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Cloud platform advances drug research


A pioneering digital platform launched today will help accelerate the development of breakthrough drugs.

Global Intellectual Property Analytics company, PatSnap, has announced the new Chemical by PatSnap, a Software as-a-Service (SaaS) product, is now available.

It will speed up vital research by giving intellectual property (IP) stakeholders – leading science-focussed organisations – access to a comprehensive database of 121 million patents and 114 million ‘chemical structures, clinical trial information, regulatory details, and toxicity data’. By tapping into big data via machine learning and artificial intelligence (AI) scientists and other professionals will be able to quickly confirm the credibility of their chemical development projects.

Sequoia-backed PatSnap is providing critical support to companies worldwide that are routinely spending in excess of US$1 billion on R&D annually. NASA and MIT are just two of the global innovation giants that rely on PatSnap’s research tools to carry out their groundbreaking work.

Ali Hussein, UK Product Leader at PatSnap, said: “The main challenges in R&D are that companies use resources in a way that’s not productive, for example hiring people to do studies and accumulate lots of data, but at the end of the day, they do not assimilate all that information into a coherent strategy. Successful commercialisation of a drug is expensive and fraught with high risk. Estimated costs can rise to as much as $2.6 billion, while 14 drug candidates will fail clinical trials for every one that makes it to market. Current strategies have not been able to bring down the costs of Research and Development, and the pressure to adopt value-based and outcome-based pricing models has rapidly intensified.”

“It’s a well-established principle that Big Data holds the potential to address these problems, but until now it has been difficult to extract this information from the worlds of chemistry and innovation intelligence in either a cost-effective or resource-efficient way. Particularly challenging is the accurate integration of multiple relevant data sets and the skill set required to analyse and interpret results.”




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