Impact AI Searching for New Therapeutics in Nature with David Healey from Enveda Biosciences
New therapeutics refer to newly developed drugs, treatments, or interventions designed to prevent, treat, or cure diseases or medical conditions. The process of discovering new therapeutics is a complex and challenging task that requires significant resources and expertise.
In today’s episode, I sit down with David Healey, the Vice President of Data Science at Enveda Biosciences, to discuss searching for new therapeutics in nature. Enveda Biosciences is a cutting-edge biotech company revolutionizing drug discovery processes using automation and machine learning. It has a unique approach involving mapping the vast unknown chemical space in nature to identify potential therapeutics. David is a data scientist with a knack for machine learning in life sciences. He has expertise in deep neural networks, computer vision, natural language, and graph models, including a solid background in drug discovery, cheminformatics, metabolomics, and experimental biology.
In our conversation, we talk about the role of machine learning in drug discovery and the importance of developing treatments. We discuss using big data for drug discovery, the challenges and opportunities of the field, the hurdles of working with mass spectrometry data, and Enveda Biosciences’s approach to research. Hear how Enveda Biosciences finds the best talent, why drug discovery is an exciting field, and much more.
Key Points:
- David’s background leading up to his role at Enveda Biosciences.
- What Enveda Biosciences focuses on and their approach to drug discovery.
- Learn about mass spectrometry, tandem mass spectrometry, and chromatography.
- The role of machine learning in biosciences and how it is used with mass spectrometry.
- Enveda Bioscience’s applies machine learning differently.
- He explains the challenges encountered when working with mass spectrometry data.
- Find out the value of large language models and other advances in the field.
- We unpack the niche nature of the work Enveda Biosciences is doing.
- Overview of the different types of experts that are working at Enveda Biosciences.
- David shares what recruiting approaches have been most successful for the company.
- Advice that David has for other AI-powered startups.
- He tells us about the impact he wants Enveda Biosciences to have in the future.
Quotes:
“Fundamentally, what we are doing at Enveda is looking for active molecules in nature. What that involves is trying to learn what the molecules are that nature produces, and what they do.” — David Healey
“We are using machine learning to sort of interpret the language of the mass spectrometry, and in particular, to treat it like a natural language problem, or like a machine translation problem.” — David Healey
“We do a lot of work on learning better representations of the spectra so that we can compare them with each other in a way that would better approximate the actual similarity of a molecule.” — David Healey
“Really being deliberate about getting the best talent in the door at the very beginning, I think, is really crucial.” — David Healey
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