Artificial intelligence is helping scientists search for possible treatments for some of the hardest brain diseases to treat, including Alzheimer’s disease, ALS and psychiatric disorders.
Developing a new drug can take years. Scientists first need to understand the disease, find a suitable biological target and identify compounds that could affect it. Those compounds then have to go through laboratory tests and clinical trials.
Drug discovery can be even harder for brain diseases. Researchers need to understand complex changes in the brain, while potential medicines also need to reach the brain to work. AI is now being used to speed up some of this early research. Scientists are using AI models to study genetic and biological data, look for possible drug targets and search large collections of chemical compounds.
A recent review in Nature Reviews Drug Discovery found that AI is becoming increasingly useful for finding and studying potential drug targets.
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AI Can Screen Billions of Compounds for Alzheimer’s Drug Research
One of the biggest problems in drug research is the sheer number of compounds that scientists could test. Researchers at Indiana University are using AI to help narrow this search for Alzheimer’s disease.
The five-year project has received a $6 million grant from the National Institutes of Health and brings together AI, chemistry and medical research. The team wants to build systems that can screen billions of compounds and identify those that could interact with proteins linked to Alzheimer’s disease.
The researchers are also looking for compounds that could reach the brain, which is a major challenge in developing drugs for neurological diseases. Rather than testing every compound in the laboratory, AI can first create a smaller list of potential candidates. Scientists can then test those compounds in laboratory experiments.
AI does not replace these experiments. A computer prediction can suggest that a drug might work, but researchers still need to test whether it actually works and whether it is safe.
Alzheimer’s Drug Research Faces Major Scientific Challenges
Alzheimer’s disease is one area where researchers hope AI can speed up the search for new treatments. The disease involves several biological processes, and scientists are still studying how they contribute to the progression of the disease. This makes it difficult to identify targets that could lead to effective drugs.
A potential Alzheimer’s drug also needs to cross the blood-brain barrier and reach the brain. The Indiana University project is trying to address these problems by combining computer-based research with chemistry and laboratory testing. Researchers will use AI to search for chemical structures that could interact with proteins involved in Alzheimer’s disease.
Indiana University researchers have also been studying possible new drug targets for Alzheimer’s. In separate research published earlier this year, they found that removing a particular enzyme from neurons reduced amyloid plaques and changed lipid metabolism in the brain.
AI could help researchers study similar targets by comparing large amounts of biological data and looking for compounds that could affect them.
AI Helps Researchers Find Existing Drugs That Could Treat ALS
AI can also be used to find new uses for medicines that are already available. A recent study published in npj Digital Medicine used genetic data to look for existing drugs that could potentially be used to treat amyotrophic lateral sclerosis (ALS).
The researchers analyzed more than 150,000 samples, including 29,612 people with ALS and 122,656 people without the disease. They then compared the data with the effects of 1,001 FDA-approved drugs.
The analysis identified furosemide, a drug commonly used as a diuretic, as a possible candidate for further ALS research. The researchers carried out additional work using U.S. Medicare prescription data covering 114,950 people. They also used clinical-trial simulations and tested the drug in mice.
The results suggested that furosemide may help protect nerve cells by reducing excessive activity in neurons. However, the study does not show that furosemide is an approved treatment for ALS. More research is needed to determine whether the drug could safely and effectively treat people with the disease.
The study shows how AI and large datasets can help researchers find new uses for existing medicines without starting the drug discovery process from the beginning.
AI Helps Researchers Identify New Drug Targets
Finding the right target is an important part of developing a new medicine. Researchers can use AI to examine genetic information, protein data and other biological information linked to a disease. This can help them identify possible targets and decide which ones are worth investigating.
Tools such as AlphaFold, which can predict the structure of proteins, are also being used in drug research. For brain and psychiatric diseases, researchers can use these tools to study proteins involved in disease processes and look for ways to target them with medicines.
A 2026 review in Translational Psychiatry said AI could help with some of the major problems in developing drugs for neurological and psychiatric conditions. These include finding suitable targets, understanding disease mechanisms and getting drugs across the blood-brain barrier.
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AI Could Speed Up Early Stages of Drug Development
AI is being used for more than just finding potential drug molecules. Researchers are developing systems that can analyze data, suggest compounds, predict how those compounds might behave and help decide which experiments should be carried out next.
A recent article in Nature Chemical Biology described the growing use of AI systems alongside automated laboratory equipment. In this type of setup, researchers can use AI to suggest a compound, test it in the lab and then feed the results back into the system.
The next set of experiments can then be based on what was learned from the previous tests. This could help researchers move through the early stages of drug development more quickly. However, the technology still has important limits.
AI Still Faces Major Challenges in Drug Discovery
AI can help researchers find promising candidates, but a computer prediction does not mean a drug will work in people. An August 2026 review in Nature Reviews Drug Discovery found that there is still limited evidence showing that AI has produced major improvements in clinical drug development.
Researchers still face problems with complex biological data and with turning computer predictions into treatments that work in real patients. This is especially important for brain diseases.
A 2026 review in Translational Psychiatry found that several AI-related drug candidates have reached clinical trials. However, no commercially available drug has so far been developed entirely through an AI-based approach.
Human researchers therefore remain an important part of the process. Potential drugs still need laboratory testing and clinical trials to establish whether they are safe and effective.
AI Could Speed Up Early Drug Research for Brain Diseases
The growing use of AI is giving researchers another way to approach difficult diseases such as Alzheimer’s and ALS. Instead of manually searching through huge numbers of compounds and biological datasets, scientists can use AI to narrow the field and identify candidates for further testing.
The Alzheimer’s research at Indiana University and the ALS study show two different approaches. One uses AI to search for new chemical compounds, while the other uses large datasets to look for new uses for existing drugs.
Neither approach guarantees a successful treatment. A promising result from an AI system is still only the start of the drug development process. But as researchers combine AI with larger biological datasets, better protein models and automated laboratory testing, the technology could make the early search for brain disease treatments faster.
For diseases that have remained difficult to treat for decades, even a faster way to identify promising drug candidates could help researchers move more quickly toward the next stage of testing.
