Harnessing Data Science and AI for Drug Development Innovation

The pharmaceutical industry is a big business, often referred to as “big pharma”. It is also a very competitive industry capable of a great societal impact because of its role in public health and the overall economy.

Despite its size and maturity, there is a growing “drug discovery problem” in the industry. While the spending on drug discovery Research and Development (R&D) is increasing, the regulatory approval of new therapeutics has been experiencing a decline. The mounting challenges of the drug discovery can be summarized as follows in the three major categories:

There is a growing trend across multiple industries on finding ways to harness the power of data. Leveraging data with Data Science and Artificial Intelligence (AI) are at the forefront of driving business transformation. When it comes to drug discovery and development, employing these technologies has become an existential challenge for the pharmaceutical industry. A 2017 McKinsey & Company article positioned the application of breakthrough digital technologies in R&D as “the $100 billion opportunity”.

So how can these technologies help the pharmaceutical companies reduce the staggering expense and

amount of time it takes to develop new therapeutics, getting there sooner, with less compounds needing to be tested and proved? AI & Machine Learning (ML) are deployed to explore potential drug candidates faster, able to meet a particular target product profile.

But while leveraging new software applications to design new therapeutics completely virtually is highly desirable, it has not been entirely possible. Generative Therapeutics Design (GTD) is an AI platform that is an innovative combination of the Virtual (in silico) and Real (in lab) development, in a synergistic “active learning” system. This “Lab in the Loop AI” approach consists of the iteration and interplay of the following three system components:

Traditionally, the new compound design effort has been mostly a trial-and-error approach. With AI and ML, it is possible to create a more structured approach to design and save time and money on experimentation.