Science to venture

Finding the next biotech founders with breakthrough potential from in-depth analysis of scientific publications dynamics

Top-tier European venture capital firm

Our client’s mission is to improve the lives of patients through science, and it does so by helping some of the world’s most transformative biotechs go from inception to reality.

Background

Finding the brightest in Biotech

Successful early-stage biotech investors find the latest promising academic researchers, put significant capital behind them and reap the rewards of propelling successful innovation. But this is easier said than done. Discovering research worth pursuing is difficult. Only a tiny fraction of it is relevant, and finding it requires combing carefully through mountains of dense and detailed descriptions of scientific findings.

Adding to the daunting volume is time pressure. Why? Because whoever finds the next big scientist-cum-entrepreneur first could secure the largest share in a potentially transformative idea. Our client wanted to explore whether machine learning could help spot the next wave of emerging scientific talent.

Impact

A monthly funnel of investment opportunities

The project has been running for three years, where our client uses monthly reports generated by our discovery engine to create a list of talent and prioritise who to follow up with. It’s helping them meet, find and invest in the leaders behind some of tomorrow’s most innovative biotech solutions.

Approach

Using AI to create a competitive advantage

You can’t win in this race by browsing through academic publications. Gone, too, are the days where you might be able to rely on tip offs alone. It’s why AI-driven tools are beginning to provide huge competitive advantages.

Building a discovery engine

We built a discovery engine centred around people. Our solution was made to track the individual careers of scientists over time, allowing us to scan research across institutions and industry. We were able to track people’s career trajectories, so we could distinguish between academics and entrepreneurs.

Using machine learning, we were then able to categorise realistic targets, turning data and research into a compelling plan. It meant our client could quickly zoom in on the people and ideas in biotech that will matter next.