The recent rapid progress in computational protein design promises to unlock advances in biotechnology, medicine, and sustainable chemistry, yet critical gaps remain such as the design of dynamic sensors, bespoke enzymes, and proteins with complex functions. This conference addresses those challenges by bringing together experts in data acquisition, automation, model development and deployment to unlock the next frontiers in protein design.

Primary objectives of this conference are to showcase cutting-edge methods and applications in protein design, to disseminate novel methods that combine deep learning with physical modeling, showcase emerging workflows for custom data collection and automated pipeline integration, and to catalyze collaborations that will accelerate functional protein engineering. Program elements include a hands-on AI-model fine-tuning tutorial, a career-development panel, and a manuscript-writing workshop. By providing these interactive formats, we aim to strengthen collaborations not only among protein designers but also between machine-learning engineers and small-molecule chemists.

What sets this meeting apart is its integrated focus on data generation, automation, and hybrid AI-physics modeling, combined with unique hands-on training and career-focused sessions. No existing forum simultaneously offers this comprehensive blend of technical depth, interdisciplinary networking, and professional development, making it the essential gathering for anyone seeking to lead the next wave of protein-design breakthroughs.