Development of a machine learning detector for North Atlantic humpback whale song.

The study of humpback whale song using passive acoustic monitoring devices requires bioacousticians to manually review hours of audio recordings to annotate the signals. To vastly reduce the time of manual annotation through automation, a machine learning model was developed. Convolutional neural networks have made major advances in the previous decade, leading to a wide range of applications, including the detection of frequency modulated vocalizations by cetaceans.

A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning.

Fast and accurate molecular force field (FF) parameterization is still an unsolved problem. Accurate FF are not generally available for all molecules, like novel druglike molecules. While methods based on quantum mechanics (QM) exist to parameterize them with better accuracy, they are computationally expensive and slow, which limits applicability to a small number of molecules.

Protocol for the Wessex AsThma CoHort of difficult asthma (WATCH): a pragmatic real-life longitudinal study of difficult asthma in the clinic.

Background: Asthma is now widely recognised to be a heterogeneous disease. The last two decades have seen the identification of a number of biological targets and development of various novel therapies. Despite this, asthma still represents a significant health and economic burden worldwide.

Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors.

Fragment-based drug discovery (FBDD) has become a mainstream approach in drug design because it allows the reduction of the chemical space and screening libraries while identifying fragments with high protein-ligand efficiency interactions that can later be grown into drug-like leads. In this work, we leverage high-throughput molecular dynamics (MD) simulations to screen a library of 129 fragments for a total of 5.85 ms against the CXCL12 monomer, a chemokine involved in inflammation and diseases such as cancer.

Drug Discovery and Molecular Dynamics: Methods, Applications and Perspective Beyond the Second Timescale.

Bio-molecular dynamics (MD) simulations based on graphical processing units (GPUs) were first released to the public in the early 2009 with the code ACEMD. Almost 8 years after, applications now encompass a broad range of molecular studies, while throughput improvements have opened the way to millisecond sampling timescales. Based on an extrapolation of the amount of sampling in published literature, the second timescale will be reached by the year 2022, and therefore we predict that molecular dynamics is going to become one of the main tools in drug discovery in both academia and industry.

RAPPORT: running scientific high-performance computing applications on the cloud.

Cloud computing infrastructure is now widely used in many domains, but one area where there has been more limited adoption is research computing, in particular for running scientific high-performance computing (HPC) software. The Robust Application Porting for HPC in the Cloud (RAPPORT) project took advantage of existing links between computing researchers and application scientists in the fields of bioinformatics, high-energy physics (HEP) and digital humanities, to investigate running a set of scientific HPC applications from these domains on cloud infrastructure. In this paper, we focus on the bioinformatics and HEP domains, describing the applications and target cloud platforms.

SPECTRa-T: machine-based data extraction and semantic searching of chemistry e-theses.

The SPECTRa-T project has developed text-mining tools to extract named chemical entities (NCEs), such as chemical names and terms, and chemical objects (COs), e.g., experimental spectral assignments and physical chemistry properties, from electronic theses (e-theses).

SPECTRa: the deposition and validation of primary chemistry research data in digital repositories.

The SPECTRa (Submission, Preservation and Exposure of Chemistry Teaching and Research Data) project has investigated the practices of chemists in archiving and disseminating primary chemical data from academic research laboratories. To redress the loss of the large amount of data never archived or disseminated, we have developed software for data publication into departmental and institutional Open Access digital repositories (DSpace). Data adhering to standard formats in selected disciplines (crystallography, NMR, computational chemistry) is transformed to XML (CML, Chemical Markup Language) which provides added validation.

WEDS: a Web services-based environment for distributed simulation.

Web services have the potential to radically enhance the ability of researchers to make use of distributed computing resources, but jargon and a plethora of standards make their use almost impossible for the scientist without prior experience of the necessary technologies. A powerful and simple WSRF-based middleware scheme is presented, designed to let scientists remotely deploy single or multiple instances of a pre-existing code across multiple resources, and giving steering, visualization and workflow functionality with only simple modifications to program code. It is hoped that the development and implementation of such a toolkit will be relevant not only to the problem of deploying workstation-class codes in real time, but also the move towards more tractable alternatives to the Globus toolkit for deployment of processes in a high-performance computing environment.