Industry Perspective

AI in Drug Discovery and Manufacturing

Get an overview of the role of Artificial Intelligence (AI) and Machine Learning (ML) in pharma, biotech, drug development and patient care.

September 15, 2020

MedTech

Improve Efficiency and Increase Speed to Market

Artificial intelligence is redefining the pharma and biotech industries, from drug development to patient care, enhancing the way we develop and manufacture drug treatments. Now more than ever, life science organizations are implementing intelligent technology systems to streamline those processes, improve efficiency and ultimately, increase speed to market.

The global AI in drug discovery market is growing rapidly as organizations address the challenges presented by COVID-19. The growing number of cross-industry collaborations/partnerships, rising costs and the demand for faster speed to market are key factors further influencing adoption.

Significant manufacturing costs also trickle down to patients around the world. Considering that almost half of the individuals in the U.S. have taken at least one prescription drug within the last 30 days, it’s key that organizations find ways to make drug manufacturing as efficient as possible.

What AI-Enabled Systems Can Do:

  • Increase the success rates of new drugs while decreasing operational costs at the same time.
  • Gain greater data insight and analysis helps organizations streamline processes, optimize yield, improve quality and save lives.
  • Transform drug discovery processes and medical research resulting in lowered drug prices in the long-term. For the 10 biggest pharma firms, the costs of new drugs and R&D spend is close to $70 billion annually.
  • Help diminish failure rates in clinical trials and produce superior medicines and drug discovery down from years to months.
  • Optimize yield and quality through data insights, data tracking and predictive analysis.
  • Build real-time predictive models, identify find patterns in manufacturing deviations to prevent their recurrence, and improve output consistency.

Integrating artificial intelligence and machine learning approaches within the life science industry can help alleviate mounting pressure. The more insights you collect through intelligent systems, the faster your teams can work and accelerate critical project development.

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