BenevolentAI platform helps researchers to review millions of papers and abstracts and can organize the information into a network of known facts. The AI tool can generate a large number of possible hypotheses, and these hypotheses can be further analyzed and distilled into several promising hypotheses. This AI tool can raise the efficiency and success rate of pharma research and development.
The following content is cited from https://www.ddw-online.com/why-adopting-ai-is-essential-for-a-sustainable-pharma-industry-1682-201612/ It is estimated that the average academic researcher reads 250-270 articles per year and given that over 50 million scientific articles exist worldwide (3) then no one individual could read all relevant articles in their lifetime. In addition, much information lies buried in text in articles whose titles and abstracts might not reflect that information. Increasing the amount of evidence a researcher can access must improve their decision-making and ability to create new insights into disease. It is here that AI and machine learning technology can make a big impact by enabling the analysis of the sum of all scientific knowledge more quickly and effectively than has previously been possible. It gives researchers the ability to correlate, assimilate and connect all this data, by using cutting edge algorithms to annotate and structure the data. This enables researchers to surface relevant information more rapidly and ask much deeper and broader questions of the scientific literature. It also allows both structured and unstructured data to be brought together in a common format. This not only helps to speed up the process of finding novel targets, ie increasing innovation, but it can also help to choose the most important targets and therefore the hope is that this will reduce the failure rate in clinical development. Given the enormous expense of bringing a drug to market, even a two or four-fold reduction in this attrition would have a dramatic effect on the economics of drug discovery and development as a whole. For example, at BenevolentAI, we have conducted research into Amyotrophic Lateral Sclerosis (ALS) using AI. Our AI platform is able to review billions of sentences and paragraphs from millions of scientific research papers and abstracts. Using this existing information, it has been able to link direct relationships between the data, organising it into ‘known facts’. These known facts are curated and unrealised connections are made, helping to generate a large number of possible hypotheses using a set criterion put in place by the scientists. By using AI, we were able to generate 200 hypotheses, using a volume of data that they would have struggled to access previously. Once further assessed by our tools – in silico validation – a shorter list of 20 novel hypotheses were further explored and prioritised. Ultimately we whittled these down to five hypotheses which were then tested in an academic lab of experienced ALS researchers. This process has allowed us to explore the potential for new mechanisms for disease modification in a timescale of weeks rather than months. Of the drugs that were tested in the in vitro assay system used, one did not work but the other four did. The drugs that were tested were all ones that had previously been used in different indications than ALS and therefore offer some opportunities for repurposing. Further work is ongoing in an animal model of ALS.
Pharma industry is an extremely important area for modern society, especially in 2020, when the whole world is fighting with covid-19. AI technology can boost the development of pharmacy and save more lives.