From the course: How AI Is Transforming Pharma: From R&D to Care Delivery

AI-powered drug discovery

From the course: How AI Is Transforming Pharma: From R&D to Care Delivery

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AI-powered drug discovery

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- What if I told you we could predict which drugs will work before a single experiment is run? No more guesswork, no more years of trial and error, just answers. Well, we're actually there. Drug discovery has always been a long, expensive, high stakes roller coaster. But, now, AI is shaking things up and saving researchers tons of time and money in the process. Let me explain. To fully appreciate what is happening now, you need to understand how proteins work in the body. Wait, what? I thought this was a course on AI. Hang with me, I promise it'll be important. So, proteins are like tiny little machines inside our cells, and if scientists don't understand their shape, they can't develop the right medicines. Now, computers, AI, are helping scientists figure out how these tiny little machines work. Computers can see how the pieces fit together, kind of like putting together a puzzle. For example, one company doing this is Google DeepMind. They've created AlphaFold. It can reverse engineer the shape of a tiny machine, that is proteins, just by looking at its tiny little parts, also known as amino acids. That used to take years of lab work. I'm talking what used to be years now takes minutes. AlphaFold helps scientists crack the code on how these proteins fold and twist into shapes through computer simulations. Think virtual chemistry lab. This helps scientists find important pieces that could be therapeutic targets for new treatments. So, instead of starting off by testing 10,000 candidates, they're zeroing in on 100 of the most promising ones from the start. These applications have contributed directly to the rise of TechBio, new AI-first companies. An evolution from the traditional biotech. These companies are built from the ground-up to harness massive multi-omic datasets as foundational tools for drug discovery. Instead of relying on slow, linear R&D models, these companies use predictive algorithms and computational screening platforms to design, simulate, and iterate in record time. By doing this, they cut down on the time and money spent in the wet lab. They're not just speeding up the science, they're reinventing how we discover and develop medicines altogether. And get this, AI is not just finding new drugs, it's finding new uses for old ones, which can lead to label expansions. It's called, drug repurposing. You take an existing medication and ask, hey, could this work for something else? Turns out, sometimes. AI digs through loads of past studies, patient records, you name it, to find hidden connections. It can spot potential safety risks, helping researchers focus on the compounds that are most likely to succeed in a new context. One company doing this is Owkin AI. They built a platform called, DrugMATCH. It uses public research, real-world patient data, and something called, graph machine learning to figure out how diseases, drugs, and patient groups are all intricately linked. Basically, it builds a massive knowledge map, identifying patterns that explain why certain targets were successful in past research. Then it matches existing drugs with different disease areas or patient groups to uncover new treatment possibilities. So, yeah, AI isn't just speeding up drug discovery, it's making it smarter. It's helping scientists work with precision to reduce failure rates and get medicines to patients sooner. With AI, we don't have to roll the dice and wait for breakthroughs. We're engineering them with intention.

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