The brain is one of our most crucial organs, controlling survival functions, learning, memory, and even movement. Some diseases can affect the functionality of the brain. An important disease that can diminish brain function is Alzheimer’s disease. This condition causes a progressive degeneration of the brain, leading to dementia and other complications. Researchers have found that the use of generative AI can be useful in designing new drug candidates for people who have Alzheimer’s disease. Let’s take a look at the disease and review the latest research on tech and treatment options.
Overview of Alzheimer’s Disease
Alzheimer’s disease is a relatively common condition among older individuals. It is a type of progressive brain disease. Over time, it affects the physiological structure of the brain, which is why it’s also considered a degenerative condition. The disease eventually destroys certain cognitive skills we depend on, including our ability to think and remember things [1].
This leads to dementia. In fact, Alzheimer’s disease is considered to be the most common type of dementia. Dementia is considered a complication of Alzheimer’s disease instead of a condition on its own.
There are other conditions that can also lead to dementia as a symptom, including cerebrovascular disease, frontotemporal degeneration, Lewy body disease, and Hippocampal sclerosis, amongst others.
The Alzheimer’s Disease International Association reports that in 2020 [2], more than 55 million people around the world had AD. They also estimate that the number of cases is likely to double every two decades. By 2030, it’s expected that the cases of Alzheimer’s disease will rise to 78 million.
Symptoms of Alzheimer’s Disease
One of the most concerning matters related to Alzheimer’s disease is that the disease can start to develop around 20 years before a person begins to notice symptoms like memory loss [3]. This means, by the time it is diagnosed, the disease has already progressed in many cases.
Memory loss is considered a hallmark symptom of Alzheimer’s disease. It starts gradually, but gets worse over time. You begin to notice you’re forgetting where you left the keys. Then you can’t recall birthdays or dialing a number that you usually easily remember gets difficult. Over time, the memory loss begins to interfere with your daily life.
This isn’t the only symptom that shows up with Alzheimer’s. People with AD also experience:
- Mood changes: Many people with AD have fluctuations in their mood. The disease can also affect their personality. Research has also linked AD to a higher incidence of mental disorders, including depression [4] and anxiety.
- Confusion: It’s also common to see confusion and disorientation in people with Alzheimer’s disease. They may become confused about the date, where they are, and other factors.
- Daily tasks: Over time, Alzheimer’s disease can begin to have a negative impact on a person’s ability to do their daily tasks. Managing finances or even simple tasks like following a basic recipe can become difficult for them to complete properly.
Decline in physical health: As AD causes structural changes in the brain, it’s also possible to notice physical decline in these individuals. Their ability to be in control of their body can become diminished. When confusion, mood changes, and memory loss start to become more severe, it also makes it difficult for a person to practice self-care.
Causes of Alzheimer’s Disease
The specific cause of Alzheimer’s disease is not well understood. There are several research papers that have outlined contributing factors, with age being a particularly important one. The disease is most common in people who are over 65 years old. Among those aged 85 and older, about 33% develop AD [5].
While age is a crucial risk factor to consider, there are also other elements that can contribute to the development of Alzheimer’s:
- Health problems: Researchers have found an increased risk for AD among people who are obese, and those with diabetes and hypertension.
- Lifestyle factors: People who live sedentary lives and those who smoke also seem to be at a higher risk for AD.
- Genetic factors: Genetics also play a role in the development of the disease. It’s not directly inherited. However, there does seem to be a higher chance of developing Alzheimer’s disease if it’s a condition that runs in a person’s family. There are also certain genetic mutations that have been linked to an early onset of the condition.
- Environmental factors: Exposure to certain environmental factors, such as pollutants and toxins, may also be contributing factors to AD.
Diagnostics and Treatment
The earlier Alzheimer’s disease is diagnosed and treated, the better the overall outlook. Unfortunately, it’s often diagnosed later on.
There are several tests that a neurologist will generally perform to confirm an AD diagnosis. This includes a mental status exam, neuropsychological testing, and neuroimaging. Additional tests may also be performed to rule out other potential causes for the existing symptoms. These tests all help the care provider get a better idea of the patient’s brain and overall health, as well as their mental status.
There isn’t a complete cure for Alzheimer’s disease. However, treatment is available to help slow down the progression of the disease. Some treatments may include:
- Donepezil
- Galantamine
- Rivastigmine
These medicines are called cholinesterase inhibitors.
There are also newer treatment options that the FDA has approved. These include options like Donanemab and Lecanemab, which focus on addressing amyloid plaques identified in the brain. These plaques are associated with the development and progression of Alzheimer’s disease.
Current Challenges in Treating Alzheimer’s Disease
There are several challenges the medical industry is currently facing when it comes to the treatment of AD. One of the major concerns right now is the fact that treatments focus on symptomatic relief. They don’t modify the disease. This means they simply slow down the progression of the deterioration that happens in people with Alzheimer’s.
There’s also the concern about the side effects people tend to experience with the current FDA-approved treatments for Alzheimer’s disease. For example, researchers have raised concerns about the potential for brain swelling and even bleeding in some of the newer drugs used for AD patients.
Another challenge remains in the diagnostics timeline. The disease is often only diagnosed when it has already progressed significantly. This calls for a need to both recognize and intervene at earlier stages. When diagnosed at a later time, treatments are not as efficient in helping to reduce the rate at which AD progresses.
Generative AI and Alzheimer’s Disease Treatments
Generative AI has become a trending topic in the modern day. Following the public launch of tools such as OpenAI’s ChatGPT, the public started to turn to these systems for their everyday tasks. The technology has quickly evolved, and now the next generation of artificial intelligence is commonly implemented in the medical industry.
While the average person may use these tools to generate images or automate business tasks, researchers and scientists are using it to save lives by improving treatments for diseases. One great example lies in how researchers are using generative AI solutions to assist in advancing the treatment of Alzheimer’s disease.
We’ve already discussed the many challenges that lie in the treatment of Alzheimer’s disease. One common issue that comes up is the fact that the current treatments are generalized and tend to focus on managing the symptoms a person experiences.
However, recent studies show that gen AI could become a useful option for developing new drugs that could target factors beyond the current candidates' treatments focus on. There are already several studies that show how generative AI can be used in the development of AD treatments. This shows great potential, as using this technique could help to provide physicians and neurologists with an opportunity to develop highly personalized treatments that are tailored to each patient.
The Advancements in Gen AI for the Medical Industry
The medical industry has already seen the implementation of gen AI in various areas. Researchers have developed models such as the Generative Adversarial Networks and the Variational Autoencoders to specifically focus on novel molecule generation. These models are fine-tuned and provided with a vast amount of data. Through advanced analytics and assessments, they can develop molecular structures that could help researchers uncover new ways to treat diseases.
Other advancements that have already been made in Gen AI for the medical industry include:
- Using generative AI to predict how patients would respond to specific drugs and treatments. Gen AI can take into account specific biological aspects of a patient to provide accurate predictions.
- Researchers are turning to gen AI to speed up the process of analyzing imaging tests from MRIs, CT scans, and X-rays. This reduces the time needed to detect abnormalities, which could lead to a faster diagnostic process in cases where patients have conditions like cancer or cardiovascular disease.
- Gen AI has also shown potential in helping to enhance images that have too much noise or are low resolution. This helps to reconstruct much faster.
Advancements in generative AI have also allowed researchers to develop more personalized approaches to treating
Researchers Turn to Gen AI to Design Novel Drug Candidates for Alzheimer’s Disease
There are specific studies that have looked at how gen AI can be used specifically for Alzheimer’s disease.
One of the most significant findings here comes from Exscientia, a pharma-tech company that has a large focus on using artificial intelligence to advance the medical industry [6]. They were among the first to use AI to develop new drug candidates for artificial intelligence. The interesting thing about this advancement from Exscentia is that their drug candidate was already set to enter Phase I of its clinical trial by 2021. This was before the true AI boom started, which shows that companies were already working with AI for several years.
The CEO of Exscentia, Andrew Hopkins, has revealed several important findings, as well as discussed their approach to pharma-tech. According to Hopkins, they combined the use of generative AI, machine learning, and data acquisition in order to drive the development of new drugs for diseases like Alzheimer’s.
In the case of their new drug for AD, the AI systems at Exscientia were able to identify a specific small molecule that would help to provide targeted treatment. The molecule acts as a 5-HT2A receptor antagonist and, at the same time, a 5-HT1A receptor agonist. The molecule that was created is able to selectively avoid receptors that are similar to these. They specifically mentioned the avoidance of the dopamine D2 receptor. The dual action of the drug required to provide enhanced treatment for Alzheimer’s disease was a major challenge
Another study [7] looked at how gen AI could be used as a strategy for drug repurposing candidates. This has long been an important role of generative AI in drug development. Repurposing involves identifying existing drugs that could offer benefits beyond their prescribed or approved uses. In this particular study, real-world clinical validation was a priority in providing solid evidence on the efficacy and accuracy of AI in this strategy.
OpenAI technology was used in this particular case, where generative AI had to provide a proposal for the top 20 drugs that could be repurposed for Alzheimer’s disease. The researchers then tested the feasibility of the 10 drugs proposed as repurposed agents for AD.
One of the main findings here was that generative AI has the ability to use the global web in order to research scientific elements. In this case, it had to conduct extensive research to identify the drugs that had the most potential for use in patients diagnosed with Alzheimer’s disease. It also laid a foundation for future studies that might focus on further testing the ability of generative AI to help with repurposing drugs for brain diseases.
Current Challenges with Generative AI in Drug Development
As researchers continue to look into the use of gen AI for Alzheimer’s drug development, it’s important to understand the current challenges they face. This can help to create a more realistic expectation when looking at the progress and when this tech might become more mainstream.
Right now, the complexity of Alzheimer’s disease is a particularly important challenge. While there’s a large focus on factors like tau and amyloid proteins in AD, many more complicated elements also play a role. Neuroinflammation, protein phase separation, and synaptic dysfunction are only a few examples in these findings. Due to the complex interaction between these factors, AI is facing some challenges in modeling these relationships in order to provide a high level of accuracy.
There’s also the issue of hallucination in artificial intelligence. This can interfere with the overall accuracy of the molecules suggested by gen AI models, thus causing problems with its ability to ensure efficacy in every situation where it needs to develop a personalized treatment protocol.
Apart from these, it’s also important to understand the typical black box issue when it comes to artificial intelligence. Sometimes, gen AI models may, especially in cases of more complex systems, generate predictions or suggest molecules without providing the right level of transparency about their decisions. This causes a barrier for scientists and medical experts, as it’s not always clear why gen AI chooses specific ways to develop new, novel drugs for AD.
Future Directions
We can already see a lot of progress in the field of AD drug development using generative AI. However, researchers have only touched the surface right now. As technology continues to evolve, researchers are looking to create a more holistic approach to using gen AI for drug discovery and treatment in Alzheimer’s disease.
This means integrating a wider variety of datasets in order to improve how gen AI works. For example, a combination of different imaging data from MRI, fMRI, and PET could help gen AI better understand the changes that happen in the brain over a certain period of time. Adding genetic data to generative AI could also further enhance its understanding of Alzheimer’s disease. These additions to current tech datasets would further improve not only how gen AI understands the disease, but also create an opportunity for a much more personalized approach to treating AD.
When we look at the future of this tech, we’ll likely also see an integration of real-world data. This may include getting data from wearable sensors, allowing gen AI to get a view of sleep, motor function, and patient activity, and take those factors into consideration when developing potential drug targets.
The goal here is not only to use generative AI to develop drug candidates for AD. It is also to provide patients with personalized care. Taking into account unique elements related to each patient’s case is currently a major challenge for doctors treating Alzheimer’s disease. If real-world data can be effectively integrated into gen AI models, it would allow artificial intelligence to customize treatments based on each individual case.
Conclusion
AI is critical for the medical industry. It's constantly driving new discoveries and helping to advance treatments as we know them. Emerging research also shows that generative AI is useful in developing new drug candidates for Alzheimer’s disease. More research is definitely needed around AD and gen AI. However, we can see that there's already a lot of potential. It's making treatments better, and even finding how existing drugs can be repurposed for these patients.
References
[1] Alzheimer’s disease. Mayo Clinic. https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447
[2] Dementia statistics. Alzheimer’s Disease International. https://www.alzint.org/about/dementia-facts-figures/dementia-statistics/
[3] 2025 Alzheimer’s Disease Facts and Figures. Alzheimer’s Association. 2025. https://www.alz.org/getmedia/ef8f48f9-ad36-48ea-87f9-b74034635c1e/alzheimers-facts-and-figures.pdf
[4] C. Crump, W. Sieh, B.G. Vickrey, A.C. Edwards, et al. Risk of depression in persons with Alzheimer’s disease: a national cohort study. Alzheimer’s Association. 14 Apr 2024. https://pmc.ncbi.nlm.nih.gov/articles/PMC11016814/
[5] What Causes Alzheimer’s Disease? National Institute on Aging. https://www.nia.nih.gov/health/alzheimers-causes-and-risk-factors/what-causes-alzheimers-disease
[6] The role of AI in Alzheimer’s drug candidate design. Drug Discovery World. 1 Jul 2021. https://www.ddw-online.com/the-role-of-ai-in-alzheimers-drug-candidate-design-12099-202107/
[7] C. Yan, M.E. Grabowska, A.L. Dickson, B. Li, et al. Leveraging generative AI to prioritize drug repurposing candidates for Alzheimer’s disease with real-world clinical validation. NPJ Digital Medicine. 26 Feb 2024. https://pmc.ncbi.nlm.nih.gov/articles/PMC10897392/