Ingenious e-Brain Solutions forecasts a considerable trend for “AI democratization” where various machine learning/deep learning technologies become available in pre-trained, pre-configured “off-the-shelf” formats, or relatively ready-to-use formats — via cloud-based models, frameworks, and drag-and-drop AI-pipeline building platforms (for example, KNIME), companies, like Google, Tencent, Nvidia, Microsoft, and others, are expanding into healthcare space via not only offering specialized services and tools to pharma counterparties but also directly investing into biotech start-ups and larger companies, creating own centres of excellence and commercial subsidiaries with focus on Life Sciences.
AI is regarded by some top executives at big pharma (Novartis, Pfizer, GSK and others) as a tool to uncover not only new molecules but also new targets. The ability of deep neural networks to build ontologies from multimodal data (e.g., “omics” data) is believed to be among the most disruptive areas for AI in drug discovery, alongside data mining from unstructured data, like text (using natural language processing, NLP).
In this report, the use of artificial intelligence or any other computational algorithm for drug discovery, biomarker development, and advanced R&D is highlighted along with technology providers in pharma industry, computational methods used by the most advanced AI companies, an overall growth of investment and business development activity in the area of pharmaceutical AI along with some other sections which are listed in the table of content of the report.
The technology providers in Pharmaceutical Industry are profiled in the report into 6 categories: Artificial Intelligence (R&D Platforms), Quantum Computing, Supporting Services, “Big Tech” Corporations, Autonomous Labs, and Big Data Providers.
IEBS has also highlighted certain challenges which are faced by the pharma sector during drug discovery and their solutions that could be implemented.
A new drug takes an average of ten years to reach the market. According to Tufts University’s Center for the Study of Medicine Development (CSDD), the cost of producing a new prescription drug that receives FDA clearance is around $2.6 billion as of 2014. The pharmaceutical industry is reaching the end of its life cycle, and the returns on new treatments that do make it to market no longer justify the vast investments that pharma makes in R&D.
More details @ https://www.iebrain.com/reports/artificial-intelligence-in-bio-pharmaceuticals/
