Generative AI’s ability to analyze data and create new insights has made waves in almost every industry imaginable, from oil & gas to banking and healthcare. Artificial intelligence’s ability to sift through massive amounts of data to draw conclusions makes it incredibly appealing for any sector that has to do so, such as the pharmaceutical industry and drug discovery.
How Generative AI Can Be Used in Drug Discovery
There are an estimated ten to the sixtieth power number of possible drug-like molecules in existence. Obviously, it would be impossible to discover, create, and test all of these molecules. Even starting from a practical base, it can take decades and cost billions to go from concept to clinical trials to public release.
Generative AI can cut through these time and costs, helping create new drugs faster and more affordably. The primary applications for generative AI in drug discovery include:
Molecule Generation
Generative AI can simulate entire molecular structures and how they interact with a patient’s body. This can be used to simulate molecules with desirable properties that are still safe to use in patients. AI models can combine several techniques used to create molecules, drastically accelerating the drug development process.
Antibody Design
Generative AI can be trained on protein sequences and used to create specific antibodies that target specific pathogens. These protein language models can improve the quality and speed of antibody design, and even develop antibodies that are “zero-shot,” meaning they are created without any training data of antibodies known to bind to those specific targets.
Drug Repurposing
By reviewing existing scientific knowledge and documentation with AI, pharmaceutical companies can discover new uses for drugs that have already been approved for public use. This helps companies avoid the typical development costs associated with discovery. For example, the drug semaglutide was originally created to help people manage type 2 diabetes, but was later adopted for weight loss as Ozempic. AI algorithms can even model clinical trials that simulate a wide range of individuals across genders, ethnic groups, comorbidities, and other factors that might influence a drug’s effects on an individual.
De Novo Drug Design
AI models are currently being used to generate and predict entirely new molecular structures that can interact with biological targets. Essentially, it attempts to create drug molecules from scratch rather than modify existing compounds. This approach to chemistry has been applied to atom-based, fragment-based, and reaction-based approaches for creating new structures, giving researchers multiple angles to approach a problem.
Precision Drug Development
Precision drugs are extremely desirable for healthcare, as they can help doctors treat a patient’s condition more accurately than a generic prescription. However, creating custom medication for every individual patient is obviously impractical under the current drug development paradigm. Using generative AI to analyze multiple datasets, such as a patient’s health information, genetics, biobank studies, and more, can help design drugs that are tailored to the patient’s specific needs.
Benefits of Generative AI for Drug Discovery
Using generative AI for drug discovery isn’t simply a matter of using the newest, shiniest tool. It’s the key to developing better drugs and making them faster and cheaper.
Lower Costs
One of the most common complaints about healthcare in general and pharmaceuticals specifically is the cost of medication. Much of that cost comes from the price of developing and testing iteration after iteration of the same drug, trying to amplify its positive effects while mitigating its downsides. Generative AI’s ability to sift through enormous amounts of data to find the right combination of molecules to produce a viable drug cuts down on the dead ends and wasted effort, lowering the cost of creating a new drug.
Faster Time to Market
With an average time of twelve to fifteen years to obtain a novel drug, too many patients are forced to wait for potentially life-saving treatment. Additionally, new drugs and treatments are needed to counter the rising threat of antibiotic-resistant bacteria, an issue that contributes to nearly 5 million deaths every year. This isn’t just a question of a pharmaceutical company’s profit margins, but a matter of life and death for patients around the world. Getting better drugs to the market faster, thanks to generative AI, can save lives.
Greater Treatment Accuracy
If you’ve ever seen an advertisement for a new pharmaceutical drug, you’ve probably seen the laundry list of side effects that they leave for the end of the ad. AI-designed drugs promise to be more accurate and refined, meaning they will have fewer adverse side effects as they work. Precision drugs tailor-made to the specific individual’s body can achieve greater effectiveness, leading to faster treatments and easier recovery.
Challenges for AI-Based Drug Discovery
Like any innovation, artificial intelligence comes with its hurdles to overcome. The most significant of these challenges comes with supporting and using these AI models effectively.
Avoiding AI Hallucinations
One of the biggest weaknesses of AI is that it can “hallucinate” and create incorrect results or outcomes that are impossible to achieve. For example, it might suggest chemical compounds that are physically impossible to create in real-life conditions. The solution to this issue is to use AI models specifically trained on molecules and chemical reactions that are known to be valid, such as Stanford Medicine’s SyntheMol AI. This helps ensure that the AI only suggests drugs that can be created.
Hardware Support
Modern AI models are heavily dependent on parallel processing, which allows them to analyze vast amounts of data simultaneously. However, parallel processing requires specialized computers with the right equipment, such as GPUs specifically designed for this task. Healthcare and pharmaceutical groups interested in using generative AI need to use specialized tools like medical AI box PCs to support it.
Cost of Implementation
As with any new tool, there is a price tag attached to AI. Both the hardware needed to run AI models and the licenses to use them can cost healthcare groups and pharmaceutical companies a pretty penny. One way to ameliorate this cost is to work with an original equipment manufacturer (OEM) for your hardware needs. These companies specialize in customizing products to fit the exact needs of the end-user, which helps you get the performance and features you need without overpaying for things that you don’t.
Embrace AI Drug Discovery with Cybernet Manufacturing
While there are challenges associated with its adoption, using generative AI for drug discovery could revolutionize the pharmaceutical industry, leading to better outcomes for patients around the world.
If your healthcare group or pharmaceutical company needs computer hardware capable of supporting AI models, contact the team at Cybernet Manufacturing today. We offer AI computers equipped with the latest in NVIDIA GPUs that can handle a range of parallel processing tasks, and we can customize our products to better fit your specific requirements.