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The pharma industry is embracing artificial intelligence (AI) to streamline drug discovery and development and is rapidly expanding. Back in 2019, it was reported that 132 startups were using AI in drug discovery and today that number has grown to 491 according to status-insights.com (see here). The global “AI in drug discovery” market was worth about $1.1 billion last year but should grow at a 30% clip from 2023 to 2030, according to Grand View Research. Source
This is a one-of-a-kind original article by Lorimer Wilson, Managing Editor of munKNEE.com – Your KEY To Making Money! A more lengthy version of this article was posted here.
Making a new drug is like finding a needle in a haystack
Making a new drug is like finding a needle in haystack – and AI can find that needle in that haystack very, very quickly – possibly measured in days rather than years:
- AI can map out human genetic data in an instant,
- AI can identify mutations in that data in an instant,
- AI can run simulations of compounds to fight those mutations in an instant.
That effort is well underway:
- Research firm Deep Pharma Intelligence estimates that investments in the field of AI-powered drug discovery have tripled over the past four years to nearly $25 billion.
- Morgan Stanley believes that AI-powered drug discovery will lead to an additional 50 novel therapies being brought to market over the next decade, with annual sales in excess of $50 billion! In other words, a $50 billion AI drug discovery revolution is underway.
Source: Deep Pharma Intelligence
The Clinical Trial Process
New drugs are currently approved through human clinical trials: rigorous, year-long procedures starting in animal trials and gradually moving to patients in trials who are exposed to side effects that cannot be predicted or expected. The process typically cost billions of dollars and take many years to complete, sometimes more than a decade, and, even if their trials are successful, they still have to receive approval of a country’s respective regulatory agency. Source
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Source: Biosourcing
Why Use AI?
- In its simplest form, AI is just a number of machine learning algorithms scouring millions of data to parse and analyze every possible connection in that data so as to draw meaningful conclusions from – and make predictions based on – that data.
- Luke Lango of incestorplace.com summarizes the above by saying “That’s really all AI is – it is just algorithms and data, with data acting as the “fuel” of the model and, much like the amount of fuel in the tank determines how far a car can drive, the quantity and quality of data an AI model has access to ultimately determines the quality of the AI model itself. The more data, the better the AI. It’s that simple.
- When it comes to the human body, there is no dearth of quality data. Humans – like computers – are really nothing more than a bunch of data strung together – a bunch of As, Gs, Cs, and Ts strung together – or the four base types found in human DNA molecules – with each determining a person’s characteristics, traits, and even actions and when AI is applied to all that data it will have a huge impact on the medical industry.” Source
Source: NCRAD
- Artificial intelligence technology, however, helps companies aggregate and synthesize a lot of information that’s needed for clinical trials, thus shortening the drug development process. It can also help understand the mechanisms of the disease, establish biomarkers, generate data, models, or novel drug candidates, design or redesign drugs, run preclinical experiments, design and run clinical trials, and even analyze the real-world experience. Source
- A study by Janssen Research & Development (JNJ arm) concludes that the AI method to be up to 250 times more efficient than the traditional method of drug discovery. It holds the potential to reduce timelines for drug discovery, to increase accuracy of predictions on efficacy and safety as well as to create better, and more, opportunities to diversify drug pipelines.
To learn which 10 AI in drug discovery and drug development clinical-stage companies trade on the various Canadian and American stock exchanges continue reading…