LIGATE

Today digital revolution is having a dramatic impact on the pharmaceutical industry and the entire healthcare system. The implementation of machine learning, extreme scale computer simulations, and big data analytics in the drug design and development process offer an excellent opportunity to lower the risk of investment and reduce the time to patent and time to patient.

LIGATE aims to integrate and co-design best in class European open-source components together with European Intellectual Properties (whose development has already been co-funded by previous Horizon 2020 projects). It will support Europe to keep worldwide leadership on Computer-Aided Drug Design (CADD) solutions, exploiting today’s high-end supercomputers and tomorrow’s exascale resources, while fostering the European competitiveness in this field.  The project will enhance the CADD technology of the drug discovery platform EXSCALATE.

The proposed LIGATE solution enables to deliver the result of a drug design campaign with the highest speed along with the highest accuracy. This predictability, together with the fully automation of the solution and the availability of the exascale system, will let run the full in silico drug discovery campaign in less than one day to respond promptly for example to worldwide pandemic crisis. The platform will also support European projects in repurposing drugs, natural products and nutraceuticals with therapeutic indications to answer high unmet medical needs like rare, metabolic, neurological and cancer diseases, and emerging ones as new non infective pandemics.  

Since the evolution of HPC architectures is heading toward specialization and extreme heterogeneity, including future exascale architectures, the LIGATE solution focuses also on code portability with the possibility to deploy the CADD platform on any available type of architecture in order not to have a legacy in the hardware.

The project plans to make the platform available and open to support the discovery a novel treatment to fight virus infections and multidrug-resistant bacteria. The project will also make available to the research community the outcome of a final simulation. 

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