​The last few years have seen undeniable, headline-grabbing breakthroughs in computational biology. With generative AI dreaming up novel proteins and deep learning solving decades-old structural mapping challenges, the industry has sold a compelling narrative: that we are on the cusp of fully in silico drug design. But these advances have birthed a pervasive misconception—the idea that the primary bottleneck in therapeutics is simply a lack of predictive algorithmic power. It isn't. The foundational problem in drug discovery, whether traditional or AI-driven, is that the process remains stubbornly sequential. The industry currently treats AI as a standalone oracle, applying massive models in isolated, disconnected steps that leave ultimate clinical viability entirely up to chance. To move beyond the hype, we have to stop treating biologics as a one-shot prediction game. The true frontier isn't just scaling up compute; it is shifting the entire paradigm from sequential guessing to continuous learning.

​The evening will feature dinner, discussion, and networking with entrepreneurs at the intersection of AI and health care.

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​Speaker

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​Ido Calman
CEO and Co-founder
Onava