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  • 2026.07.23 (v1.3.2): point the bundled checkpoint download URLs to the v1.3.1 GitHub release (the URLs shipped in v1.3.1 pointed at the wrong release tag, so checkpoints failed to download); declare previously-undeclared runtime dependencies (platformdirs, requests, scikit-learn, tqdm) so a fresh install imports cleanly; consolidate the separate PyPI and GitHub READMEs into a single README.md; render the encoder architecture/invariance page (docs/source/encoder.md) in the online documentation.
  • 2026.07.22 (v1.3.1): make the 3D molecular encoder fully E(3)-invariant (rotation, reflection, and translation) — the legacy encoder was only O(3)-invariant and changed its output when a molecule was moved in space. The corrected encoder MolConv2 is now the default (aliased as MolConv), with the legacy encoder kept as MolConv1; an optional chirality channel makes it SE(3) (reflection-sensitive) for chiral tasks. Also add optional self-supervised 3D pretraining (MolNet_SSL), batched MS/MS inference, in-memory input (load_dataframe, load_smiles), and per-user checkpoint caching ($MOLNETPACK_HOME).
  • 2026.04.15 (v1.2.2): reorganize source code (scripts consolidated under scripts/, package moved to top-level molnetpack/); add train() and evaluate() methods directly to MolNet so training and evaluation are available from the Python API without using CLI scripts; update all documentation and examples; add scikit-learn and tqdm to declared dependencies.
  • 2025.10.12 (v1.2.1): change the build-backend to "setuptools.build_meta"; fix the error of unmatched inputs in v1.2.0; apply black for better code format.
  • 2025.4.28 (v1.2.0): add online documentation; add scaler option for RT prediction but using scaler makes convergence harder.
  • 2024.12.20 (v1.1.10.post1): fix the bug of importing plot_msms.
  • 2024.08.30 (v1.1.10): modify the arguments and return values in MolNet for better usage; update the documents.
  • 2024.08.29 (v1.1.9): fix the GitHub release address (using v1.1.8 release until update the weights).
  • 2024.08.28 (v1.1.8): release retention time prediction model.
  • 2024.08.27 (v1.1.7): add the chiral-stationary-phase prediction method introduced in "Hong, Y., Welch, C.J., Piras, P. and Tang, H., 2024. Enhanced structure-based prediction of chiral stationary phases for chromatographic enantioseparation from 3D molecular conformations. Analytical Chemistry, 96(6), pp.2351-2359.".
  • 2024.08.19 (v1.1.6): release orbitrap ms/ms model, and codes for fine-tuning our model on customized data; move the model weights on google drive to GitHub release.
  • 2024.07.09 (v1.1.5): enable the local checkpoint path; add evaluation (cosine similarity) step.
  • 2024.07.08 (v1.1.4): enable batched prediction.
  • 2024.03.08 (v1.1.3): add the function save_features, which can be used to save embedded features; these features can then be used in downstream tasks.
  • 2024.02.27 (v1.1.2): fix the independences; update ccs prediction.
  • 2024.02.27 (v1.1.1): PyPI package release.
  • 2023.10.30 (v1.1.0): enlarging training set by MoNA and Waters QTOF datasets.
  • 2023.10.22 (v1.0.2): pretraining on QM9-mu dataset + ETKDG algorithm. We establish a dataset from QM9-mu (dipole moment) with the generated conformations using ETKDG for pretraining 3DMolMS. It helps the model learning knowledge of molecular 3D conformations and pretraining enhances the performance on MS/MS slightly (~0.01 cosine similarity).
  • 2023.09.14 (v1.0.1): data augmentation by flipping atomic coordinates. Notably, this model is sensitive to the geometric structure of molecules. For tasks insensitive to geometric structure, e.g. mass spectrometry is chirally blind, please use data augmentation. However, for the tasks sensitive to geometric structure, e.g. different enantiomers with varying retention times, avoid data augmentation.
  • 2023.06.30 (v1.0.0): initial version.