How AI is being used to develop new drugs and treatments

Artificial intelligence (AI) is rapidly transforming the drug discovery and development process. AI can be used to analyze large amounts of data, identify patterns, and make predictions that would be impossible for humans to do on their own. This same data-driven intelligence is now influencing industries far beyond healthcare, shaping how organizations adopt automation, predictive analytics, and AI-led decision-making across sectors, as explored in how AI is transforming Indian businesses. This is helping scientists to discover new drug targets, design new drugs, and predict how drugs will interact with the body.

Here are some specific ways in which AI is being used to develop new drugs and treatments:

Identifying new drug targets

AI can be used to analyze large datasets of biological data, such as gene sequences, protein structures, and disease models, to identify new drug targets. These are molecules that play a role in the development or progression of a disease, and which can be targeted by drugs to stop the disease process.

Research institutions such as Nature’s machine learning research publications highlight how AI-driven modeling is accelerating biomedical discovery timelines.

For example, AI has been used to identify new drug targets for cancer, Alzheimer’s disease, and Parkinson’s disease. AI has also been used to identify new drug targets for infectious diseases, such as malaria and tuberculosis.

Designing new drugs

Once a new drug target has been identified, AI can be used to design new drugs that target that molecule. AI can be used to generate new chemical compounds, and to predict how those compounds will interact with the drug target.

The underlying principle—using AI to simulate outcomes before real-world execution—is now widely applied in business environments through AI-enabled digital transformation strategies.

For example, AI has been used to design new drugs for cancer, Alzheimer’s disease, and COVID-19. AI has also been used to design new drugs for infectious diseases, such as malaria and tuberculosis.

Predicting how drugs will interact with the body

AI can be used to predict how drugs will interact with the body, including how they will be metabolized, how they will be distributed to different tissues, and how they will interact with other drugs. This information can be used to optimize drug design and to predict how drugs will perform in clinical trials.

This predictive capability mirrors how organizations evaluate AI performance in operational environments using structured AI investment ROI frameworks.

For example, AI has been used to predict how drugs will interact with the liver, which is responsible for metabolizing many drugs. AI has also been used to predict how drugs will interact with the blood-brain barrier, which prevents many drugs from reaching the brain.

Challenges and opportunities of using AI in drug discovery and development

There are a number of challenges and opportunities associated with the use of AI in drug discovery and development.

One challenge is that AI models need to be trained on large amounts of data. This data can be expensive and time-consuming to collect and curate.

Today, even small teams are leveraging accessible AI-powered utilities to process and structure information more efficiently using practical solutions like lightweight AI-driven tools.

Another challenge is that AI models can be complex and difficult to interpret. This can make it difficult to understand how the models are making predictions and to identify any potential biases in the models.

Despite the challenges, AI has the potential to revolutionize the drug discovery and development process. AI can help scientists to discover new drug targets, design new drugs, and predict how drugs will interact with the body. This can lead to the development of new and more effective treatments for a wide range of diseases.

Global health organizations including WHO’s AI in healthcare initiatives recognize AI as a key driver of faster diagnostics and treatment innovation.

Examples of how AI is being used to develop new drugs and treatments

Here are some specific examples of how AI is being used to develop new drugs and treatments:

AI is being used to develop new cancer drugs.

For example, the company DeepMind has developed an AI model that can generate new cancer drug candidates that are more likely to be effective than those generated by traditional methods.

AI is being used to develop new Alzheimer's disease drugs.

For example, the company Eisai has developed an AI model that can predict how Alzheimer’s disease drugs will interact with the brain. This information is being used to design new drugs that are more likely to be effective.

AI is being used to develop new COVID-19 drugs.

For example, the company Pfizer has used AI to design and develop the COVID-19 vaccine Paxlovid.

AI is being used to develop new drugs for infectious diseases.

For example, the company Sanofi has used AI to design and develop a new drug for malaria.

These are just a few examples of how AI is being used to develop new drugs and treatments. AI is a powerful tool that has the potential to revolutionize the drug discovery and development process.

AI is rapidly transforming the drug discovery and development process. AI is being used to identify new drug targets, design new drugs, and predict how drugs will interact with the body. This is helping scientists to develop new and more effective treatments for a wide range of diseases.

As AI matures across industries, the convergence of research, business intelligence, and digital infrastructure is shaping a new era of responsible innovation and scalable technology adoption.AI is still in its early stages of development, but it has the potential to revolutionize the way that drugs are discovered and developed. In the future, AI could be used to develop personalized treatments for each individual patient, and to develop new treatments for diseases that are currently incurable.