Ola Wlodek
Ola is the CEO of Constructive Bio, a spinout from Jason Chin's lab in Cambridge, UK. The company is dedicated to broadening the range of building blocks for peptides and proteins produced recombinantly, leveraging cutting-edge synthetic genomics and engineered translation to unlock new possibilities for biopharma and beyond. Ola holds a PhD in Biological Sciences from the University of Cambridge and an Executive MBA from Warwick Business School, combining deep scientific expertise with strategic leadership skills. Her scientific career has been driven by a fascination with designing and producing 'unnatural' products through biological means-”transforming synthetic biology from exploration to purposeful innovation.
Constructive Bio
Strategic Alliances: How Pharma Thinks About Computational Drug Discovery Partnerships
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This session explores how pharma companies approach and build effective partnerships with computational drug discovery firms. Industry experts will share insights on best practices for data sharing, strategies for navigating cultural challenges with biotech and startups, and the role of explainable AI in fostering trust. The discussion will also cover common mistakes companies make when approaching pharma and the considerations around using proprietary algorithms versus open-source models to advance drug discovery.
Senior Leadership AIxBioPharma Luncheon - Inflection Points: Lessons from Computing and Cloud to Catalyze AI in Bio
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This is an invite-only exclusive luncheon with senior leaders at the forefront of AI and BioPharma to explore how decades of progress in computational technologies can inform the next wave of breakthroughs in biology. Featuring insights from Sandy Pentland (MIT), Richard Socher (You.com, ex-Chief Scientist at Salesforce), Ola Wlodek (Constructive Bio), and Simon Kohl (Latent Labs) this conversation will examine the strategic parallels between past inflection points in computing and today’s rapidly evolving AI-driven biological revolution. From data infrastructure to predictive modeling and decision-making under uncertainty, what can we learn—and what must we do differently—as we chart the future of biology.