This week, Ross sits down with Mike Nally, CEO at Generate:Biomedicines, a pioneer in generative biology that is transforming the way medicines are developed. Mike joined the Data in Biotech podcast to discuss the AI-driven drug development landscape and how data is set to change the way every drug is made in the future.

Mike shares his journey to Generate:Biomedicines, motivated by the ambition to improve productivity and democratize the availability of drugs.

He discusses the latest in drug development trends, from how the availability of data accelerates what is possible to breakthroughs in de novo generation that allow proteins to be developed with unprecedented specificity. He shares how Generate innovates at each phase of AI-driven drug development and provides insight into Chroma, an open-source diffusion model, explaining how it allows scientists to push the boundaries of protein discovery.

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

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[1:21] Mike gives a quick rundown of his background and the route to his current role as CEO at Generate:Biomedicines.

[4:03] Mike discusses the changes in the availability of data to advance biotechnology.

[6:37] Mike explains the process of designing new proteins and where AI fits into this.

[11:12] Mike introduces Chroma, an open-source diffusion model from Generate:Biomedicines, and explains how it allows scientists to expand the natural universe of proteins.

[16:12] Ross and Mike discuss the challenge of combining biology and computer training.

[18:09] Mike gives his view on the current status of machine learning's role in biotech R&D and how this will evolve.

[21:05] Mike emphasizes the importance of human attention in AI-driven drug discovery and outlines how technological advancements require workflow innovation.

[26:13] Mike highlights teamwork, company culture, and ambition as key differentiators for Generate:Biomedicines.

[28:05] Ross asks Mike his perspective on skepticism around AI-discovered drugs

[30:25] Mike shares updates on the two leading candidates coming out of Generate:Biomedicines.

This week, Nathan Clark, CEO at Ganymede, joins the Data in Biotech podcast to discuss the challenges of integrating lab instruments and data in the biotech industry and how Ganymede’s developer platform is helping to automate data integration and metadata management across Life Sciences.

Nathan sits down with Data in Biotech host Ross Katz to discuss the multiple factors that add to the complexity of handling lab data, from the evolutionary nature of biology to the lab instruments being used. Nathan explains the importance of collecting metadata as unique identifiers that are essential to facilitating automation and data workflows.

As the founder of Ganymede, Nathan outlines the fundamentals of the developer platform and how it has been built to deal practically with the data, workflow, and automation challenges unique to life sciences organizations. He explains the need for code to allow organizations to contextualize and consume data and how the platform is built to enable flexible last-mile integration. He also emphasizes Ganymede's vision to create tools at varying levels of the stack to glue together systems in whatever way is optimal for its specific ecosystem.

As well as giving an in-depth overview of how the Ganymede platform works, he also digs into some of the key challenges facing life sciences organizations as they undergo digital transformation journeys.

The need to engage with metadata from the outset to avoid issues down the line, how to rid organizations of secret Excel files and improve data collection, and the regulatory risks that come with poor metadata handling are all covered in this week’s episode.

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

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[1:28] Nathan gives a quick overview of his background and the path that led him to launch Ganymede.

[5:43] Nathan gives us his perspective on where the complexity of life sciences data comes from.

[8:23] Nathan explains the importance of using code to cope with the high levels of complexity and how the Ganymede developer platform facilitates this.

[11:26] Nathan summarizes the three layers in the Ganymede platform: the ‘core platform’, ‘connectors’ or templates, and ‘transforms’, which allow data to be utilized.

[13:18] Nathan highlights the importance of associating lab data with a unique ID to facilitate data entry and automation.

[15:05] Nathan outlines why the drawbacks of manual data association are inefficient, unreliable, and difficult to maintain.

[16:43] Nathan explains what using Ganymede to manage data and metadata looks like from inside a company.

[24:50] Ross asks Nathan to describe how Ganymede assists with workflow automation and how it can overcome organization-specific challenges.

[27:42] Nathan highlights the challenges businesses are looking to solve when they turn to a solution, like Ganymede, pointing to three common scenarios.

[34:32] Nathan emphasizes the importance of laying the groundwork for a data future at an early stage.

[37:49] Nathan and Ross stress the need for a digital transformation roadmap, with smaller initiatives on the way demonstrating value in their own right.

[40:35] Nathan talks about the future for Ganymede and what is on the horizon for the company and their customers.

Download our latest white paper on “Using Machine Learning to Implement Mid-Manufacture Quality Control in the Biotech Sector.”

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This week, Harshil Patel, Director of Scientific Development at Seqera, joins the Data in Biotech podcast to discuss the importance of collaborative, open-source projects in scientific research and how they support the need for reproducibility.

Harshil lifts the lid on how Nextflow has become a leading open-source workflow management tool for scientists and the benefits of using an open-source model. He talks in detail about the development of Nextflow and the wider Seqera ecosystem, the vision behind it, and the advantages and challenges of this approach to tooling.

He discusses how the nf-core community collaboratively develops and maintains over 100 pipelines using Nextflow and how the decision to constrain pipelines to one per analysis type promotes collaboration and consistency and avoids turning pipelines into the “wild west.”

We also look more practically at Nextflow adoption as Harshil delves into some of the challenges and how to overcome them.

He explores the wider Seqera ecosystem and how it helps users manage pipelines, analysis, and cloud infrastructure more efficiently, and he looks ahead to the future evolution of scientific research.

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

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[1:23] Harshil shares a quick overview of his background in bioinformatics and his route to joining Seqera.

[3:37] Harshil gives an introduction to Nextflow, including its origins, development, and the benefits of using the platform for scientists.

[9:50] Harshil expands on some of the off-the-shelf process pipelines available through NFcore and how this is continuing to expand beyond genomics.

[12:08] Harshil explains NFcore’s open-source model, the advantages of constraining pipelines to one analysis per type, and how the Nextflow community works.

[17:43] Harshil talks about Nextflow's custom DSL and the advantages it offers users

[20:23] Harshil explains how Nextflow fits into the broader Seqera ecosystem.

[26:08] Ross asks Harshil about overcoming some of the challenges that arise with parallelization and optimizing pipelines

[28:01] Harshil talks about the features of Wave, Seqera’s containerization solution.

[32:16] Ross asks Harshil to share some of the most complex and impressive things he has seen done within the Seqera ecosystem.

[35:42] Harshil gives his take on how he sees the future of biotech genomics research evolution.

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Download our latest white paper on “Using Machine Learning to Implement Mid-Manufacture Quality Control in the Biotech Sector.”

Visit this link: https://connect.corrdyn.com/biotech-ml