Don’t be surprised if you wake up tomorrow to discover that AI has taken over another industry you never thought of. Recent AI transformation across different industries is proof that no space can confine artificial intelligence all to itself. Like every other industry that has enjoyed the proliferation of artificial intelligence, Big Pharma is having its fair slice of the AI-pie.

In the healthcare space, healthcare practitioners rely on medications produced by pharmaceutical companies to treat a variety of diseases and to increase the life expectancy of patients. Globally, the biopharmaceutical industry is a multi-billion dollar industry that is always up on its toes towards novel, innovative medicines with major core areas in drug discovery and development. 


Drug discovery is the process of how new medicines are discovered. It ensures that a compound is therapeutic in curing and treating diseases. Once the lead compound has been identified via drug discovery, the process of bringing it to the market starts – this is drug development. The process from finding the lead compound to getting it to the market isn’t a walk in the park, either is the associated cost or timeline. It can take a decade for a new medicine to walk that route without factoring in clinical trials with an approval rate of less than 12%, which may span six to seven years. This costs pharmaceutical companies an average sum of $2.6 billion, according to reports by Tufts Center for the Study of Drug Development published in the Journal of Health Economics.


In the last six years, AI has re-invented how medical scientists develop new drugs to tackle diseases. Some pharmaceutical companies now resort to the use of automated algorithms to carry out tasks in drug discovery and development that once depended on human intelligence. The availability of Big Data and data analytics are responsible for this. The manufacturing systems used by pharmaceutical companies utilize the Internet of Things (IoT) to collect data at every stage of the drug development process. By using sophisticated AI tools, medical researchers at the fore-front of drug development get actionable insights from stacks of unstructured data in good time. 

This makes drug discovery and development faster and accurate. Patients whose lives are dependent on these medications are also able to access them in good time. To maximize the wealth of potential in AI, Big Pharma is going into partnerships with AI start-ups to help it make sense of the many data it is generating. For instance, Moderna is using Amazon’s AWS Cloud to develop messenger RNA medications to fight diseases, including COVID-19.


In drug discovery, new candidate medications are discovered. The process is filled with numerous trials and errors to identify the compound of interest. Target identification is the first step in a drug discovery process and involves high thorough-put screening. A drug target is a molecule in the body that is linked with the particular disease the drug-in-development is expected to act on.  

The next step is to validate the target. Here, medical researchers must show two things. First, that the target molecule is directly linked to the disease. Second, that the drug-in-development can alter the action of the target to achieve favorable outcomes. Before now, Biopharmaceutical companies have relied on flawed, time-consuming, and expensive conventional methods to carry out these processes. In fact, this has an average failure rate of 92% and costs Big Pharma over $80 billion every year. 

By using deep learning and machine learning algorithms, medical researchers now identify promising drug candidates while speeding up the overall process and saving operational costs. Bristol-Myers Squibb deployed machine learning models to find data patterns that are associated with CYP450 inhibitors. CYP450 inhibitors block the activity of CYP450 enzymes that are important for breaking down medications. The knowledge of these inhibitors helps to reduce adverse side effects and interactions of the drug-in-development. 

According to the senior vice president of research and development and translational medicine at Bristol-Myers Squibb, Saurabh Saha, machine learning models increased the accurate predictions of the analysis by 95%. This is no surprise because AI selects the target and lead component that has a high success rate of making it to the clinical trial stage. 

Other things researchers look out for are the mechanism of action, metabolism, the effect on other cellular processes and body functions. AI models help scientists and doctors predict the adverse side effects of new drug candidates both independently and when used with other medicines – which can be a life saver before a drug is tested on humans in clinical trials. 


Before the clinical trial phase is a pre-clinical stage of drug development where the drug is tested on animal models. All thanks to AI, we now have digital animal simulations to bypass the need to test drugs on animals. A perfect example is the AI-powered bio-simulations invented by VeriSim Life. In the clinical trial phase, Biopharmaceutical corporations test the drugs on humans and collect data on patient responses to these drugs. Clinical trials fail due to failure to follow FDA guidelines, poor manufacturing protocols, and lack of efficacy.

Machine learning algorithms eliminate these problems by analyzing data from clinical trial workflows. This analysis reveals and proffers recommendations on inefficiency hot-spots. It can also predict clinical trial outcomes, reveal the individual drug responses of each patient subpopulation and offer a patient-centered endpoint.

Eli Lily, Novartis, and Pfizer are currently using Antidote’s AI platform to organize every step of the clinical trial process. Such AI platforms predict patient drop-out rates while predicting which patient population is the best candidate. It is able to achieve this by screening out high-risk individuals before they enroll. This reduces the operational cost and failure rates of clinical trials. 

The future of AI in Big Pharma looks promising because making new drugs will cost less and take few days. Of a surety, if new start-ups and existing pharmaceutical companies must stand out, implementing AI is inevitable.

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