BioAscent, Selcia Collaboration Enhances Online Collection
News Aug 02, 2016
BioAscent Discovery and Selcia have entered into an agreement to make Selcia’s unique compound fragment library available to researchers through BioAscent’s new online Compound Cloud service. The new agreement adds to the 125,000+ compounds available through Compound Cloud and stored at BioAscent’s state-of-the-art compound management and logistics facility. Scientists can now benefit from immediate access to Selcia’s 1366 fragment collection, which has been designed in collaboration with Cambridge MedChem Consulting. The collection contains a structurally-diverse range of compounds with little overlap with other commercially-available libraries consisting of both commercial and non-commercial custom synthesised fragments. All have been put through a stringent quality control process to ensure chemical attractiveness and stability. Customers can order ready-to-use, single-use sets for rapid despatch through Compound Cloud.
Compound Cloud promises to significantly enhance early-stage drug discovery, by enabling easy online selection and ordering of a diverse collection of IP-free, high quality chemicals for screening, including an increasing number of third party collections. It applies the cloud-computing concept to early stage drug discovery by enabling scientists to remotely pick and choose specific compounds of interest from BioAscent’s storage facility and have them quickly prepared and delivered for immediate use. By removing the need for organisations to expend resources in developing or acquiring compound collections, compound screening through Compound Cloud becomes highly cost- and time-efficient.
Archaeology researchers are benefitting from the University’s first high performance computing (HPC) system. Revolutionising the capacity for data collation, the HPC cluster enables the archaeological team to effectively preserve endangered or destroyed heritage across the world, the Temple of Bel in Palmyra, Kathmandu and Notre Dame.
North Carolina State University researchers have developed a new framework for building deep neural networks via grammar-guided network generators. In experimental testing, the new networks - called AOGNets - have outperformed existing state-of-the-art frameworks, including the widely-used ResNet and DenseNet systems, in visual recognition tasks.