13 million patient records, millions of preclinical screening data points, and billions upon billions of recorded chemical structural connections: the sheer amount of data available gives us the ability to propel world health and medical development at an exponential rate. The issue is, however, that much of this data is relayed in unstructured, raw, or inaccessible formats, often trapped in patents, clinical records, or other documents. Therefore, the necessary bridge between drug development and future medicine is artificial intelligence. Today, machine learning in pharmaceutical device development shifts through biomedical data more efficiently in order to create a reliable and predictable blueprint for the entire cycle of preclinical and translational development for new drugs.
One example of the many growing machine learning companies is BioSymetrics. BioSymetrics combines biomedical data and phenotypic screening with machine learning methods. They work with companies that aim to either invent a better drug to treat specific populations or practitioners who look to make improved diagnoses. From pharmacokinetic and pharmacodynamic modeling to analyzing human clinical data, this specific machine learning introduces a new way of gathering, inspecting, and illustrating all available data. The values of BioSymetrics hold direct focus on personalizing medicine and creating connections between the infinite data set and the individual’s unique patient needs.
The growth of artificial intelligence, specifically natural language processing (NLP) algorithms, introduces a means of identifying biologically and chemically relevant elements such as names of drugs, proteins, diseases, and can rapidly scan billions of documents and datasets. The applications are endless, and AI in drug discovery alone is predicted to reach a global value of $1.4 billion by 2024.
Reliability, efficiency, and personalization are the three keys to penetrating the pharmaceutical market. AI is the newest investment across all pharmaceutical companies, specifically in early-stage discovery and developmental phases. By bringing together biodegradability, contraception, and a focus on expanding medical accessibility across the world, Hera Health Solution’s flagship product, Eucontra, hits all the marks. Many of the developing algorithms in machine learning work to find new and innovative uses for the already 4,000+ FDA-approved drugs. As Hera Health expands to new products and markets, it is a possibility that AI could play a role in Hera Health Solution’s innovative process in the future.
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