How artificial intelligence is helping to discover drugs faster than ever

Discovering a new drug used to take more than 10 years and cost billions of dollars. But with the advent of artificial intelligence (AI) , that process is being transformed. Thanks to algorithms that learn from large volumes of data, medical science has managed to accelerate drug development , find new combinations of compounds, and optimize clinical trials. This digital revolution could forever change the way we fight cancer, rare diseases, and other complex conditions.

The development of a traditional drug involves multiple stages:

• Identify an active molecule that can influence a disease
• Test it in cellular and animal models
• Evaluate its safety and efficacy in human clinical trials
• Obtain approval from regulatory agencies such as the FDA

This process, in addition to being costly and lengthy, has a high failure rate: 9 out of 10 drugs never reach the market . The reasons range from lack of efficacy to unexpected adverse effects.

AI applied to medicine uses machine learning models to analyze millions of data points from genetic studies, medical images, clinical outcomes, and scientific literature. From this data, it can:

• Predict how a molecule will act in the human body
• Suggest new combinations of existing drugs
• Detecting hidden patterns in diseases
• Optimizing clinical trial design
• Reducing the margin of error in laboratory tests

Instead of performing thousands of manual tests, algorithms can simulate chemical and biological reactions in seconds, filtering out the best options for implementation.

The promise of AI is already bearing fruit in the real world. Some notable examples:

In 2020, Exscientia, in collaboration with Sumitomo Dainippon Pharma, developed the first drug created entirely with AI to treat obsessive-compulsive disorder. It went from an idea to clinical trials in just 12 months , an industry record.

During the pandemic, AI tools helped repurpose existing drugs , accelerating the search for effective treatments. They were also used to predict virus variants and design more efficient vaccines.

In 2023, MIT researchers used artificial intelligence to find an antibiotic capable of eliminating bacteria resistant to common treatments. The model analyzed more than 100 million compounds in a matter of days.

Applying artificial intelligence to drug discovery has multiple benefits:

• Reduce development time from years to months
• Reduces research costs by avoiding unnecessary testing
• Improves the accuracy of therapeutic target identification
• Increases safety by anticipating possible side effects
• It promotes personalized medicine by creating treatments tailored to each patient.

Furthermore, AI can accelerate research into rare or neglected diseases, where resources are often scarce.

While AI offers tremendous opportunities, its application in medicine also poses challenges:

• Transparency of algorithms : it is often not known how the algorithm reaches certain conclusions (the “black box algorithm” problem).
• Privacy of medical data , which must be rigorously protected
• Need for human oversight , as models can make mistakes if input data is inaccurate
• Inequality in access to technology , which could widen gaps between countries or medical centers

Therefore, many experts agree that AI should be a tool that complements the doctor , not replaces him.

Artificial intelligence is changing the game in the pharmaceutical industry. Its ability to analyze large volumes of data and find solutions in record time could accelerate the development of treatments for diseases that currently have no cure. Although there is still a long way to go, the future of medicine is increasingly linked to the collaboration between science and technology .