect predictions to drug repositioning. Here, we provide a review on published ligand- and target-based as well as hybrid approaches for computational target prediction, together with current limitations and future directions.Medicinal Chemistr

Dysregulated Prefrontal Cortex Inhibition in Prepubescent and Adolescent Fragile X Mouse Model

Changes in excitation and inhibition are associated with the pathobiology of neurodevelopmental disorders of intellectual disability and autism and are widely described in Fragile X syndrome (FXS). In the prefrontal cortex (PFC), essential for cognitive processing, excitatory connectivity and plasticity are found altered in the FXS mouse model, however, little is known about the state of inhibition. To that end, we investigated GABAergic signaling in the Fragile X Mental Retardation 1 (FMR1) knock out (Fmr1-KO) mouse medial PFC (mPFC). We report changes at the molecular, and functional levels of inhibition at three (prepubescence) and six (adolescence) postnatal weeks. Functional changes were most prominent during early postnatal development, resulting in stronger inhibition, through increased synaptic inhibitory drive and amplitude, and reduction of inhibitory short-term synaptic depression. Noise analysis of prepubescent post-synaptic currents demonstrated an increased number of receptors opening during peak current in Fmr1-KO inhibitory synapses. During adolescence amplitudes and plasticity changes normalized, however, the inhibitory drive was now reduced in Fmr1-KO, while synaptic kinetics were prolonged. Finally, adolescent GABA(A) receptor subunit alpha 2 and GABA(B) receptor subtype B1 expression levels were different in Fmr1-KOs than WT littermate controls. Together these results extend the degree of synaptic GABAergic alterations in FXS, now to the mPFC of Fmr1-KO mice, a behaviourally relevant brain region in neurodevelopmental disorder pathology

Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT

Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more widely available than MR scans, their application is currently limited to the visual assessment of brain integrity and the exclusion of co-pathologies. CT has rarely been used for tissue classification because the contrast between grey matter and white matter was considered insufficient. In this study, we propose an automatic method for segmenting grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume (ICV) from head CT images. A U-Net deep learning model was trained and validated on CT images with MRI-derived segmentation labels. We used data from 744 participants of the Gothenburg H70 Birth Cohort Studies for whom CT and T1-weighted MR images had been acquired on the same day. Our proposed model predicted brain tissue classes accurately from unseen CT images (Dice coefficients of 0.79, 0.82, 0.75, 0.93 and 0.98 for GM, WM, CSF, brain volume and ICV, respectively). To contextualize these results, we generated benchmarks based on established MR-based methods and intentional image degradation. Our findings demonstrate that CT-derived segmentations can be used to delineate and quantify brain tissues, opening new possibilities for the use of CT in clinical practice and research

Ocean model resolution dependence of Caribbean sea-level projections

Abstract Sea-level rise poses severe threats to coastal and low-lying regions around the world, by exacerbating coastal erosion and flooding. Adequate sea-level projections over the next decades are important for both decision making and for the development of successful adaptation strategies in these coastal and low-lying regions to climate change. Ocean components of climate models used in the most recent sea-level projections do not explicitly resolve ocean mesoscale processes. Only a few effects of these mesoscale processes are represented in these models, which leads to errors in the simulated properties of the ocean circulation that affect sea-level projections. Using the Caribbean Sea as an example region, we demonstrate a strong dependence of future sea-level change on ocean model resolution in simulations with a global climate model. The results indicate that, at least for the Caribbean Sea, adequate regional projections of sea-level change can only be obtained with ocean models which capture mesoscale processes.info:eu-repo/semantics/publishe

Water motion and vegetation control the pH dynamics in seagrass-dominated bays

info:eu-repo/semantics/publishe

Tropical biogeomorphic seagrass landscapes for coastal protection:Persistence and wave attenuation during major storms events

The intensity of major storm events generated within the Atlantic Basin is projected to rise with the warming of the oceans, which is likely to exacerbate coastal erosion. Nature-based flood defence has been proposed as a sustainable and effective solution to protect coastlines. However, the ability of natural ecosystems to withstand major storms like tropical hurricanes has yet to be thoroughly tested. Seagrass meadows both stabilise sediment and attenuate waves, providing effective coastal protection services for sandy beaches. To examine the tolerance of Caribbean seagrass meadows to extreme storm events, and to investigate the extent of protection they deliver to beaches, we employed a combination of field surveys, biomechanical measurements and wave modelling simulations. Field surveys of sea- grass meadows before and after a direct hit by the category 5 Hurricane Irma documented that estab- lished seagrass meadows of Thalassia testudinum re- mained unaltered after the extreme storm event. The flexible leaves and thalli of seagrass and calci- fying macroalgae inhabiting the meadows were shown to sustain the wave forces that they are likely to experience during hurricanes. In addition, the seagrass canopy and the complex biogeomorphic landscape built by the seagrass meadows combine to significantly dissipate extreme wave forces, ensuring that erosion is minimised within sandy beach fore- shores. The persistence of the Caribbean seagrass meadows and their coastal protection services dur- ing extreme storm events ensures that a stable coastal ecosystem and beach foreshore is maintained in tropical regions