How is AI changing drug discovery?

Related Services:
Artificial Intelligence

The article is based on Unicsoft’s extensive experience in developing AI for drug discovery.

Artificial Intelligence, or simply AI, makes our lives easier. It has become an essential part of everyday life and continues to penetrate more business spheres. AI allows computers and machines to analyze and learn from past behaviors just like human beings. It has established a permanent role in our daily routines, and in many cases, its influence is invisible. 

AI has great potential to streamline processes and make them more cost-effective in many spheres, and drug discovery is no exception. Have you ever thought about how much time, effort, and money it takes to develop one new drug? $1.3 billion is the average price tag for developing a treatment. The cost of developing drugs varies across different disease segments, with cancer treatment being the most expensive. That’s the primary reason why the pharmaceutical industry is looking for new cost-efficient ways to develop drugs that save or improve lives. 

Leading biopharmaceutical companies believe that implementing an AI-powered drug discovery algorithm could be the solution to empowering drug discovery and cutting the costs and time required to find new treatments. So let’s take a deeper look at the role of AI in drug discovery

How Artificial Intelligence is Improving Drug Discovery

When AI and gifted scientists can combine their work to enhance outcomes, the result is a complete transformation for the future of drug discovery. This transformation can be used to harness the power of information from a diverse range of data sources, disease registers, scientific papers, medical research, and clinical trial outcomes. 

Artificial intelligence can significantly reduce the costs required for drug discovery because of its ability to streamline operational processes. AI and ML-powered technologies combined with the IoT and smart wearables allow for new conveniences like tracking patient health from homes, controlling medication adherence, monitoring responses, and improving patient outcomes. 

The technology can also assist in selecting the right patients who stand to benefit the most from treatment and those who could be at risk while taking part in clinical trials. With AI-powered algorithms, drug discovery gets a jet-propelled injection of efficiency. 

A collaborative environment for research

AI platforms can collect and interpret data from multiple sources. For example, Owkin is a French AI startup that connects scientists, academic researchers, and pharmaceutical companies. The AI-powered platform analyzes multimodal datasets and helps pharmaceutical companies discover new drugs, optimize clinical trials, and identify potential candidates of interest. 

It takes advantage of federated learning to develop and train its ML models and increase the efficiency of drug discovery and clinical trials. 

AI-powered platforms can help researchers analyze data to find drugs from a variety of sources:

  • Unstructured evidence such as patents, textbooks, biomedical literature, and others
  • Organized databases of substances, pathogens, drugs, etc. 
  • Various imaging such as computed tomography (CT), magnetic resonance imaging (MRI), x-rays, ultrasound, positron emission tomography (PET), etc. 
  • Records ranging from unstructured or standardized data channels like electronic health record data, clinical trial data, toxicology reports, and Rx claims

Patient enrollment

Finding the right patient for the right trial is a time-consuming and tedious task for both researchers and patients, resulting in a shortage of enrolled patients in clinical trials. Only 3% of cancer patients take part in clinical trials. 

With AI, a study team can get access to a wider pool of patients. AI-powered platforms can mine, analyze, and interpret data from multiple data systems like medical imaging, electronic health records, and omics data. The following approach can improve the selection of patients and increase the effectiveness of AI-based drug discovery.

Remote patient monitoring 

Artificial intelligence algorithms, coupled with wearables, can collect and monitor patient data 24/7 and provide real-time insights into the effectiveness of treatments as well as safety. The following monitoring devices can record vital information such as heart rate, blood pressure, and oxygen levels without human intervention. 

AI can open up a new source of powerful data and increase insights into a patient’s various responses to treatment. This collected data can help AI-enabled algorithms generate more accurate predictions. 

Pattern recognition

AI-enabled systems can recognize data regularities and patterns. It can work with various data types such as biometric recognition, image recognition, and facial recognition. The following systems can help researchers fully understand human biology and better assess the efficiency of a drug. AI can help pinpoint even the slightest changes and help design personalized treatments for patients with specific biological profiles. 

The following data can help researchers make better-informed decisions in a more timely manner. 

Accelerated drug discovery via AI algorithms

AI services can assist pharmaceutical companies in getting the right treatment to market faster. The systems powered by AI algorithms can process data much faster than humans. It took researchers more than 13 years to study and identify the 20,500 genes present in human DNA. If they used AI and ML-enabled systems, they would have arrived at this result in less than 24 hours.

While AI systems are fast and more accurate, they can also catch problems early on and prevent an entire drug discovery process from being corrupted by bad data. Thanks to AI’s ability to make connections between datasets, it can help analyze thousands of pharmaceutical compounds and find the best working treatment. Hence, improved accuracy and predictability of all pharmaceutical compounds can lead to faster and more cost-efficient drug discovery processes. 

Wrapping Up 

Artificial intelligence has an important role in drug discovery and clinical trials. Al-enabled platforms can process massive amounts of data that help drug discovery and development researchers reveal valuable insights. AI can help us augment traditional techniques with a new layer of computational intelligence for faster and more accurate results. 

Patterns recognized by AI and ML can expose trends in drug discovery that the human eye just isn’t capable of detecting. By emphasizing these patterns and trends, researchers can focus on the most critical information while skipping past meaningless details. Since AI can spot any ever-so-slight changes, the number of experiments can be reduced to save time and rededicate entire budgets.  

At Unicsoft, we know how to leverage the power of AI and ML technologies and speed up your business processes in any field. Contact us to get a free estimate of your future project.


  1. How is AI used in drug discovery?
  2. Why is AI important in drug discovery?
  3. What is the problem with AI in drug development?
  4. What are the disadvantages of AI in drug discovery?