The landscape of drug discovery is on the brink of transformation, thanks to innovations in generative AI. IntelliGenAI’s IntelliFold foundation model is setting new benchmarks in structural biology, achieving results that rival or even surpass the renowned AlphaFold 3. This technology not only promises to enhance the efficiency of drug development but also introduces controllable features that could significantly improve the success rates of new therapies. Historically, the drug development process has been plagued by the infamous double ten rule, which dictates a decade-long timeline, exorbitant costs, and a dismal success rate of less than 10%.

In the rapidly evolving field of drug discovery, the integration of generative AI with a physics-based active learning framework is proving to be a game-changer. This innovative approach leverages generative models (GMs) to design molecules with specific properties, addressing common challenges such as target engagement and synthetic accessibility. By merging a variational autoencoder with nested active learning cycles, researchers can iteratively refine predictions, leading to the generation of diverse, drug-like molecules that exhibit high predicted affinity and synthesis accessibility.