ekwan16/MagNET

A library for processing, analyzing and modeling spectroscopic data.

State-of-the-Art Geometric GNN for Predicting Molecular Properties

MIST is a family of molecular foundation models for molecular property prediction. The models were pre-trained on SMILES strings from the Enamine REAL Space dataset using the Masked Language Modeling (MLM) objective, then fine-tuned for downstream prediction tasks.

A Chemprop v2 multi-component MPNN model that predicts 7 spectroscopic properties of organic chromophores from molecular structure (SMILES) and solvent.

Predict olfactory descriptors from SMILES strings. A GINE-based graph neural network trained on the Leffingwell PMP dataset, reproducing the Principal Odor Map (POM) approach.

This repository contains NMRTrans checkpoints for C-NMR + H-NMR + Formula input.