In the U.S., drug companies spend more than $50 billion on R&D, while in Europe spending surpasses €30 billion. To help lower costs and to reduce the time taken for new drugs to hit the market, the pharmaceutical sector is turning to machine learning.
The pharmaceutical regulatory environment is becoming more challenging, and drugs must go through extensive testing before they hit the market. As a result, there are major incentives for drug companies to reduce R&D spending in order to free up funds for additional ventures and offer lower prices for their products.
By adopting sophisticated data science, and machine learning, pharmaceutical researchers can save money and time on R&D. On top of that, machine learning technology provides new ways for drug companies to streamline nearly every other aspect of their businesses.
This article highlights the scope for developing therapeutics with new innovative, alternative approaches. The current trillion dollars pharmaceutical industry is in peril where the current drug development pipeline is slow, inefficient and incapable of being extended to multi-combination drug therapies as well as minimally focused on prevention. The complex modeling of diseases and biological functions has been limited because of the inability to integrate large scale molecular pathways.
Watch this Video to understand how recent advances provide breakthrough technology for doing scalable modeling of complex molecular systems to dramatically accelerate drug discovery and development.