Accelerating Vaccine Development with AI

Pharma Tech Outlook: Pharma Tech Magazine

Accelerating Vaccine Development with AI

Stacey Smith, Pharma Tech Outlook | Tuesday, July 28, 2020

Organizations across the world are taking great initiatives to use AI for effective vaccine development.

FREMONT, CA: Conventionally, the vaccine development is very long, which takes around eight to ten years from research and development to market.  Artificial Intelligence (AI) could play a significant role by expediting the overall vaccine test process from years to months by evaluating various scenarios across parameters. AI-based techniques can potentially help to understand the essential part of the microorganisms to determine how they will play an integral role in designing vaccines and accelerate the development of vaccines to combat deadly pandemics. Top 10 Analytical Services Companies - 2020

AI helps in developing a self-learning platform that is nurtured on the historical clinical and pharma data and several other real-time information. Vaccine design initiatives are reliant on the molecular structure of it. Insights are drawn from the data engines to create novel drug candidates or repurpose the already available ones. AI and Machine Learning techniques can be used to predict potential targets and find the most efficient among them based on different parameters like expected response reliability and safety.

Machine learning models can recognize patterns from vast training examples, often in ways that humans would have a very difficult time replicating. The primary and the much-needed strategy at this time is to collate this disparate data so that AI experts can apply their algorithms to derive actionable insights. For this, the world agencies and policymakers need to step up to invite big pharma companies and research labs to join forces with research organizations and combine data sources.

AI is still early in the era of applying machine learning to vaccine design. An AI model is only as good as its training data is trained on much smaller datasets. Generating larger and more diverse datasets will enhance the reliability of immunology models, and their impact on the field is enormous.

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