Generative artificial intelligence may be having its banner moment, but the technology existed long before ChatGPT and DALL-E. It began in 2014 with a paper by Ian Goodfellow and several other researchers entitled “Generative Adversarial Networks” (GANs). Goodfellow is a computer scientist who worked for Google Brain and Apple and is currently with DeepMind. Today, his paper has been cited more than 55,000 times and underpins several AI tools.
Nearly a decade ago, Goodfellow uncovered a breakthrough: by using technology to draw on large amounts of data, AI tools can generate “synthetic” data under the right conditions. Over time, with constant training and feedback, the system learns to provide synthetic data closely aligned with the desired output. Today, these synthetic data might include smart contract code, fraud detection algorithms, and of course, hyperrealistic avatars with your face in the metaverse.
Generative AI not only solves challenges like coding and risk management but also drives powerful biotech innovations. Despite advances in manufacturing and discovery, it still takes 10-15 years and costs millions of dollars to bring a drug from discovery to market. And instead of declining with technological advances, the cost to bring a drug to market is only increasing.
AI can optimize speed and efficiency in drug discovery by streamlining new targets, designing new drugs, and even determining the likelihood of clinical trial success.
Generative AI enters the chemistry world
In 2016, Dr Alex Zhavoronkov, founder of drug discovery unicorn Insilico Medicine, made waves in the chemistry world by presenting generative AI technology at conferences from London to San Francisco. His research findings seemed farfetched to some but transformative to others–GANs, combined with reinforcement learning, could generate novel molecules for treating diseases.
Seven years ago, many still found AI a sci-fi, futuristic concept. Zhavoronkov brought examples of the technology’s ability to make something new to change people’s minds. He added petals to photographed flowers and generated unique faces to explain how AI can create new molecules. The chemists were skeptical, but Zhavoronkov was undeterred. AI was going to transform our health experiences; it just needed time.
Insilico eventually showed that its AI could find new disease targets. Using generative AI technology to produce and evaluate candidates and drug targets, their platform designed new…
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