How do you drug the undruggable? In this episode of Data in Biotech, Ross Katz sits down with Will Fondrie, Head of Data Science and Engineering at Talus Bio, to explore how machine learning, mass spectrometry, and innovative computational models are transforming drug discovery. Learn how Talus Bio is targeting transcription factors—once considered out of reach—with scalable, high-impact data science.

What You'll Learn in This Episode

• Why transcription factors are historically hard to drug and how Talus Bio is changing that
• How mass spectrometry offers high-throughput, unbiased views of protein-DNA interactions
• The role of recommender systems in prioritizing compound testing
• Strategies for balancing build vs. buy in data infrastructure at scale
• Why open-source software is essential for scientific transparency and progress

Links:

• Find out more about Talus Bio: https://talus.bio
• Connect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/

Connect with Will Fondrie on LinkedIn: https://www.linkedin.com/in/wfondrie/

Meet Our Guest

Will Fondrie is the Head of Data Science and Engineering at Talus Bio, a biotech company pioneering the development of small molecule drugs targeting transcription factors. With a PhD in molecular medicine and a background in proteomics, Will brings deep expertise in computational biology, machine learning, and scalable data systems.

About the Host

Ross Katz is the Principal and Data Science Lead at CorrDyn. He hosts Data in Biotech to spotlight innovative thinkers and data-driven leaders pushing the boundaries of biotechnology.

Enjoying the show? If you liked this episode, consider sharing it with a colleague and exploring more conversations at Data in Biotech. Your support helps us keep delivering expert insights on the future of biotech.

Sponsored by CorrDyn

This episode is brought to you by CorrDyn, a leader in data-driven solutions for biotech and healthcare. Learn more at CorrDyn.

In this episode of the Data and Biotech Podcast, host Ross Katz sits down with Dr. Jonathan Usuka to discuss Usuka’s extensive background in bioinformatics and genomics, leading to his current role as CEO of Sapient. 

The conversation dives into the importance of deep molecular characterization of diseases, the role of proteomics and metabolomics in drug discovery, and the gaps between real-world data and clinical trial data. 

As a leader with a small but impactful team, Jonathan discusses the complexities of the dark proteome and metabolites, emphasizing the opportunity for deeper biological insights in pharmaceutical research using repeated, deep profiling of the same cohorts of patients. Jonathan also explores how advancements in computational approaches allow Sapient to build a robust data foundation for insight generation in biopharma. 

​​What You'll Learn in This Episode:

• The significance of longitudinal patient sampling
• Why we need robust conclusions in biotechnology 
• How the role of the FDA is evolving for drug approval and personalized medicine applications

Connect with Our Guest:

• Sponsor: CorrDyn, a data consultancy
• Find out more about Sapient 
• Connect with Dr. Jonathan Usuka on LinkedIn

Connect with Us:

• Follow the podcast for more insightful discussions on the latest in biotech and data science.
• Subscribe and leave a review if you enjoyed this episode!

How can AI improve clinical trials and accelerate drug development? 

In this episode of Data in Biotech, Ross Katz sits down with Patrick Leung, CTO of Faro Health, to explore how AI-driven tools are reshaping clinical trial design. Patrick shares insights into document generation, patient burden analysis, and AI governance in biotech. 

Learn how Faro Health is developing clinical protocols and leveraging AI to optimize trial success while ensuring regulatory compliance. Whether you're in biotech or healthcare, this conversation offers valuable takeaways on the future of AI in increasing clinical trial efficiency. 

What You'll Learn in This Episode:

• How AI is used to generate clinical trial protocols and reduce inefficiencies.
• The role of AI in assessing patient burden and optimizing trial designs.
• How data model development enables specialized biomedical AI workflows.
• How large language models (LLMs) support clinical trial automation.
• Future trends in AI-driven clinical trial optimization.

Links:

• Find out more about Faro Health: https://www.farohealth.com
• Connect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/

Connect with Patrick Leung on LinkedIn: https://www.linkedin.com/in/puiwah/

Meet Our Guest:

Patrick Leung is the Chief Technology Officer at Faro Health, where he leads AI-driven innovations in clinical trial design. With a background in data science and software engineering from companies like Google and Two Sigma, Patrick brings a fresh perspective to life sciences, focusing on optimizing clinical trials through AI and structured data models.

About the Host:

Ross Katz is the Principal and Data Science Lead at CorrDyn, specializing in applying data science to biotech and healthcare. As the host of Data in Biotech, Ross explores the latest trends and innovations shaping the industry.

Enjoying the Show? Visit Faro Health to learn more about AI-driven clinical trial optimization. Don’t forget to rate and review Data in Biotech on Apple Podcasts!

Sponsored by CorrDyn

This episode is brought to you by CorrDyn, a leader in data-driven solutions for biotech and healthcare. Learn more at CorrDyn.

In this episode of Data In Biotech, Ross Katz interviews Naren Tallapragada, CEO and Co-founder of Tessel Bio, about his background in electrical engineering and physics and how personal circumstances led to him pivoting his focus to enter biotech and start Tessel Bio.

Naren breaks down Tessel Bio’s unique approach to drug discovery, which involves "reverse engineering" chronic diseases. Instead of starting with a potential drug and testing its effects, they begin with a clear picture of the disease itself—specifically, how it appears and behaves in the body. A key part of their method is using human organoids—tiny, lab-grown versions of human tissues—to closely replicate real diseases and see how different treatments interact with them. This “small data” approach is made substantially more efficient with the addition of active learning. Join us for a fascinating conversation about Tessel Bio’s approach to finding cures for chronic diseases that impact hundreds of thousands of people every day. 

​​What You'll Learn in This Episode:

• Naren’s journey into biotech and personal motivation
• Understanding Tessel Bio's approach to drug discovery
• The role of organoids in disease modeling
• Data gathering and target identification strategies
• Active learning in drug discovery at Tessel Bio

Connect with Our Guest:

• Sponsor: CorrDyn, a data consultancy
• Find out more about Tessel Bio 
• Connect with Naren on LinkedIn

Connect with Us:

• Follow the podcast for more insightful discussions on the latest in biotech and data science.
• Subscribe and leave a review if you enjoyed this episode!

In this episode of the Data and Biotech Podcast, host Ross Katz sits down with Alexander Junge, Co-Founder and CTO of amass, to break down how AI is reshaping scientific research in life sciences. 

Alex walks us through how his platform is creating professional tools for researchers, biotech companies, and venture firms to navigate the (rapidly!) growing breadth and depth of scientific knowledge by leveraging artificial intelligence - and what this will look like in the future. 

Alex also shares insights into his company’s work with Nordic Bio Ventures and how amass delivers reliability and builds trust in its platform.

​​What You'll Learn in This Episode:

• What challenges researchers face in formulating questions and retrieving relevant answers from the breadth of scientific knowledge.
• The importance of design and user experience in life sciences, and how the company adopts a rigorous approach to information retrieval.
• How amass helps life sciences professionals navigate complex scientific domains by building trust in answers generated by AI.
• A real-world case study of how amass is assisting Nordic BioVentures a venture firm to build a biotech company.
• Alex’s predictions for AI's impact on scientific discovery over the next decade.

Connect with Our Guest:

• Sponsor: CorrDyn, a data consultancy
• Find out more about amass
• Connect with Alex on LinkedIn

Connect with Us:

• Follow the podcast for more insightful discussions on the latest in biotech and data science.
• Subscribe and leave a review if you enjoyed this episode!

In this episode of Data in Biotech, Ross sits down with Dipen Sangurdekar, VP of Data Sciences at KSQ Therapeutics, to discuss the role of data-driven approaches in therapeutic design and development. The conversation explores the intersection of computational biology, machine learning, and bioinformatics in advancing personalized medicine and improving patient outcomes.

Dipen shares his journey in the industry, emphasizing the importance of integrating data science with biological research and the challenges associated with working in the rapidly evolving field of cell therapies. From hypothesis-driven research to leveraging multimodal data for actionable insights, this episode explores the nuances of using statistical methods and AI to enhance drug development.

Key Takeaways:

• Successful data science in therapeutics requires a deep understanding of both statistical methods and biological processes.
• High-dimensional but low-sample-size data demands a guided hypothesis-driven approach to avoid false positives.
• Data integration and collaboration between computational and biological teams are critical for generating meaningful insights.
• Emerging AI and machine learning tools are enhancing productivity but must be carefully applied in therapeutic research.
• Picking a problem you’re passionate about and going deep into it is crucial for long-term success in the field.

Connect with Our Guest:

• Sponsor: CorrDyn, a data consultancy
• Find out more about KSQ Therapeutics 
• Connect with Dipen on LinkedIn

Connect with Us:

• Follow the podcast for more insightful discussions on the latest in biotech and data science.
• Subscribe and leave a review if you enjoyed this episode!

This week on Data in Biotech, we welcome Timothy Jenkins, the Head of Data Science and Associate Professor at DTU Bioengineering, a leading scientific community dedicated to advancing areas of biotechnology, food technology, and health through innovative teaching and research. 

Timothy starts the conversation by walking us through his background and early career beginnings, from the first time he expressed interest in zoology and venomous snakes to now leading a research group focused on AI-guided drug discovery for snake antivenom.

He and our host, Ross Katz, dive into one of DTU’s most exciting recent publications about "de novo" designed proteins to neutralize lethal snake venom toxins. 

Inspired by Nobel Prize winner David Baker’s groundbreaking paper and in collaboration with the Baker Lab on computational design methodology, this project holds great promise in therapeutic discovery and drug development.

Tim explains how computational protein design and protein structure prediction are revolutionizing his field, highlighting compelling examples and milestones from his research on antivenom.

He also provides an overview of the process used to discover new antivenoms, including the sourcing of biological data, model training, and integration of experimental feedback.

Finally, we get Tim’s perspective on the future of AI-powered therapeutic discovery, and his take on the integration of quantum computing into protein design.

Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences.

Useful Links

Nature - De novo designed proteins neutralize lethal snake venom toxins

Timothy’s LinkedIn

DTU Bioengineering Website

UW Institute for Protein Design

“de novo” designed proteins project

Information on Nobel Prize winner David Baker

In this episode, Ross Katz sits down with Fred Manby, Co-Founder and Chief Technology Officer of Iambic Therapeutics, to explore how cutting-edge AI technologies are reshaping the landscape of drug discovery. From building advanced machine learning platforms to designing user-friendly interfaces for scientists, Fred shares insights into the company’s approach to tackling some of the biggest challenges in biotech.

Fred dives into the unique capabilities of Iambic’s Enchant multi-modal transformer model, its differentiation from other biological foundation models, and the importance of aligning model architecture with data creation and acquisition in modern drug discovery. We also discuss Iambic’s data-driven approach to developing oncology drugs, the exciting possibilities of incorporating new modalities like imaging, and the recent breakthroughs in protein-ligand structure prediction with NeuralPLexer3. 

Highlights:

• Enchant Multi-Modal Transformer Model: How it systematically surpasses state-of-the-art molecular AI technologies.
• Uncertainty Quantification: The role of probabilities and predictions in streamlining drug discovery.

Interface Design for Scientists: Iambic’s approach to integration between AI platforms and experimental workflows.

Connect with Our Guest:

• Sponsor: CorrDyn, a data consultancy
• Find out more about Iambic’s work on their website 

Connect with Fred Manby on LinkedIn

In this episode of Data in Biotech, Ross Katz explores the transformative role of AI in microscopic imaging and life sciences with Ilya Goldberg, Chief Science Officer, and Reese Findley, an AI Data Scientist at ViQi. 

They discuss the cutting-edge applications of AI in automating high-content screening (HCS), enabling more efficient drug discovery, and unraveling complex biological processes. From streamlining time-course analysis to detecting off-target effects in drug compounds, ViQi’s tools are revolutionizing how scientists approach image-based data.

Key Highlights:

• How AI-powered "eyes" analyze thousands of microscopy images to identify phenotypic changes.
• The benefits of using brightfield imaging and live-cell dyes to capture time-course data efficiently.
• Real-world applications of AutoHCS for high-content screening, including drug efficacy studies, stem cell differentiation tracking, and phenotypic clustering for toxicity detection.
• Insights into how automation empowers researchers by reducing repetitive tasks and enhancing focus on innovation.
• The broader implications of AI in medicine, from radiology to pathology, and how it's redefining workflows for specialists.

Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences.

Learn more about who was featured on the podcast:

• CorrDyn, an enterprise data specialist that enables excellent companies to make smarter strategic decisions, at www.corrdyn.com 
• ViQi – helping virologists accelerate, automate and simplify infectivity assays:  www.viqiai.com 

Connect with Reese and Ilya on LinkedIn

In this episode of Data in Biotech, Ross Katz reflects on what he’s learned from one year of hosting the podcast. Diving deep into the intersection of data science and biotechnology, this episode covers topics like:

• The need for predictive models in biotech that are grounded in real-world experimentation.
• The challenges of biases in model evaluation and designing experiments that maximize collecting information for iterative improvements.
• The balance between leveraging computational methods and validating insights through experimental data. 

As we look to 2025, Ross shares his vision of the emerging democratization of the biotech data ecosystem by making domain knowledge, datasets and tools more accessible. He discusses the possibility of a future where decentralized collaboration, akin to open-source software projects, can tackle specific diseases through computational pipelines and cloud labs, enabling experiments without the need for costly infrastructure. Or where emerging trends like foundation models and ensemble modeling in drug discovery, cell and gene therapy, and the role of new data from advanced imaging and assay technologies can be unlocked to create novel insights.

Finally, he invites regular listeners to contribute ideas, guest suggestions and resources as we build community and embrace more curiosity and openness. 

Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences.