Artificial Intelligence and Machine Learning are powering incredible changes across a huge range of industries. But in data and research-dependent industries such as pharmaceuticals, they’re having an unparalleled impact. From improving candidate selection processes for clinical trials, to accelerating new drug development, AI is quickly becoming an essential tool for those that want to stay competitive in this dynamic industry.

So, here’s a look at how artificial intelligence is making an impact on the pharmaceutical industry:

Drug Discovery Process and Design

From making small molecules to determining novel biological targets, AI plays a prominent role in drug target identification and validation; phenotypic, target oriented, and as multi-target drug innovation; biomarker identification etc.

A major benefit of AI in the pharma industry is that it minimizes the time taken for a drug to be approved and reach the global market for sale. This leads to cost-reduction, which means lower cost medications for patients care without side effects and more treatment options.

For example, researchers in pharma industry can identify and verify novel cancer drug targets using data such as longitudinal EMR records (Electronic Medical Records) and other ‘omic data’.


Pharma companies across the globe are developing advanced AI-powered tools and ML algorithms to smoothen drug innovation process. These technology tools are designed to detect complex patterns in large datasets and, therefore, can be used to resolve problems associated with complex biological networks.

This ability to study patterns of various diseases and to determine which composite formulations are best suited for the treatment of specific symptoms of a particular disease is excellent. Pharma industries can invest in the R&D of such drugs that are more likely to treat a disease or medical condition successfully.

Disease Prevention

Pharmaceutical organizations are using AI to try and develop cures for both Parkinson’s and Alzheimer’s and for other very rare diseases. In general, pharma companies don’t spend resources and time to find medicine for an early stage of rare diseases because the ROI for such efforts is less compared to the cost and time it takes to develop a drug for rare diseases.

It is a fact that almost 95% of rare diseases do not have FDA approved cures or treatments, as per Global Genes. However, thanks to the innovative capabilities of AI and ML, the scenario is changing rapidly for the better.


Physicians can use advanced machine learning systems to gather, process, and analyse patient health care data. Healthcare professionals across the globe are using deep learning and ML to securely store patient data in the centralized storage system or cloud. This is called Electronic Medical Records (EMR).

Physicians may refer to these health records when they need to understand the effect of a specific genetic trait on a patient’s health or how medicine treats.

These systems can use data stored in EMRs to generate real-time estimates for diagnostic purposes and to indicate appropriate treatment for the patient.

As ML technologies are capable of processing and analysing large amounts of data quickly, they can help speed up the diagnostic process, thereby saving millions of lives.

Epidemic Prediction

Pharma companies and healthcare industries are using ML and AI technologies to monitor and assess the spread of infections worldwide. These modern technologies consume data collected from unequal resources on the web, study the connection of several environmental, biological, and geographical factors on the population health of diverse geographical regions and attempt to connect the dots between these factors and the prevalence of previous epidemics.

Such models are particularly beneficial for developing economies that lack medical infrastructure and financial framework to combat the spread of infection.

A good example of this is the ML-based malaria outbreak prediction model, which serves as a warning tool for malaria outbreaks and helps health care providers take the best action to combat it.

Identifying Clinical Trial Candidates

AI not only helps to understand clinical trial data, but also helps the pharmaceutical industry to find patients to participate in clinical trials. Using AI, researchers can analyze genetic data to determine the appropriate patient population for a clinical trial and identify the appropriate sample size.

Some AI technology allows patients to read the free-form text as they enter structured data and clinical trial applications like intake documents and doctor notes.

Drug Adherences and Dosage

It is a big problem for pharma companies to ensure that volunteers in clinical studies comply with the drug study protocol. Patients in a study should be excluded from the study if they do not follow the rules of the study, or there is a risk that the study drug will distort the results.

One of the critical factors in a drug trial is to ensure that participants take the required dose of the drug studied at regular intervals. Hence, it is so important to have a way of sticking to the prescribed doses. Through remote monitoring and algorithms to predict test results, AI technology can sort out good apples from bad.

Who’s leveraging artificial intelligence in pharma today?

It is probably not a big surprise that the leaders in the technological arms race are the biggest players in the pharmaceutical industry who can afford to invest a lot of money in artificial intelligence and machine learning solutions.

Almost all leading pharma companies use some incarnation of artificial intelligence technology or big data solutions to prompt research and development in the area. Artificial intelligence in the pharmaceutical industry can be seen among the following companies:

  • Pfizer uses IBM Watson, a system that uses AI and big data analysis, to power its search for immuno-oncology new drugs with a drug discovery platform.
  • GlaxoSmithKline is a British pharma giant investing in machine learning and AI to automate drug discovery.
  • Sanofi is a French multinational pharmaceutical company headquartered in Paris which leverages AI to accelerate their research into metabolic-disease therapies.
  • Genentech (a Roche subsidiary) is leveraging an AI system provided by the data analytics company GNS Healthcare for researching and creating new cancer treatments.
  • BenevolentBio is a London-based start-up that uses data from sources such as research papers, patents, clinical trials, and patient data into its AI big data platform to gain actionable insights for the pharma industry. They build artificial intelligence tools to pinpoint relationships between genes, symptoms, diseases, proteins, tissues, species, and drugs.
  • F. Hoffmann-La Roche AG developed a data-driven medical research platform leveraging deep learning.
  • Roche has acquired Flatiron Health, a start-up using AI for cancer research and patient care improvement.


AI-based solutions in pharma are gaining momentum, becoming the new competitive battleground for many manufacturers. The pharmaceutical industry desperately needs digital transformation and new technologies to process vast amounts of health data efficiently. It identifies significant relationships between them, effectively decreasing time-to-market in drug manufacturing.

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