In a world where a drug takes years and billions of dollars to develop, just one in 20 candidates makes it to market. Daphne Koller is betting artificial intelligence can change that dynamic. Twenty years ago, when she first started using artificial intelligence to venture into medicine and biology, Koller was stymied by a lack of data. There wasn't enough of it and what there was, was often not well suited to the problems she wanted to solve. Fast-forward 20 years, however, and both the quantity and quality of data, and the tools for studying biology, have advanced so dramatically that the adjunct professor of computer science at Stanford founded a company, insitro, that uses machine learning (a subspecialty of artificial intelligence) to explore the causes and potential treatments for some very serious diseases.

The COVID-19 pandemic has unveiled a pressing issue – the need to develop effective drugs rapidly. But developing a new drug is easier said than done. Drug discovery begins with a hypothesis that the inhibition or activation of a target molecule or pathway results in a therapeutic effect. After target identification and validation, comes hit-to-lead and lead optimization steps. This involves identifying hit molecules with an affinity to the target and selecting the "best" molecule to take forward.

The emergence of Artificial Intelligence [AI] within the past few years has garnered either optimism, skepticism, or fear as we see an increase in adoption, from everyday smart products to large-scale innovation. Often acknowledged as a game-changing technology, AI offers untapped potential in improving established ways of doing business, as well as with new opportunities in meeting and addressing critical pain points across many industries, including banking, manufacturing and healthcare. The pharmaceutical industry is also embracing the trendy technology for its abilities in effectively advancing and/or addressing the ever-changing drug or therapeutic needs from those who suffer from everyday viruses to complex diseases, like pancreatic cancer or Alzheimer's. As we see new renditions of once-eradicated viruses or destructive diseases like polio, traditional R&D efforts can be ineffective and expensive, often taking between 11 - 15 years and with costs upwards of $2.6 billion. AI-powered drug discovery efforts are enabling big pharma and biotechnology companies to streamline R&D efforts, including calculating vast patient datasets into digestible, tangible information, identifying personalized / precision medicine opportunities or forecasting potential responses to new drugs.