The pharmaceutical business is one of the riskiest industries to venture into. Drug discovery is an artisanal process where a carefully designed drug takes about 10 years and approximately 2.5 billion dollars to be approved and launched into the market. The complexity of biological systems places the odds at a ridiculous failure rate of 90%. In recent years, the declining efficiency of the R&D efforts has put the pharma industry on its toes.
In the past decade, Artificial Intelligence (AI) has already revolutionized several industries, including automotive, entertainment and fintech. AI dictates routes and ETA on google maps, executes multiple stock exchange transactions, enables facial recognition, and powers the voice assistants Siri and Alexa. However, the adoption of AI in pharma has been restricted due to limited data available about what works (the successful 10%) and the innate complexity of the process of drug discovery.
Cambridge, UK, September 29th 2020 - Following on from June's breakthrough news that OKRA hit its target of 2 million validated predictions at >95% accuracy a full 6 months early, the company has set its sights on a record-breaking 6 million predictions completed by year end. This means that pharma companies have benefitted from an unparalleled level of support during the most uncertain time in recent memory, using predictions to guide their staff to take the right next step. This is 1 million more than the revised target set only in June.
The application of next-generation data analytics tools, powered by machine learning and artificial intelligence (AI) components, has become a long-term strategic priority for most companies in the pharmaceutical and biotech industries. However, such systems have to make sure the organisational data is findable, accessible, interoperable, and reusable across different sub-systems, applications, departments, teams, and even companies.
Aigenpulse, a technology company at the forefront of data management and analytics in the Life Science industry, has built a portfolio of tools for working with organisational research data at scale and accelerating the discovery and development of better targets and candidates using advanced machine learning technologies.
11th September 2020: Life science and data technology innovator, Aigenpulse, is launching its CytoML Experiment Suite – an automated, end-to-end, machine learning solution specifically aimed at streamlining and automating cytometry analysis at scale and replacing manual gating processes. With it, users will benefit from a single point-of-truth about all cytometry data across any organisation.