Data in Biotech

Ask a general AI model how many approved drugs hit a target, and it might tell you three when the real answer is six, sounding just as confident either way. If your team is grounding drug discovery decisions in AI output with no way to trace where the answer came from, you're one regulator's question away from a very expensive problem. Lisa Downey is CEO of DrugBank, a structured biomedical intelligence platform cited in more than 60,000 papers and used by nine of the top 20 global pharma companies. She previously built Clarivate's genomic and rare disease data business from the ground up and held leadership roles at GlobalData, giving her almost 20 years across healthcare and life sciences data. Lisa breaks down why the bottleneck in AI-driven drug discovery has shifted from data scarcity to trustworthy grounding, and what that means for teams making target identification and go/no-go calls. You'll hear how DrugBank's knowledge graph separates causation from correlation, why reproducibility matters more than speed, and what questions to ask before building a reference data layer in-house. This episode covers deterministic versus probabilistic data, human-in-the-loop versus human-over-the-loop curation, and how biopharma teams connect grounding layers to their AI agents through MCP. It's built for data and analytics leaders, R&D teams, and anyone deciding whether to build or buy their biomedical data infrastructure. Key Takeaways - A general model asked how many approved drugs hit PD-L1 will answer with total confidence, and total inaccuracy, missing half the real number without any signal that it's wrong. - Anthropic's own benchmarks found frontier models pulling public genomic data got it right as little as 17% of the time, until a deterministic tool pushed accuracy past 99%. - DrugBank moved from human-in-the-loop curation to human-over-the-loop oversight once its data was connected enough that one expert validating one relationship could cascade trust across dozens of related facts. - Before building or buying a reference data layer, Lisa lays out four questions that separate real infrastructure from marketing, starting with whether every fact traces back to a source and a date. Chapter Markers 00:00 Why data scarcity isn't the real bottleneck anymore 01:22 What drew Lisa to DrugBank's mission 03:04 What DrugBank is and who relies on it 05:03 The grounding layer: completeness and reproducibility 07:28 Anthropic's benchmark on data infrastructure 09:21 The high-stakes decisions DrugBank data informs 12:23 Where lost cycle time actually comes from 14:13 DrugBank versus homegrown knowledge graphs 19:43 Human-in-the-loop versus human-over-the-loop curation 24:12 How DrugBank checks its own data quality 25:37 Deterministic versus probabilistic data explained 28:52 The J&J case: separating causation from correlation 33:06 Connecting DrugBank to your AI stack via MCP 37:28 Four questions to ask before you build or buy 42:15 Where DrugBank fits, and where it doesn't 44:44 AI as an amplifier of both good and bad decisions Useful Links & Resources - Connect with Lisa Downey on LinkedIn (https://www.linkedin.com/in/lisaldowney/) Connect With the Show - Ross Katz on LinkedIn (https://www.linkedin.com/in/b-ross-katz/) - CorrDyn on LinkedIn (https://www.linkedin.com/company/corrdyn/) Have you run into an AI model giving you a confident, wrong answer in your own R&D work? Tell us about it in the comments, we're always looking for real examples for future episodes. Visit corrdyn.com to learn how CorrDyn can help your organization extract value from data. #DataInBiotech #BiotechAI #DrugDiscovery #DataScience #LifeSciences

Most drug discovery genomic data comes from a thin slice of the world, and that bias follows every decision downstream. Your team can run a Mendelian randomization study on 35,000 patients and still walk away with a single signal that doesn't even apply to the population you care about. If your phenotype definitions are fuzzy, more data won't save you. Erika Kvikstad is a computational biologist who led precision medicine for cardiovascular disease at Bristol-Myers Squibb, working on therapies including Camzyos for hypertrophic cardiomyopathy. She now works independently on genomic data equity, focused on how reference populations shape everything from target discovery to clinical trial recruitment. You'll get a practical look at how to evaluate real-world data vendors, why heart failure is nearly impossible to define cleanly from billing codes, and where statistical power breaks down even with tens of thousands of patients. Erika also explains how her team used AI to reconstruct missing imaging data and validate cardiomyopathy diagnoses at scale. This episode covers GWAS studies, Mendelian randomization, UK Biobank, proteome-wide analysis, and the practical gap between biobank-scale data and disease-specific cohorts. It's built for data and analytics leaders working in life sciences who need to understand where genomic bias enters their pipeline, not just that it exists. Clarification Around 57:58–58:24, in discussing the proteome-wide Mendelian randomization study, Erika moved quickly between two related findings. BTN3A2 was identified as a candidate associated with ischemic stroke and potential immune-modulatory biology. Separately, single-cell expression data helped contextualize other candidate signals, including some with enriched expression in cardiomyocyte populations. Cardiomyocyte-enriched expression was not a specific finding for BTN3A2. Chapter Markers 00:00 Whose genome are we designing drugs for 01:34 Erika's path from academic genomics to BMS 03:48 Building the precision medicine strategy at BMS 06:37 Ross shares his own HCM diagnosis 07:09 Why heart failure resists clean definition 11:11 How medication use reclassifies patients 14:35 Imaging as a biomarker, and its data gaps 20:23 Data infrastructure gaps across regions 22:44 What to look for when evaluating a data vendor 27:35 Consortia and biobanked specimens for rare mutations 29:52 Cardiovascular data infrastructure versus oncology 32:29 Where statistical power breaks down 37:07 UK Biobank's strengths and its limits 40:01 Bridging broad biobanks with disease-specific cohorts 44:32 How reference population bias propagates downstream 48:53 Where genomic bias hits hardest in the pipeline 53:18 Inside a proteome-wide Mendelian randomization study 59:42 Choosing the right computational tool for the question 1:06:38 Building globally representative genomic infrastructure 1:08:04 Ross's takeaways on bias and statistical power Useful Links & Resources - Erika on LinkedIn: https://www.linkedin.com/in/erikakvikstad - UK Biobank: https://www.ukbiobank.ac.uk - Alliance for Genomic Discovery: https://alliancegenomicdiscovery.org - SHaRe Registry (DCM Foundation): https://dcmfoundation.org Connect With the Show - Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ - (Ross Katz on X: https://x.com/brosskatz - CorrDyn LinkedIn: https://www.linkedin.com/company/corrdyn/ Have you run into genomic reference bias in your own work? Tell us what it looked like and how your team caught it. Visit corrdyn.com to learn how CorrDyn can help your organization extract value from data. Subscribe to Data in Biotech so you don't miss the next conversation.

Why treating the cell, not the protein, could turn chronic disease treatment into something closer to a cure. You've built single-cell pipelines that spit out clusters, p-values and target lists, but nothing that survives contact with the clinic. What if the clustering method itself is quietly leading you astray? Adam Freund is Founder and CEO of Arda Therapeutics, a biotech using single-cell sequencing to find the pathogenic cells driving chronic disease. He spent seven years as a Principal Investigator at Calico Life Sciences, building a research lab on the biology of ageing and helping grow the company from 15 to more than 200 people, and holds a PhD in Molecular and Cell Biology from UC Berkeley. You'll get a working model for how Arda's discovery engine turns single-cell and spatial transcriptomic data into causal cell targets. Adam explains why a common statistical shortcut in single-cell analysis produces disease signals that don't hold up and how cell depletion could replace daily dosing with a handful of treatments that reset the immune system. Ross and Adam cover how Arda finds pathogenic cell populations across hundreds of donors, why chi-squared tests on cell clusters can substitute cell count for donor count without anyone noticing, and how B-cell depletion therapies proved that removing a cell can beat blocking its pathway. This one is for data science leaders and computational biologists building single-cell pipelines, not listeners after a general intro to drug discovery. Key Takeaways - Chi-squared tests on cell clusters draw their statistical power from the number of cells, not the number of donors, so a single oversampled patient can produce the same p-value as a hundred-donor study. - Rituximab clears 100% of B cells from circulation yet does nothing for lupus because the disease-driving cells live in tissue, not blood, a lesson now shaping where Arda tests its own molecules. - Neighborhood analysis scores each cell by the donor identity of its nearest neighbours rather than forcing cells into predefined clusters, producing a continuous disease-enrichment map with no cluster boundaries. - When depleted cells regrow, they often come back without the trait that made them harmful in the first place, which means a handful of doses can hold a chronic disease in remission for months. Chapter Markers 00:00 Why cell depletion beats pathway blocking 01:05 Welcome Adam Freund to the show 01:30 From Calico Life Sciences to founding Arda 03:29 Why blocking one pathway rarely works 05:32 B-cell depletion as the proof of concept 08:14 Building a modular library of depletion tools 10:46 Single-cell sequencing removes the need for a hypothesis 11:43 Why clustering is a dial, not ground truth 15:24 The chi-squared trap in single-cell analysis 20:40 Neighbourhood analysis and donor-weighted scoring 23:44 Moving from enrichment to causality 26:32 Inside Arda's lead fibrosis program 30:33 Why solid tissue testing beats blood samples 34:25 Simulating depletion in spatial transcriptomic data 38:49 The case for intermittent dosing over daily pills 43:58 The data infrastructure behind Arda's platform 48:46 Where spatial and protein data are heading Useful Links & Resources - Adam Freund on LinkedIn: https://www.linkedin.com/in/adam-freund-0657654 - CorrDyn: https://corrdyn.com Connect With the Show - Host Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ - Host Ross Katz on X: https://x.com/brosskatz - CorrDyn on LinkedIn: https://www.linkedin.com/company/corrdyn/ If your team runs single-cell pipelines, how do you currently decide on the number of clusters, and have you ever checked whether your significance scales with donor count rather than cell count? Tell us in the comments; we're building a running list of data QA checks for biotech data science teams. Visit corrdyn.com to learn how CorrDyn can help your organization extract value from data.

Everyone in biotech agrees AI needs more data. Almost no one is willing to pay for it. If you're trying to build or buy a biotech AI model, you've hit the same wall: predictive performance depends on data your budget doesn't cover, and nobody in the field seems willing to close that gap. John Androsavich runs Ginkgo Datapoints, the bio AI data arm of Ginkgo Bioworks. He trained as an RNA scientist, spent years on the pharma side deciding which technologies were worth buying, and now sells the raw biological data everyone claims to want. Ross and John get into why biotech spends a fraction of what tech spends on data, how automation dropped ADME testing to $199 a compound, and what that unlocks for drug discovery pipelines and data science in biotech more broadly. You'll hear why single-cell foundation models don't scale the way the field expected, and how GPT-5 designed its own lab experiments inside an autonomous facility. This one's for data and analytics leaders in biotech who need a clearer read on where to spend on data generation, and where the field is still guessing. It's less useful if you're after a general AI overview with no biotech specifics. Key Takeaways - One Meta investment in a data-labelling vendor outweighs a full year of AI drug discovery venture funding combined, and dwarfs the entire single-cell data market. Biotech's data spend looks nothing like tech's. - Ginkgo's ADME-1 offering runs at roughly a tenth of standard pricing, which is changing when and how much companies test. Teams are now running full tier-one panels earlier instead of triaging molecules before they've generated the negative data models need. - A recent Microsoft Research paper found single-cell foundation model learning saturates at 200,000 to 2 million cells, out of a possible 20 million. Volume alone isn't the lever people assumed it was. - GPT-5 wrote its own experimental protocols for optimising cell-free protein expression, ran them through Ginkgo's autonomous Nebula lab, and hit the lowest price-per-titer ever recorded in the field. Chapter Markers 00:00 Introducing John Androsavich and Ginkgo Datapoints 01:12 Why Ginkgo launched a bio AI data business 05:03 Which companies benefit most from Datapoints 06:31 The paradox: everyone wants data, no one pays 09:00 How automation drives ADME-1's $199 price point 12:59 Testing the Jevons paradox in biotech data buying 16:05 Do we actually know biotech AI's scaling laws? 20:54 Why foundation model builders resist more data 24:59 What an empirical bake-off for bio AI could look like 29:32 The case against sitting on the sidelines 33:26 Inside the Virtual Cell Pharmacology Initiative 41:57 Where VCP fits among other virtual cell projects 44:50 The Antibody Developability Consortium with Apheris 53:57 Autonomous labs and GPT-5 designing its own experiments 59:38 Advice for mid-stage biotech data strategy 01:01:31 Final thoughts on where bio AI investment is heading Useful Links & Resources - Ginkgo Bioworks: [ginkgobioworks.com](https://www.ginkgobioworks.com) - Related episode: Apheris CEO Robin Rohm on federated co-folding (Data in Biotech) - Related episode: Eliza Appel on Lilly's TuneLab and federated learning (Data in Biotech) - CorrDyn: [corrdyn.com](https://www.corrdyn.com) Connect With the Show - Host LinkedIn (Ross Katz): [linkedin.com/in/b-ross-katz](https://www.linkedin.com/in/b-ross-katz/) - Host X: [x.com/brosskatz](https://x.com/brosskatz) - CorrDyn LinkedIn: [linkedin.com/company/corrdyn](https://www.linkedin.com/company/corrdyn/) Where does your organisation sit on the data investment paralysis John describes? Are you waiting for someone else to prove the scaling laws first, or are you buying the data now? Drop your take in the comments. Visit corrdyn.com to learn how CorrDyn can help your organisation extract value from data. #DataInBiotech #BiotechAI #DrugDiscovery #DataScience #GinkgoBioworks

In this episode of Data in Biotech, host Ross Katz sits down with Woody Sherman, Founder and Chief Innovation Officer at PsiThera, for a conversation on why AI can transform drug discovery's paperwork and code while barely touching the hardest part of the problem: the molecules themselves. Woody's career runs through physical chemistry at MIT; over a decade at Schrödinger building tools the industry still relies on; founding Silicon Therapeutics (where his team took a small molecule STING agonist from concept to clinic in roughly three years); scaling that platform after Roivant's acquisition; and now leading PsiThera's effort to build oral small molecules for immunology targets that today are only reachable with injectable biologics. The conversation digs into why large language models excel at automation, coding, and regulatory writing but hit a wall when the task is predicting how a molecule behaves, what "physical AI" actually means as a category distinct from both LLMs and traditional physics-based simulation, and why representing molecules as quantum mechanical objects rather than text strings or 2D graphs changes what's predictable. Woody also walks through the STING program in detail, why the field's excitement over fast co-folding models like Boltz needs a strong dose of skepticism, and what it takes to build a database and team culture where chemists, biologists, and data scientists can actually understand each other. What you'll learn in this episode: >> Why the contradiction of "AI is transforming drug discovery" and "drugs still take a decade and billions of dollars" can both be true at once. >> How Silicon Therapeutics engineered a small molecule STING agonist to dimerize itself through a quantum mechanical interaction that had never been designed for before. >> What "physical AI" means as a new category built on embeddings from orbital-level, quantum mechanical representations of molecules, rather than language tokens or force-field simulations. >> Why molecular representation is the whole game: the limitations of SMILES strings and 2D graphs versus true 3D, quantum mechanical embeddings like PsiThera's Psiformer model >> Why a widely publicized claim of near-FEP-quality binding affinity at 1,000x the speed didn't hold up under scrutiny. >> How PsiThera captures not just simulation and wet lab data but human chemist judgment and reasoning as structured data, and why building a shared vocabulary across computational and experimental teams is as important as any model. Meet our guest: Woody Sherman, PhD, is Founder and Chief Innovation Officer at PsiThera, a biotechnology company designing oral small molecule drugs for immunology and inflammatory diseases, starting with the TNF superfamily. His career spans physical chemistry research at MIT, more than a decade at Schrödinger developing computational drug discovery tools, founding Silicon Therapeutics (acquired by Roivant), and leading the platform's evolution through PsiThera today. He has published more than 100 peer-reviewed papers spanning molecular dynamics, quantum mechanics, free energy simulations, and machine learning for drug design. Connect with Woody Sherman on LinkedIn: https://www.linkedin.com/in/woodysherman/ About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. https://www.linkedin.com/company/corrdyn/

In this episode of Data in Biotech, host Ross Katz sits down with Paul Finn, Chief Scientific Officer at Oxford Drug Design, for a conversation on what it actually takes to find a drug molecule that works not just on paper but also in the lab, in the cell, and, ultimately, in the clinic. Paul brings four decades of experience across what became GSK, Pfizer, and a series of Oxford-area spinouts and has shepherded a compound all the way to a marketed drug. That perspective gives him a particular kind of skepticism toward AI results that look too good to be true because he's done the work of checking whether they are. The conversation moves through synthesizability as a first-class constraint, why chemistry has proven so much harder for AI than biology, how 3D molecular representation gets closer to the physics that actually matters, and what rigorous multi-parameter optimization looks like when you're trying to kill cancer cells and drug-resistant bacteria at the same time. What you'll learn in this episode: >> Why synthesizability is chronically underestimated and why changing a single atom in a structure can take a molecule from trivially easy to make to practically impossible >> How Oxford Drug Design constrains the generative search to reaction schemes and purchasable building blocks, and why that chemical space is still so vast that novelty is not meaningfully sacrificed >> Why most generative AI models learn from a 2D string representation of a molecule; two steps removed from the 3D physics that govern how a drug actually binds to its target >> How Bayesian optimization over reagent space, rather than molecular space, allows an active learning loop to focus on the structural patterns associated with activity >> Why benchmarking complex models against simple ones is the discipline that exposes false correlations and why Paul and his co-authors were able to recover the Halicin result using methods decades older than deep learning >> What a pharma company should actually ask an AI drug discovery vendor before buying what they're selling Meet our guest: Paul Finn is Chief Scientific Officer at Oxford Drug Design, a computational drug discovery company with roots in Oxford's chemistry department. His career spans over 40 years of computational drug discovery, from early structure-activity modeling in the 1980s through to modern generative AI methods, with deep experience at what became GSK and Pfizer before moving into the Oxford spinout ecosystem. At Oxford Drug Design, Paul leads internal programs in oncology and antibacterial resistance, combining novel computational methods with a rigorous, synthesizability-first approach to multi-parameter optimization. Connect with Paul Finn on LinkedIn: https://uk.linkedin.com/in/paul-finn-2250616 About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ 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! Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. https://www.linkedin.com/company/corrdyn/

In this episode of Data in Biotech, host Ross Katz sits down with Arvind Rao, Professor of Computational Medicine and Bioinformatics at the University of Michigan, for a discussion on the gap between what biomedical AI can do and what it can reliably be trusted to do in clinical practice. Arvind's research sits at the intersection of computational oncology and AI governance and his lab works across H&E histopathology, multiplex immunofluorescence, spatial transcriptomics, and single-cell RNA sequencing, not just to build predictive models, but to understand the full lifecycle from data to model to inference, and to ask where that lifecycle can be trusted and where it can't. The conversation moves through two of his recent papers on SPIFEE, a graph-based framework that replaces scalar interaction scores in the tumor microenvironment with spatially resolved functional representations, and a multimodal framework that traces a path from stained tissue slides to nominated drug targets via morphological pattern discovery and spatial transcriptomic mapping. What you’ll learn in this episode: >> Why the field's central failure is not algorithmic but translational and the gap between a model that performs well on a benchmark and one that can be consistently trusted in a high-stakes clinical setting >> How SPIFEE replaces the conventional scalar edge representation of cell-cell interactions in the tumor microenvironment with spatially resolved functional edges >> How Arvind's multimodal framework moves from H&E pathology slides labeled with clinical outcomes, through morphological pattern discovery via multiple instance learning, to spatial transcriptomic mapping, to the nomination of molecular mechanisms and actionable drug targets >> Why Goodhart's Law applies directly to foundation model evaluation in biology >> What the AI literacy gap costs when it goes unaddressed in healthcare and pharma organizations Meet our guest: Arvind Rao is a Professor of Computational Medicine and Bioinformatics, with a joint appointment in Radiation Oncology, at the University of Michigan. His research focuses on establishing trust in biomedical AI predictions across the full data-to-decision pipeline, integrating H&E histopathology, spatial transcriptomics, multiplex immunofluorescence, and single-cell RNA sequencing to build models that are predictive, interpretable, and biologically credible. Alongside his research, Arvind develops AI literacy programs for healthcare and pharma professionals, helping clinical and procurement teams evaluate and govern AI systems with the rigor those decisions demand. Connect with Arvind Rao on LinkedIn: https://www.linkedin.com/in/arvind-rao-3301301ba/ About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ 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! Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn . https://www.linkedin.com/company/corrdyn/

In this episode of Data in Biotech, host Ross Katz sits down with Sadegh Salehi, Director of Research and Principal Scientist at Overjet, to explore what rigorous model evaluation actually looks like when the stakes are clinical. Overjet builds FDA-cleared vision models that detect and quantify dental disease across billions of X-ray images from thousands of practices - a data problem with a staggering number of dimensions. Thirty-two teeth per adult patient, each with different morphology. Multiple image types capturing different anatomy. Fifteen to twenty sensor manufacturers producing perceptually distinct images, each with different contrast, resolution, and noise characteristics. And disease severity distributions ranging from barely visible early-stage decay to obvious pathology. Sadegh walks through what it takes to evaluate models responsibly across all of those dimensions and discusses why aggregate metrics like F1 score can mask catastrophic failures on specific subgroups, how models find and exploit shortcuts in training data, and why the same flawed sampling that creates gaps in your training set also creates them in your test set. He also traces Overjet's architectural evolution from over twenty narrow task-specific models to a single foundation model they call Unity, explains how treatment plan procedure codes provide a noisy but real production feedback signal, and describes how Overjet became one of the first companies to secure the FDA's Predetermined Change Control Plan (a framework that allows model updates without filing a new clearance each time.) What you’ll learn in this episode: >> Why aggregate evaluation metrics are insufficient for high-stakes medical AI >> How models exploit shortcuts in training data: if all images from a rare sensor in the training set happen to be healthy, the model doesn't learn to read that sensor, it learns that the sensor means healthy, bypassing the visual task entirely and producing systematic false negatives in production >> How Overjet evolved from over twenty narrow, sensor-specific and indication-specific models into a single foundation model called Unity, using noisy labels generated by the small models as the training signal for a much larger backbone, then building independent prediction heads for each clinical indication on top of it >> Why the decision to keep prediction heads architecturally independent from one another was driven as much by FDA regulatory strategy as by modeling considerations >> How Overjet uses dental treatment plan procedure codes as a production monitoring signal Meet our guest: Sadegh Salehi is Director of Research and Principal Scientist at Overjet, where he leads the team responsible for building, evaluating, and deploying FDA-cleared vision models for dental disease detection and quantification. Connect with Sadegh Salehi on LinkedIn: https://www.linkedin.com/in/sadegh-salehi/ About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ 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! Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn . https://www.linkedin.com/company/corrdyn/

In this episode of Data in Biotech, host Ross Katz sits down with Jesse Johnson, founder of Merelogic, a software consulting firm specializing in data infrastructure for biotech organizations. Jesse brings a rare perspective to the conversation: having built data systems at Google where engineers control the data collection function end to end, before moving into biotech, where the biology does what it wants and bench scientists, not engineers, generate the data. The result is a grounded, pragmatic take on one of the most consequential and underappreciated questions in life sciences right now: as bio foundation models fundamentally change the value equation for experimental data, are biotech labs structured to capture that value? Jesse argues the answer is usually no and that the fix is less technical than most assume. It doesn't require a production-grade data pipeline or a cloud architecture. It requires lightweight, human-readable standard operating procedures, clear expectations between computational and wet lab teams, and a data strategy designed not just for the questions you're asking today, but for the ones you don't yet know you'll need to ask. What you’ll learn in this episode: >> Why the transition from tech to biotech requires a fundamental reset of assumptions about data infrastructure and why the biggest difference isn't technical, it's organizational. >> How bio foundation models have flipped the value equation for experimental data by reducing the cost of organizing it while dramatically increasing the potential return >> How the strategic value of proprietary data is evolving in the biotech ecosystem, from Tahoe Therapeutics building an acquirable single-cell dataset to Eli Lilly's Lowe lab using data as currency for partnerships >> Why electronic lab notebooks aren't going anywhere and how the real question facing biotech software teams isn't whether to use an ELN, but how to balance schema rigidity against the flexibility required for the long tail of one-off exploratory assays that no automation pipeline will ever fully capture Meet our guest: Jesse Johnson is the founder of Merelogic, a software consulting firm that works with biotech and biopharma organizations on data infrastructure and data operations strategy. Jesse writes regularly about data strategy for biotech on his Substack, covering topics from bio foundation model adoption to the evolving role of electronic lab notebooks in an AI-augmented research environment. Connect with Jesse Johnson on LinkedIn: https://www.linkedin.com/in/jesse-johnson-biotech/ Follow Merelogic on Linkedin: https://www.linkedin.com/company/merelogic/ About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ 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! Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn .

This week, we're delighted to be joined by Markus Gershater, Chief Scientific Officer and CoFounder of Synthace - a digital experiment platform built for high-performance life science R&D teams to help them run more powerful experiments and accelerate scientific progress. Host Ross Katz speaks with Markus on what’s broken about: What’s broken about how laboratories across the globe run biological experiments at scale The opportunity that exists for researchers in striving to achieve experimental design automation processes Why, as an industry, we must move towards implementing infrastructure that enables multifactorial experiments versus one-factor experiments And the role of AI in making sense of complex systems. --- If you’re a biotech company struggling to transform your business with data, CorrDyn can help. Whether you need to supplement existing technology teams with specialist expertise or launch a data program that lays the groundwork for future internal hires, you can partner with Corrdyn to unlock the potential of your business data - today. Visit connect.corrdyn.com/biotech to learn more. --- Data in Biotech is a fortnightly podcast exploring how companies leverage data to drive innovation in life sciences.

This week, we're delighted to be joined by Michelle Wiest, Director of IVD Biostatistics at Freenome - a high-growth biotech company that creates tools to help prevent, detect, and treat disease. Host Ross Katz speaks with Michelle on the use of biostatistics in the field of diagnostics, what biases can corrupt diagnostic tests and how to catch them early, the different types of data sets that are being used to develop diagnostic models and how to prepare to present data to regulatory bodies such as the FDA. --- If you’re a biotech company struggling to transform your business with data, CorrDyn can help. Whether you need to supplement existing technology teams with specialist expertise or launch a data program that lays the groundwork for future internal hires, you can partner with Corrdyn to unlock the potential of your business data - today. Visit connect.corrdyn.com/biotech to learn more. --- Data in Biotech is a fortnightly podcast exploring how companies leverage data to drive innovation in life sciences.

In this episode of Data in Biotech, host Ross Katz sits down with Kevin Brown, co-founder of Standard BioModel, to explore one of the most ambitious projects in biomedical AI, building a multimodal foundation model that represents the full complexity of a patient across time. Drawing on a career spanning brain-computer interfaces, computer-aided diagnosis at Siemens Healthineers, and oncology data science at Bristol Myers Squibb, Kevin shares the scientific and philosophical journey that led him to a single conviction: a patient is not a document. Rather than reducing a patient to clinical notes, ICD-10 codes, or isolated test results, Standard BioModel's approach maps every available modality - CT imaging, digital pathology, genomics, EKGs, longitudinal EHR data - into a shared latent space, and models how that patient moves through time. The result is a framework designed not just for prediction, but for counterfactual reasoning, clinical trial matching, and personalized intervention, with open-source models already being validated across leading academic medical centers. What you’ll learn in this episode: >> Why reducing a patient to text - clinical notes, radiology reports, genomic assay summaries - and how mapping multimodal data into a shared latent embedding space preserves information that never makes it into the written record >> How Standard BioModel's temporal architecture models patients as trajectories through an abstract embedding space rather than static snapshots, enabling counterfactual reasoning about the likely impact of interventions on a patient's future health trajectory >> Why no single foundation model can own every clinical vertical and how building a highly generalizable base model that facilitates downstream fine-tuning is a more defensible and scalable strategy than building narrow, application-specific models >> How the model handles missing modalities in real-world clinical settings, and why the architecture is designed to function effectively even when not every data type is available for every patient >> Why Standard BioModel has chosen to open-source its models and why broad, institution-specific validation across diverse patient populations is not just a scientific priority, but a prerequisite for trustworthy clinical AI Meet our guest: Kevin Brown is the Founder and CEO of Standard Model Biomedicine, where he builds foundation models for biomedicine. He previously led AI work as Director of Artificial Intelligence at SimBioSys, and held data science and applied ML roles at Bristol Myers Squibb and Siemens Healthineers. With a neuroscience research background from New York University, Kevin’s work spans generative AI and machine learning for biomedical and medical imaging applications. Connect with Kevin Brown on LinkedIn About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Ross Katz 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! Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn .

In this episode of Data in Biotech, Ross Katz sits down with Robert Abel, Chief Scientific Officer of the Platform at Schrödinger, to explore how physics-based computational modeling is transforming drug discovery. Robert unpacks why machine learning alone isn't enough to navigate the vast complexity of chemical space - an estimated at 10⁶⁰ possible drug-like molecules - and how integrating atomistic simulations with ML creates a more accurate, reliable, and scalable approach to identifying viable drug candidates. From free energy perturbation calculations to generative AI, Robert offers a rare inside look at how Schrödinger's technology platform is accelerating the path from target identification to clinical candidate and where the field is headed next. What you’ll learn in this episode: >> Why chemical space (~10⁶⁰ molecules) makes purely data-driven ML approaches fundamentally insufficient for drug discovery, and how physics-based sampling solves the training data problem >> How free energy perturbation (FEP) calculations enable quantitative prediction of protein-ligand binding affinities at near-experimental accuracy (~1.2 kcal/mol RMSE) >> How Schrödinger's active learning framework combines physics-based simulations and ML to triage billions of candidate molecules before committing to wet lab synthesis >> Why Schrödinger operates across three business lines; software licensing, collaborative programs, and proprietary drug discovery and how each strengthens the underlying technology platform >> Where the next frontiers lie: routine anti-target selectivity profiling, retrosynthetic AI integration, and the expanding role of generative ML in de novo molecular design Meet our guest: Robert Abel is Chief Scientific Officer, Platform at Schrödinger, where he helps lead the scientific direction behind computational approaches that support modern drug discovery and molecular design. With a PhD in Chemical Physics from Columbia University and a deep background in computational chemistry, he has held multiple senior science leadership roles at Schrödinger, guiding teams that build and scale scientific methods into production-grade platforms used across research and industry. Connect with Robert Abel on LinkedIn About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Ross Katz 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! Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn .

In this episode of Data in Biotech, host Ross Katz sits down with Ben Locwin, Vice President at Reliant Life Sciences, to explore the evolving landscape of artificial intelligence in biotechnology. Join us as we discuss why nearly every biotech claims to use AI but few actually do, examine successful applications like AlphaFold, and explore the challenges of implementing AI across drug development, manufacturing, and regulatory processes. Ben shares insights on maintaining healthy skepticism, understanding data provenance, and looking ahead to what this year may bring for AI in life sciences. What you’ll learn in this episode: >> The AI hype problem in biotech and why most companies claim to use AI but few actually do. >> AlphaFold as the gold standard and how DeepMind's protein structure prediction model represents the most successful application of AI in biotech >> Data quality over algorithmic sophistication and the critical importance of data provenance, examining primary sources, and understanding that data quality matters more than the complexity of the AI model >> The balance between optimism and evidence-based decision-making, distinguishing between sophisticated AI and advanced statistical modeling Meet our guest: Ben Locwin is a healthcare and life sciences executive and medical scientist known for helping bring pharmaceuticals, vaccines, and medical devices to market faster and with higher quality. A TEDx speaker and seasoned leader, he’s worked across major biotech hubs and has deep expertise in global regulatory pathways, having collaborated with the FDA, EMA, MHRA, PMDA, and more. Connect with Ben Locwin on LinkedIn About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Ross Katz 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! Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn .

In this episode of Data in Biotech , Ross Katz sits down with Kyle Smith and Jacob Mayer from Aprecia Pharmaceuticals to explore how 3D printing is transforming pharmaceutical manufacturing. They dive into the unique binder jetting process, in-cavity printing, and how real-time data and automation are enabling agile, scalable, and precise drug production. Discover how Aprecia's approach is changing the game for clinical trials and personalized medicine. What you'll learn in this episode: >> How Aprecia developed the world’s first FDA-approved 3D printed drug >> Why binder jetting stands out among 3D printing methods in pharma >> How in-cavity 3D printing enables real-time tablet-level data collection >> The future of closed-loop control and digital twins in drug manufacturing >> Why 3D printing is key to agile, distributed, and personalized pharma production Meet our guests: Kyle Smith is President and COO of Aprecia Pharmaceuticals, leading strategic growth and innovation in GMP-regulated pharma manufacturing. With 12+ years at Aprecia, he brings deep expertise in engineering, operations, and technology transfer. Jacob Maye r is Director of Engineering Innovation at Aprecia Pharmaceuticals. With a decade of experience across automation, additive manufacturing, and life sciences, he leads the advancement of 3D printing technologies and integrated pharma systems. About the host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with our guests: Sponsor: CorrDyn, a data consultancy Connect with Jacob Mayer on LinkedIn Connect with Kyle Smith 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! Connect with Ross Katz on LinkedIn Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn .

In this episode of Data in Biotech , host Ross Katz sits down with James Yoder, Founder and CEO of OpenBench, to unpack a radical new approach to early-stage drug discovery. James shares how OpenBench's "success-driven" model shifts risk away from biotech partners by only charging for validated hits. They dive deep into computational screening, molecular modeling, and the company's evolving tech stack that's making hit discovery smarter and more accessible. Discover how data, AI, and strategic collaboration are redefining biotech R&D. What you'll learn in this episode: >> Why OpenBench moved away from SaaS to a success-based service model >> How their computational platform predicts binding affinity and screens trillions of compounds >> The role of data flywheels and ML in improving drug discovery success rates >> Real-world case studies from biotech collaborations >> How OpenBench evaluates druggable targets in one week Meet our guest James Yoder is the Founder and CEO of OpenBench. With a background in statistics, data science, and applied machine learning, he leads OpenBench's mission to deliver validated drug discovery hits through computational innovation and a success-driven business model. About the host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with our guest: Sponsor: CorrDyn, a data consultancy Connect with James Yoder 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! Connect with Ross Katz on LinkedIn Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn .

Brant Peterson, Vice President & Fellow at Valo Health, joins Data in Biotech to explore how his team leverages real-world data, genetic insights, and machine learning to de-risk drug discovery. From building causal DAGs to identifying patient subtypes in neurodegenerative diseases like Parkinson’s, this episode dives deep into a patient-first, data-driven approach to biomedical innovation. What You'll Learn in This Episode: >> How Valo Health uses real-world evidence and EHR data to prioritize drug targets earlier in the development pipeline. >> Why integrating wet lab experiments and causal DAGs accelerates therapeutic validation. >> The importance of genetic pleiotropy and Mendelian randomization in refining disease hypotheses. >> How Valo Health identifies high-impact patient subgroups in neurodegenerative diseases like Parkinson’s and Alzheimer’s. >> Where machine learning models succeed and fall short, in uncovering mechanisms of disease from sparse longitudinal data. Meet Our Guest Brant Peterson is Vice President & Fellow in Data Science at Valo Health. He brings deep expertise in genetics, computational biology, and biomedical innovation. Formerly a Distinguished Data Scientist at Valo and Computational Biologist at Novartis, Brant focuses on leveraging patient-centric data to drive causal discovery in drug development. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Sponsor: CorrDyn, a data consultancy Connect with Brant Peterson 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! Connect with Ross Katz on LinkedIn Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn .

Ander Tallet, co-founder and COO of Dash Bio and CEO of DigitalRadius, joins Ross Katz to discuss transforming the traditional CRO model through automation, transparency, and productization. Drawing on deep experience from Moderna, Science Exchange, and his leadership roles in digital transformation, Ander shares how Dash Bio is slashing turnaround times, improving data quality, and simplifying procurement for biotech companies. This episode unpacks the future of CRO services, strategic procurement, and the power of operational innovation in biotech. What you'll learn in this episode: >> Why traditional CRO models hinder speed and transparency in biotech >> How Dash Bio delivers 90% faster turnaround through automation >> What productizing CRO services really means for the customer experience >> How regulatory requirements shape innovation in clinical bioanalysis >> Why investor buy-in requires solving real, painful problems in biotech Meet our guest Ander Tallett is the co-founder and COO of Dash Bio and the CEO of DigitalRadius, where he leads digital transformation initiatives as one of the largest Smartsheet partners in the ecosystem. He previously served as Chief Strategy Officer at Science Exchange and co-founded Block Mill Capital, a B2B SaaS-focused investment fund shaped by his experience evaluating and implementing more than 100 SaaS platforms. About the host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with our guest: Sponsor: CorrDyn, a data consultancy Connect with Ander Tallet 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! Connect with Ross Katz on LinkedIn Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn .

In this episode of Data in Biotech , Ross Katz chats with Mitra Mosharraf, Chief Scientific Officer at HTD Biosystems, about how AI and machine learning are revolutionizing nanomedicine. They explore the use of AI in drug discovery, formulation, manufacturing, and clinical development, highlighting how data-driven strategies are improving safety, reducing costs, and enabling more personalized therapies in the biotech space. What you'll learn in this episode: >> How AI and ML reduce costs and increase success rates in nanomedicine development. >> Key challenges in nano drug delivery and how machine learning helps overcome them. >> How HTD Biosystems' iFormulate platform speeds up formulation with predictive modeling. >> How wearables and real-time data are reshaping clinical trial design. >> The future of personalized and automated drug delivery systems. Meet our guest Mitra Mosharraf is the Chief Scientific Officer at HTD Biosystems and co-founder of Engimata Inc. With 20+ years of experience, she leads innovation in biologics, nanomedicine, and lipid-based delivery systems. Mitra is a recognized thought leader in pharmaceutical sciences. About the host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Sponsor: CorrDyn, a data consultancy Connect with Mitra Mosharraf 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! Connect with Ross Katz on LinkedIn Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn .

In this episode of Data in Biotech , Ross Katz chats with Jaidev Chakka, Principal Scientist at the University of Mississippi School of Pharmacy, about how 3D bioprinting and AI are reshaping pharmaceutical manufacturing. They explore the development of custom scaffolds for tissue engineering, the integration of gene delivery systems, and how data-driven approaches are enabling smarter, more scalable solutions in personalized medicine. What you'll learn in this episode: >> How 3D printing is enabling customized bone scaffolds and regenerative therapies >> The role of AI in optimizing pharmaceutical 3D printing parameters >> How organoids can act as micro-organs for testing and computation >> The promise and challenge of personalized, on-demand drug manufacturing >> Why collaboration between data scientists and biopharma researchers is critical Meet our guest LR Jaidev Chakka is a Principal Scientist at the University of Mississippi School of Pharmacy, pioneering 3D bioprinting, drug delivery, and organoid research to revolutionize patient care and pharma manufacturing. About the host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Sponsor: CorrDyn, a data consultancy Connect with Jaidev Chakka 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! Connect with Ross Katz on LinkedIn Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn .