How Artificial Intelligence Is Helping Pharmaceuticals Develop Drugs

Theoretically, the process of drug development is a compact five-step process. However, in reality, it takes an average of 12 years, costing companies more than a billion dollars for a drug to travel from the laboratory to a pharmacy.

This long, complex, and expensive journey is challenging but it offers an opportunity to bring in an impactful change. This is why more and more pharmaceutical companies are exploring advanced technologies such as artificial intelligence, machine learning, big data and cloud computing to streamline the process of drug discovery to drastically reduce both time and costs. Here is an overview.

Drug Discovery

Drug discovery and development is the first step of the five-step process for drug development established by the FDA. It is followed by preclinical research, clinical research, FDA drug review and finally FDA post-market drug safety monitoring.

The California Biomedical Research Association estimates that, “Of all the tens of thousands of new drug compounds that begin the research process on the laboratory benchtop, only about five in 5,000 of the drugs that begin preclinical testing (animal trials) ever make it to human testing. Only one of these five is ever approved for human usage.”

In 2016, The Tufts Center for Study for Drug Development assessed the cost that goes into each successful drug at around $2.6 billion.

The conventional process of drug discovery starts with scientists beginning to identify and shortlist the chemical compounds from a humongous amount of data and images that could be tested for a targeted disease.

This extensive and time-consuming process is where machine learning can be applied to speed things up. The drug discovery market is projected to reach $85.8 billion in 2022, growing at a compound annual growth rate of 9.4% from 2017-2022.

Industry Collaborations

Some of the biggest names from the pharmaceutical world are working with technology giants and start-ups to shorten the time to identify new drugs and re-purpose current drugs for better healthcare.

Merck (MRK) was a pioneer among pharmaceutical giants to venture into the space of AI. Back in 2012, Merck partnered with Numerate to utilize its in silico drug design technology proprietary algorithms and cloud computing to generate new drug leads for an undisclosed cardiovascular disease target. Merck is a part of Atomwise which uses “a combination of patented structure based convolutional neural network for drug discovery, and enormous amounts of data.”

Since 2016, Pfizer (PFE) is leveraging IBM Watson (IBM) for drug discovery and support the identification of new drug targets, combination therapies for study, and patient selection strategies in immuno-oncology–an approach to cancer treatment that uses the body’s immune system to help fight cancer.[…]

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