Engaging The Black Box: How AI Changes The Stakes For Pharma

December 14, 2017 | In their never-ending quest to speed drug discovery, development, and commercialization, pharmaceutical and biotechnology companies are now turning to information technology’s latest darling, artificial intelligence (AI). Top firms, including GSK, Merck & Co, Johnson & Johnson, and Sanofi are diving deep. Smaller companies are also trying to become players. But how can drug developers leverage AI to reverse the decline in R&D productivity that has persisted in the face of other IT technologies, such as structure-based drug design or bioinformatics?

AI is substantively different from most IT tools. It will not be easy to retrain current IT staff to become AI-proficient data scientists. Data science is a very technical field requiring sophisticated expertise in computer science, mathematics and a new class of big data technologies. A bachelor’s degree or a few online courses will not suffice in filling these requirements. Meanwhile, the demand for AI professionals has surged in all fields. As a result, finding, and retaining qualified AI employees is a challenge.

In addition to talent challenges, embedding AI into your drug R&D process will require overhauling how your data is managed, breaking down organizational silos, and, most importantly, embracing a challenging shift in thinking.

Most companies aren’t ready to make the kind of investment required to integrate AI into operational systems. As a result, many have adopted the “toe in the water approach,” which is common when getting started with new technologies.  This tactic lets companies try out a few vendors and a few approaches without being overly invested in a single position. The idea is to lower risk while still “playing” in the field.

This approach is a good way to get started. Numerous vendors will gladly partner on targeting their respective niche within drug R&D. Many of these early players are focusing on drug discovery activities like predicting molecule – target bonding, identifying new biomarkers, and finding new drug repurposing opportunities. More recent players are starting to target opportunities in clinical. All of this innovation has led to a wider interest in AI, with Reuters recently reporting that “the world’s drug companies are turning to artificial intelligence to improve the hit-and-miss business of finding new medicines.”

Spending too long in this proof of concept phase is risky. What if such a targeted AI initiative proves successful and the key vendor is acquired? How will you transmit your petabytes of data that have accumulated on a distant computer back to your internal systems? How are you going to deal with public cloud security concerns? Companies need to ensure they can build upon any progress made out of the gates. Otherwise, after several such forays, they may find they have made no tangible progress.

So, while AI partners can quickly target a niche within the drug discovery, development, and commercialization pipeline, there are some several strategic areas that companies who are serious about using AI should begin to address immediately.

see the full article on Bio-IT World

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