SpectraLoRA: GNN -> Raman

Arcadia Science released SpectraLoRA, a method that post-trains the 22.5-million-parameter DetaNet equivariant graph neural network using SO(3)-equivariant LoRA to predict Hessians, vibrational frequencies, and full Raman spectra from 3D atomic coordinates. Trained on approximately 2 million molecules, the model achieves 60% fingerprint coverage at DFT-level accuracy zero-shot on out-of-distribution drug-like compounds, and a two-stage alignment trick using a 1D U-Net and Natural Evolution Strategies improved experimental fingerprint F1@15 from 0.426 to 0.532 on the RamanBiolib dataset. The open-source code and Colab demo aim to make machine learning surrogates practical for real wet-lab workflows in natural products and pharmaceutical Raman analysis.

Arcadia Science just dropped SpectraLoRA: they take the pretrained 22.5M-param DetaNet equivariant GNN and lightly post-train it with SO 3 -equivariant LoRA AdaLoRA on invariant layers + ELoRA on tensor products to predict Hessians, vibrational frequencies, and full Raman spectra straight from 3D atomic coords. Trained on ~2M molecules QM9, SPICE, etc. , it already hits 60% fingerprint coverage at DFT-level accuracy zero-shot on out-of-distribution drug-like stuff. Then they close the sim-to-real gap with a two-stage alignment trick: a 1D U-Net FiLM-conditioned on Morgan fingerprints + Natural Evolution Strategies optimizing a non-differentiable F1 score. Jumps experimental fingerprint F1@15 from 0.426 → 0.532 on their RamanBiolib set. Code + Colab demo are open-source: https://github.com/Arcadia-Science/spectralora-molecular-alignment Paper: https://doi.org/10.57844/arcadia-kn60-kxsk This feels like the kind of lightweight adaptation + clever alignment hack that could actually make ML surrogates usable in real wet-lab workflows. Anyone playing with Raman for natural products or pharma? Comments URL: https://news.ycombinator.com/item?id=48383872 https://news.ycombinator.com/item?id=48383872 Points: 1 Comments: 0