Discover how AI-driven antibody engineering is revolutionizing the biotech industry with BigHat Bio. Eddie Abrams shares insights on protein engineering, software development, and team organization at BigHat. Learn about the challenges and optimizations in drug discovery, the role of machine learning in antibody design, and the advancements in lab automation for therapeutic development.
Read more
AI Summary
AI Chapters
Episode notes
auto_awesome
Podcast summary created with Snipd AI
Quick takeaways
Antibodies can be engineered to treat diseases like cancer, leading to a rise in antibody-based drugs.
Big Hat Bio utilizes AI to explore the protein space efficiently, potentially enabling breakthroughs in disease treatment.
Deep dives
Antibodies in Drug Development
Antibodies play a crucial role in targeting foreign objects like viruses for destruction in the immune system. These proteins can be engineered to treat diseases like cancer, leading to a rise in antibody-based drugs. Big Hat Bio, a leading player in AI-driven antibody engineering, focuses on safer and more effective antibody therapies for patients. By utilizing machine learning, Big Hat aims to revolutionize antibody design with a closed loop system that analyzes and optimizes antibodies for therapeutic use.
Software Development in Biotech
Eddie Abrams, CIO at Big Hat, transitioned from a philosophy PhD to a software developer, specializing in healthcare and life sciences tech. Big Hat's engineering team operates within a matrix organization, collaborating across sub-teams to address specific challenges in antibody design. The company's tech stack includes PyTorch and PyTorch Lightning for AI-driven insights, requiring a balance of expertise from data science, cloud computing, and lab automation.
Challenges in Therapeutics Development
In the therapeutic space, finding new drugs is becoming increasingly complex and costly, known as E-Room's Law. Big Hat aims to overcome previous limitations by utilizing AI to explore the vast protein space more efficiently. This approach can lead to the targeting of diseases previously deemed unfeasible, reduce costs, and accelerate therapeutic development timelines, potentially enabling breakthroughs in disease treatment.
Automation and Innovation in Engineering
Big Hat focuses on automation to enhance lab efficiency, reduce manual labor, and ensure precision in antibody design. By implementing lab automation and robotic technologies, the company aims to increase resource utilization, improve safety, and accelerate the process of developing high-quality, safe therapeutics. Eddie stresses the importance of employing automation to support experts in focusing on innovation and problem-solving, ultimately aiming for higher success rates in therapeutic development.
Antibodies are a type of protein molecule produced by the immune system. They recognize and attach to other molecules with remarkable precision. Typically antibodies target foreign objects, like viruses, to mark them for destruction. However, they can also be engineered to treat diseases like cancer, and they are one of the fastest growing classes of drugs.
Recently, AI-driven antibody engineering has taken off, and BigHat Bio is one of the leaders of this revolution.
Eddie Abrams is the Chief Information Officer at BigHat. He joins the show to talk about protein engineering, what’s different about software development in biotech, how the engineering team is organized at BigHat, and more.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer .