Computer assisted drug development

Computer-assisted drug discovery (CADD) is an [indispensable/integral] part of identifying hits and developing leads in the modern pharmaceutical industry. Our in silico portfolio cover high-throughput virtual screening, hit-to-lead-, and lead optimization regarding target affinity, target selectivity, and ADMET properties using docking, molecular dynamics and QSAR/QSPR technologies. 

For unknown target protein structures, our experts build homology models build on a combination of related proteins and experimental data. The same method we apply for molecule development for many other applications, e.g. improving chemical probes for in vitro assays. 

Bioinformatics

With the growth in data volumes and the rapid developments of new assay technologies, bioinformatics is becoming increasingly complex. Our experts combine expertise in bioinformatics, computational biology, data science, and computer science to bring you state-of-the-art bioinformatics solutions to wide range of problems, including:

• Processing pipelines for omics data
• Single-cell data analysis
• Analysis and visualization of omics data
• Design and development of custom databases
• Data wrangling and data integration across private and public data sources
• Custom algorithm development

Machine learning

Machine learning (ML) is the foundational technology in all discovery AI solutions as it allows for the continued and automated learning from large amounts of complex data. Deep Neural Networks (a subset of ML technologies) have proven very effective in modelling extremely complex signals in biologically relevant data such as images, sequencing experiments, and text. Furthermore, ML finds numerous applications outside of the pure AI solutions in e.g., the discovery and validation of diagnostic and prognostic biomarkers and biosignatures. 

Our experts are well versed in ML engineering and in the use and customization of the world-leading the JADBio platform. This powerful and versatile automated ML software enables rapid development and testing of thousands of ML models. Combined with our expertise in selecting and engineering the right features to model your data this allows our consultants to deliver customized AI technology and ML accelerated analysis pipelines at an unprecedented speed. 

Our focus is always on delivering explainable results so that we give your company both powerful predictive models to gain maximum value from your data and also a deep understanding of the important factors which can explain your findings and drive new scientific discoveries. 

Software development

Building your own informatics capabilities may be so difficult or time consuming that you risk getting left behind by the rapid pace of the digital transformation in life-science and healthcare.

We have built and led informatics teams from start-up to corporate level including one of the worlds largest bioinformatics development organisations. We have managed large and complex product portfolios across enterprise offerings, desktop products and SaaS products. The products we have built include research software, decision support software and IVD grade software for instrument support, bioinformatics and cheminformatics.

Our world class team can deliver full-service development of digital products for life-science and bioinformatics including design, software development, deployment, maintenance and life-cycle management. Based on extensive experience we can design and implement advanced algorithms and user interfaces for complex biological and chemical data.

Whether you are building your first digital product or adding to an existing portfolio this gives you momentum, acceleration and security in reaching your digital objectives.

Computational infrastructure

Designing and building a computational infrastructure for bioinformatics analysis is a complex task that requires careful planning and execution. We help you at every step of the way, including:

• Defining the scale and type of analyses and their resource requirements.
• Selecting the appropriate hardware and software to support the analyses.
• Choosing infrastructure platform: Are your needs best met by a local installation, a hosted solution, or a cloud solution?
• Develop a robust and scalable data storage and management system.
• Develop data integration and analysis tools.
• Ensure data security and privacy.
• Establish a plan for growth and maintenance aligned with business neeeds.