In this post, we decided to highlight eleven entrepreneurial women, leading the way in applying advanced computational technologies, such as machine learning (ML), deep learning (DL), and other artificial intelligence (AI) components, for tackling some of the hardest challenges of science -- in drug discovery and healthcare. This list is composed in alphabetical order. 

Canada has a firm place in the global life sciences ecosystem, being the tenth largest market for pharmaceutical sales, and home to America’s second-largest life sciences corridor. The country has a long history of life science research, including the creation of the first pacemaker and discovery of stem cells. 

According to a survey conducted by BIOTECanada and Deloitte in 2018, conducted in 2018, the Canadian life sciences sector includes many early-stage companies with substantial growth potential -- 67% of responders identified themselves as being in the discovery or emerging phase of development in 2017. Surveyed organizations reported intentions to raise additional capital in the coming years and reported access to capital as the primary issue in the life sciences sector in Canada.

Alongside the topic of gene editing technologies that keeps making headlines these days, there is also a wave of breakthroughs in the field of RNA-targeting medicines, primarily falling into the following two categories: antisense oligonucleotides (ASO), and RNA interference (RNAi) technologies. (another promising approach, dealing with editing RNA itself by ADAR enzymes, is not covered in this post). 

The number of immunotherapies in clinical trial rolls over 5000 now, and immunology has become a common approach in some cancers. Cell technologies, as a growing sub-field in the immunotherapy landscape, have progressed considerably and now represent a $26 billion financial opportunity by 2030, according to a report by Roots Analysis.

The race for adopting new machine learning (ML), deep learning (DL) and related technologies (for simplicity -- “artificial intelligence”/”AI”) keeps rapidly unfolding in the pharmaceutical industry, albeit with varying rate of progress across different use cases. 

Let’s review retrospectively some of the key developments in the drug discovery area in 2019 and see how they characterize the current state of AI in the pharmaceutical industry (“hype vs reality”). Note, that I do not cover the healthcare sector in this post (diagnostics, medical applications of AI, digital health etc) -- those will be discussed in one of the future posts.