The New Revolution
In the past, the pharma industry has expressed skepticism around the adoption of artificial intelligence and machine learning to perform tasks such as molecular design, discovery and clinical trials. After all, lives are at stake and Mark Zuckerberg’s idea that success requires startups to “move fast and break things” is the antithesis of the industry’s guiding principle to “do no harm.”
Therefore, companies making bioactive substances to put into patients and maybe even permanently engineer their genes must be very cautious that the technologies they are using are proven and safe. Despite this reluctance, there has been an explosion of promising startups created over the last five years that merge the field of computer science with computational biology, chemistry and biophysics. These companies are seeking to disrupt the current paradigm wherein new drugs fail to reach the market over 90-95% of the time, costing US$2.6 billion to develop (compared to US$0.2 billion in the 1980s), with development timelines of 10-15 years (McKinsey).
Part of the reason for this inefficiency is that drug discovery in pharma comes from a heritage of trial and error. Many molecules are tested before finding one that works that can be taken forward. This new wave of companies is attempting to make the process much more efficient. BenchSci, for example, was founded on the thesis that a big pharmaceutical company will conduct tens of thousands of experiments per year to evaluate molecular targets. On average, around 7,000 non-clinical, non-human experiments are needed to get a drug into the clinic and roughly 50-70% of those experiments do not scientifically advance the study of those targets. Improving the efficiency of this process would generate enormous value by lowering costs and timelines. AI is an uninterested participant in terms of trying to understand biology and does not require the same need to create mental frameworks that humans rely upon.
As a result, artificial intelligence can learn what drives biological systems in a much more comprehensive and non-hypothesis-driven way. Daphne Koller, founder of Insitro refers to this as moving drug discovery from an artisanal to an engineered approach, where a lot of the pieces to be built are designed to be performed in a repeatable reproducible manner. Although biology is extremely complex, one can draw parallels to the sentiment shift that occurred in finance and see that pharma could be going through a similar generational change. Because machine learning and AI, at their heart, are the most statistically sound way to handle large amounts of information, and drug design is fundamentally a data science problem, the decades ahead could fundamentally transform expectations around drug costs, time to market and how science is done.