I’ve tried to curate some of the most popular biotech tools, services, libraries, and ML models and integrate them with a chatbot. This is isn’t an exhaustive list, but aims to be a “first stop” for students and professionals who want to explore the landscape.
I’ve lived in Seattle for four years and had the privilege of meeting people across every corner of the life sciences ecosystem. Through Bits in Bio, I regularly connect with scientists, engineers, students, professors, founders, executives, and investors. Below are a few reflections on what makes the Emerald City’s biotech community unique.
The field of protein modeling has experienced a revolution over the past few years. What began as a longstanding challenge in structural biology—predicting how a linear sequence of amino acids folds into a three-dimensional shape—has now exploded into an entire ecosystem of models for structure prediction, sequence design, and generative biology.
Kubernetes has become the operating system of the cloud, but is often underleveraged in biotech. It has tremendous potential to accelerate R&D by providing a unified platform for workflow and service orchestration that supports diverse use cases and meets business needs.
Generative AI has changed the calculus around “build vs. buy.” As the cost of software development goes down, it becomes far more feasible to solve problems in-house—raising the bar for when SaaS solutions make sense.
Software in biology is complex, and the ecosystem of tools, libraries, models, infrastructure, and SaaS solutions can be overwhelming. Here you'll find insights and perspectives drawn from delivering software solutions to support cutting edge life sciences projects in industry.
