Our key idea: use ML to invert emerging physics-based models (Dimer, FINCHES, RPA) for de novo IDP design.
Our framework uses gradient-based sequence design for target properties and is:
fast: proteome-scale (10,000 seq) < 1 min on GPU flexible: change loss, change property
