Advertise on podcast: Machines and Molecules

Mike Ryan is the founder of Bullet Point Network. Before that he spent two decades at Goldman Sachs, where he was a partner and co-head of Global Equities, and then sat on the investment committee at the Harvard Endowment, allocating $ 12B to outside funds and $ 6B directly into companies. The episode opens with a comparison the host can't reconcile: one of the best-known venture firms published its performance, and measured against a Nasdaq-100 index fund the average return came out slightly below the index — with much worse liquidity. That pattern isn't unique to one firm. So how do a hundred very smart analysts produce index-matching returns? Ryan's answer has two parts. The first is that public technology stocks have had an unusually strong run on a risk-adjusted basis over the last five, ten and seventeen years. The second is about what goes wrong inside investment firms, and it isn't what most people assume: the largest funds see nearly every deal, employ excellent people, and produce enormous amounts of analysis. What that analysis often contains, he argues, is a repackaging of information the company itself supplied. A firm can hire very smart people, build a beautiful process, and still deliver index-like returns or worse. He then pushes back on the premise. The last five years had a specific problem — funds haven't been able to exit and realise profits — and meanwhile the value creation has moved into private markets. Companies are now going from nothing to a hundred billion, and in a few cases close to a trillion, before they ever go public. He points out that Microsoft, one of the best companies of its era, did almost all of its growing as a public company, and says the new pattern is something he has not seen before in his lifetime. The host raises Ryan's undergraduate thesis, written at Yale under David Swensen, who created the endowment model that pushed universities toward illiquid assets, and asks whether he ever suspected the model had stopped fitting. Ryan treats the 2008 financial crisis as the real test of that conviction: institutions forced to sell into the liquidity crunch suffered badly, while those who held were rewarded over the following two decades. But he expects the next ten to twenty years to be weaker than the last, largely because interest rates are returning to normal levels, and he grants directly that venture returns over the past decade have not objectively beaten the Nasdaq. The first segment ends on what actually lets an investor hold through a downturn: the temperament to stay invested, and a structure with no capital calls to meet. From there the conversation moves to where fund size and fee income pull a manager's incentives away from the people whose money they manage, and why the current buildout may nonetheless require very large funds. Ryan is asked whether making a firm more rigorous can make it worse — whether a fund can pressure-test its way out of the one bet that would have returned it — and where he draws the line between the work he hands to AI systems and the work he insists a human keep. The last third turns to the audience's own problem: how a founder with no revenue and no product should present a real probability of failure to an investor, and what the best of them have stopped hiding.

Mark DePristo, former CEO of BigHat Biosciences, reveals why the best biotech innovation doesn't bolt on AI later but bakes it into research from day one. He shifts from scaling a clinical-stage antibody powerhouse to the nimble "zero-to-one" world of Ember AI Studios, where small teams leverage AI to solve overlooked deep-tech problems. Mark illustrates how embedding high-throughput experimental loops into design processes creates a rhythm that feels less like a lab experiment and more like a jazz band finding its collective, synchronized flow.

In this episode of the Machines and Molecules podcast, host Marcus Bühler, the McAfee Professor of Engineering at MIT, discusses the principle of "adaptability" as the most important universal principle of materials for newcomers to absorb. His work focuses on bridging atomistic simulation, multiscale mechanics, and AI-driven materials design, aiming to automate the scientific process. He explains how biological systems, like spider silk and bone, have a built-in ability to change and adapt over time. The conversation explores the limitations of brute-force molecular dynamics (MD) simulations and the shift toward using smarter, goal-directed computational experiments. Bühler details his lab's use of category theory to model the discovery process and train AI agents to find universal principles, create new hypotheses, and automate scientific research.  Finally, we chat about the connections between music, sound, and the material world, which informs his work in AI and the search for unifying theories.

Chris Bahl is founder and CEO of AI Proteins. The conversation delves into the challenges faced by platform companies in the biotech sector, highlighting the financial pressures that lead to the cutting of innovative projects. It discusses how investors prioritize immediate monetization of assets over long-term development, often resulting in the abandonment of potentially successful platforms. Takeaways It's tough out there in the world today to be a platform company. The fate of most platforms in biotech is that it's a cool engine. Investors want to monetize that asset. Hundreds of millions of dollars are needed to get products across the finish line. Investors are not interested in products that are 10 years away. Platforms often become line items that get cut from budgets. Many great platforms are effectively thrown in the trash. Successful platforms are often discarded due to financial pressures. Monetization is key for biotech platforms. Innovation struggles under financial constraints.

Victor Guallar is an ICREA Professor and group leader of the EAPM at the Barcelona Supercomputing Center and Co-Founder of Nostrum Biodiscovery. With a joint PhD from the Autonomous University of Barcelona and UC Berkeley, followed by roles at Columbia University and Washington University, he has built extensive expertise in molecular modeling, enzyme engineering, and drug discovery. At the Barcelona Supercomputing Center, he leads the Atomic and Electronic Protein Modeling group, where his work integrates advanced simulations, machine learning, and quantum mechanics to solve challenges in biophysics and sustainability. Victor’s contributions have resulted in over 120 peer-reviewed publications and recognition through prestigious grants, including the ERC Advanced Grant. In this episode of Machines and Molecules, Victor shares his expertise in leveraging Monte Carlo simulations for protein discovery and optimization. Victor explains the value of simulations in molecular science, detailing how they generate data to predict molecular behavior and improve drug discovery, enzyme engineering, and material science. He contrasts Monte Carlo and molecular dynamics methods, emphasizing their respective strengths and his advancements in creating more efficient simulation tools. Victor also discusses the synergy between simulations and AI, highlighting how combining virtual data with machine learning accelerates innovation and improves accuracy. Drawing from his dual roles in academia and industry, he reflects on the disconnect between academic research and industry needs, advocating for practical applications that make scientific work more impactful. The conversation concludes with insights into the benefits of multidisciplinarity, as Victor shares how diverse interests and experiences have shaped his creativity and career. 00:00 - 01:13 Introduction to Victor Guallar 01:13 - 05:57 Molecular Simulations and Their Applications 05:57 - 10:30 Monte Carlo vs. Molecular Dynamics 10:30 - 13:32 How Simulations Generate Data and Integrate with AI 13:32 - 16:56 Sampling vs. Optimization 16:56 - 20:35 The Role of AI in Molecular Modeling 20:35 - 25:30 Applications of Virtual Data in Drug Discovery & Protein Design 25:30 - 31:00 Victor’s 3rd M Word Category: Knowledge

Markus Müller is an experienced project manager with expertise in biotechnology and bioeconomy, focused on bridging research and industry to advance sustainable bioprocesses. As Project Manager at CLIB – Cluster Industrial Biotechnology, he oversees collaborative initiatives that support innovation across academia and industry. Previously, Markus led the Bio² project at RWTH Aachen University, integrating advanced biosurfactant production into biorefinery processes. With a Master’s in Molecular and Applied Biotechnology and a background in enzyme development, Markus is dedicated to fostering efficient, impactful biotechnological solutions. In this episode of Machines and Molecules, Markus Müller, Project Manager at CLIB – Cluster Industrial Biotechnology, explains the value of cluster organizations in advancing biotechnology by connecting industry players and researchers. Markus emphasizes that CLIB differs from traditional industry groups by acting as a neutral mediator, fostering collaboration rather than lobbying. Key challenges discussed include the difficulty of integrating biotech and AI into established industries, especially due to communication gaps between technical and non-technical stakeholders. Markus highlights biotechnology's role in creating sustainable alternatives to fossil-based processes, such as renewable bio-based plastics, while also addressing the bioeconomy’s potential for recycling and waste reduction. The conversation concludes with insights into effective strategies for bridging gaps between scientific advancements and policy decisions, aiming to create a supportive regulatory environment for biotechnological innovation. 00:00 - 02:30 Introduction to Markus Müller and CLIB 02:30 - 06:15 The Role of Cluster Organizations in Advancing Biotechnology 06:15 - 10:00 How CLIB Differentiates Itself as a Neutral Mediator 10:00 - 15:20 The need for Bioeconomy and differences to Chemistry 15:20 - 20:00 Trends in Biotech Industry / Bioeconomy 20:00 - 23:38 Communicating Complex Scientific Ideas to Policymakers 23:38 - 30:26 Challenges in Biotech Industry 30:26 - 34:56 3rd M-Word

Laura Möller is a seasoned expert in venture capital and entrepreneurship, with a focus on artificial intelligence and technology transfer. She holds leadership roles as Director of the Künstliche Intelligenz Entrepreneurship Zentrum (K.I.E.Z.) and UNITE in Berlin, and is the founder of Paola Ventures. With over a decade of experience, she has built expertise in supporting start-ups and fostering innovation in AI-driven ventures. She holds a Master’s degree in European Studies from Humboldt-Universität. Laura’s broad network and hands-on experience make her a vital asset in connecting entrepreneurs and investors, advancing Berlin’s tech ecosystem. In this episode of Machines and Molecules, Laura Möller, Director of KIEZ Accelerator, discusses supporting AI-driven startups, particularly those rooted in scientific research. She highlights KIEZ’s individualized approach, offering startups access to a strong network of venture capitalists, grants, and expert guidance. Laura emphasizes the challenge for AI and science-based startups in turning cutting-edge technology into practical business solutions.She also shares KIEZ’s vision of uniting accelerators and networks to create interdisciplinary teams of AI and domain experts and bridge gaps between research and commercialization. Laura stresses the need to open the funnel and fully utilize the potential of all researchers in academia, not just those who choose the entrepreneurial path. Laura believes fostering entrepreneurial education early, would be a gamechanger to the European startup ecosystem. 00:00 - 03:40 Introduction to Laura Möller and KIEZ Accelerator 03:40 - 05:45 Individualized Support Offered by KIEZ to Startups 05:45 - 07:40 Long-term Vision of KIEZ 07:40 - 10:45 Common Challenges Faced by AI and Science-Based Startups 10:45 - 15:22 Strategies for Securing Funding After the KIEZ Accelerator 15:22 - 19:50 Enhancing Access to Government Funding in the EU 19:50 - 28:50 Comparison of EU and US Funding and other Factors for Startup Success 28:50 - 32:50 3rd M-Word and Laura’s Mission

Dr. Martin Steinegger is an expert in computational biology and bioinformatics, specializing in large-scale sequence data analysis. He earned his Ph.D. from the Technical University of Munich in collaboration with the Max Planck Institute for Biophysical Chemistry, focusing on methods to cluster and assemble metagenomic sequencing data. Currently an Associate Professor at Seoul National University, his research group develops novel computational methods to analyze microbial communities using machine learning and big data algorithms. Martin is the creator of MMseqs, a highly efficient software suite for protein sequence searches and co-author of AlphaFold2. His work in pathogen detection and metagenomics has made a significant impact on bioinformatics, with a strong commitment to open science and open-source tools. Together with Martin we discuss the importance of productizing research code and the key factors for creating reusable software. He emphasizes the need for user-friendly interfaces, intuitive outputs, and software that doesn't crash. Martin talks about his background in software engineering and how it influenced his approach to developing tools in bioinformatics. Hence, he gives an overview about all the different tools he has developed over the years and for what they are used. Furthermore, he explains the significance of protein structure in understanding protein evolution and function, and highlights the role of his tools MMSeqs and AlphaFold in protein sequence and structure analysis. Martin shares his personal journey from starting in a lower-level school to pursuing higher education and research, driven by his passion for computers and learning. 00:00 - 01:32 Introduction 01:32 - 06:25 Productization of research code 06:25 - 08:20 Testing of software 08:20 - 12:28 Overview about the tools Martin has developed 12:28 - 15:40 The relevance of protein structure 15:40 - 18:00 Structural vs. Statistical approaches 18:00 - 21:00 AlphaFold collaboration and insights 21:00 - 31:10 Martin's personal journey and motivation 31:10 - 33:41 Machines and Molecules theme - 3rd M-Word Season 2, episode 5 - Category Knowledge

In this episode we host Carlos Härtel, an expert in innovation and strategy within the applied sciences. Carlos holds a Habilitation in Mechanical Engineering from ETH Zürich and a PhD in Chemical Engineering from the Technical University of Munich. Currently, he is a Venture Partner at Spacewalk VC, investing in deep tech startups, and a Senior Advisor at Carbon Removal Partners, focusing on carbon withdrawal technologies. He also chairs the Board at Science|Business, fostering collaboration between industry, research, and policy. Previously, Carlos served as CTO at Climeworks, a leader in carbon dioxide air capture technology, and held significant roles at General Electric, including CTO and Chief Innovation Officer for Europe. Additionally, he was a Non-Executive Director at Futurice, a digital consultancy, and Chairman at EUROGIA2020, promoting low-carbon energy technologies. Carlos joins us in our Ecosystem category. We dive into CO2 capturing technologies and their role in climate innovation. Key takeaways include the cost-effectiveness of capturing CO2 at industrial sources, the advanced direct air capture methods Carlos developed at Climeworks, and the challenges of ocean-based CO2 capture. Drawing from his experience, Carlos furthermore discusses the critical factors for advancing hard technical innovations in climate and deep tech. He highlights how market demand drives innovation, addresses the chicken-and-egg dilemma of reliability versus adoption, and emphasizes the role of government policies in creating market demand. His insights reveal what’s needed to overcome barriers and implement complex solutions effectively.

In this episode, we are excited to host Dr. Stanislav Mazurenko, a leading expert in protein engineering and artificial intelligence. With a Ph.D. in applied mathematics and cybernetics from Lomonosov Moscow State University and extensive postdoctoral research at Loschmidt Laboratories, Stanislav now leads research at RECETOX. We delve into molecular dynamics simulations, statistical models, and the application of machine learning to protein design. Key takeaways include the essential role of proteins, the complexities of simulating their dynamics, and the optimization benefits provided by machine learning. Stanislav highlights the synergy between automation and machine learning, and underscores the importance of learning from mistakes in scientific research.

In this episode, we're joined by Matteo Aldegni, Director of Machine Learning Research at Bayer, where he leads a team specializing in Machine Learning applications for chemistry and drug discovery. Matteo delves into the intricacies of Molecular Dynamics, the cornerstone of computer-aided molecular design, shedding light on how it paved the way for modern Machine Learning techniques. From the innovative sampling methods inspired by Molecular Dynamics to the transformative potential of Bayesian optimization, Matteo provides insights into the cutting-edge advancements driving molecular design. Join us as we explore the intersection of first-principles molecular models and Machine Learning, as well as Matteo's vision for the future of molecular design, including the concept of self-driving laboratories and Machine Learning force fields.

In this episode, we welcomed Richard Socher, AI expert and serial entrepreneur, serving as CEO and founder of you.com, a pioneering chat-search assistant, and as founder and managing partner of AIX Ventures, guiding and supporting AI startups. Focusing on this wealth of experience, we delved into the core drivers of AI startup success and investment strategies. Richard emphasized AI's versatility, its seamless integration across diverse use cases, and its immense potential, distinguishing it from other ventures and technologies. Regarding the paramount aspects of startup building and investment decisions, he stressed the importance of assembling top talent, maintaining focus, and avoiding overextension. Having a solid Plan A and a thoughtful approach to pivoting if needed are crucial for success.

A decade ago, Martin Rahmel co-founded a startup with the idea of dissolving catalysts in water for reuse. Today, he leads the Chemical Invention Factory (CIF) in Berlin, a hub for green chemistry startups. CIF's story began simply with providing a brick-and-mortar lab to decrease cost for chemistry startups, now blossoming into a ecosystem of innovation with 10Mio Euros of funding. Martin shares the trials of translating science into business, recalling a year without sales before breaking through with their first client, Lonza. His insights on the mindset needed for startup success are invaluable, emphasizing perseverance and confidence. Beyond CIF, Martin's passion extends to restoring river systems, showcasing his dedication to environmental change. Join us as we explore how self-reflection and an objective perspective are key to driving scientific and sustainability advancements.

This episode features Max Welling, a visionary at the intersection of science and machine learning. Starting under Nobel laureate Gerard 't Hooft, Max shifted from the enthralling world of physics to machine learning, inspired by the methods of his early studies. His journey through prestigious labs, including Turing Award winner Geoff Hinton's, illustrates his belief in intuition-driven research. For a long time now, Max has been pioneering ML methods using a broad toolkit of approaches. These days, he is blazing a trail in applications of ML in climate change and scientific advancements. His resilience, shaped by youthful dreams of aviation and overcoming his disappointment in not becoming a fighter jet pilot, fuels his relentless pursuit of knowledge and innovation.

In this enlightening episode, we delve deep with Finnish machine learning grandmaster Aapo Hyvärinen, as he discusses his latest book, “Painful Intelligence”. Hyvärinen, a trailblazer in unsupervised learning, sheds light on the profound connections between human suffering, the pursuit of goals, and the mechanics of learning. His discussion moves from super technical realms of non-gaussian latent variable models to the philosophical, exploring how the connection between machine learning and mindfulness meditation. As we venture into the future of AI, Hyvärinen raises thought-provoking questions about possibilities for the emergence of a survival instinct of AI agents.