Drug discovery has historically been a tedious, slow, expensive process. To get from discovery to approval takes 12 to 15 years and an investment of around $2 billion.
AI optimists have long pointed to advances in drug development as a reason for bullishness, and it's easy to understand why: The sheer data-crunching and protein-identifying prowess of such systems could potentially cut development time in half, and development prices by even more, proponents often claim.
Why? AI can complete complex math problems far faster than human scientists ever could. By feeding AI tons of data — scientific papers, drug patents, protein databases, electronic health records, etc. — these systems can identify druggable targets, design drug candidates, and even predict how well drugs will perform in trials.
Now, Insilico Medicine is turning that optimism into reality by using machine-learning tools to develop INS018055, a novel treatment for chronic lung disease idiopathic pulmonary fibrosis.
This week the company started Phase 2 clinical trials in humans. It’s a first for the pharmaceutical industry, and hopefully heralds a future where drug discovery is faster, cheaper, and better than it’s been for the last several decades.