Avi agarwal venture for america9/13/2023 Many companies are already working with AI and are aware of the practical steps for integrating it into their operations and leveraging its power. Now they search BenchSci in minutes and then order and test one to three reagents before choosing one (conducting fewer tests over fewer weeks). Previously, scientists would often use Google or PubMed to search the literature (a process that took days), then read the literature (again spending days), and then order and test three to six reagents before choosing one (over a period of weeks). Just as Google can help you figure out how to fix your dishwasher and save you a long trip to the library or a costly repair service, BenchSci helps scientists identify a suitable reagent without incurring the trouble or expense of excessive research and experimentation. What is remarkable here is that BenchSci, in its specialized domain, is doing something akin to what Google has been doing for the whole of the internet: using machine learning to lead in search. In addition, many lives could be saved by bringing new drugs to market more quickly. That adds up to potential savings of over $17 billion annually, which, in an industry where the returns to R&D have become razor-thin, could transform the market. Identifying those by combing through the published literature rather than rediscovering them from scratch helps significantly cut the time it takes to produce new drug candidates. More specifically, they could use the technology to find the right biological reagents-essential substances for influencing and measuring protein expression. Indeed, BenchSci found that if scientists took advantage of machine learning that read, classified, and then presented insights from scientific research, they could halve the number of experiments normally required to advance a drug to clinical trials. BenchSci realized that scientists could conduct fewer of these-and achieve greater success-if they applied better insights from the huge number of experiments that had already been run. To get a new drug candidate into clinical trials, scientists must run costly and time-consuming experiments. It aims to make it easier for scientists to find needles in haystacks-to zero in on the most crucial information embedded in pharma companies’ internal databases and in the vast wealth of published scientific research. It has also spurred start-ups to launch new products and platforms, sometimes even in competition with Big Tech.Ĭonsider BenchSci, a Toronto-based company that seeks to speed the drug development process. This technique for taking data inputs and turning them into predictions has enabled tech giants such as Amazon, Apple, Facebook, and Google to dramatically improve their products. The past decade has brought tremendous advances in an exciting dimension of artificial intelligence-machine learning.
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