0.2.1 Release

The new 0.2.1 release supports PyTorch 2.0 and Python 3.10. We fix quite a few bugs based on the suggestions from the community. Thanks everyone for contributing to this library.

Compatibility Changes

• TorchDrug now supports Python versions from 3.7 to 3.10, and PyTorch versions from 1.8 to 2.0. There is no change in the minimal versions, so you can easily update your previous environment to use TorchDrug 0.2.1.
• For PropertyPrediction , we change the its predict function to directly output original values rather than standardized values. This is more intuitive for new users. Note this change is not backward compatible. (#109, thanks to @kanojikajino)

Improvements

• Add batch normalization and dropout in PropertyPrediction
• Support custom edge feature function in GraphConstruction
• Support ESM-2 models in EvolutionaryScaleModeling
• Add full batch evaluation for KnowledgeGraphCompletion
• Support dict, list and tuple in config dict
• Add instructions for installation on Apple silicon (#176, thanks to @migalkin)
• Reduce dependency to matplotlib-base (#141, thanks to @jamesmyatt)

Bug Fixes

• Fix variable names in NeuralLogicProgramming (#126)
• Fix interface for the new esm library (#133)
• Fix the version of AlphaFoldDB to v2 (#137, thanks to @ShoufaChen)
• Fix inconsistent output when using edge features in convolution layers (#53, #140)
• Handle side cases in property optimization (#125, thanks to @jannisborn)
• Fix a bug when using LR schedulers (#148, #152)
• Fix a bug in graph construction (#158)
• Fix a bug in layers.Set2Set (#185)
• Avoid in-place RDKit operations in data.Molecule.from_molecule (#142)
• Fix num_class in PropertyPrediction (#142)
• Fix docker file for new CUDA image (#207, thanks to @cscandore)
• Fix chain ID in data.Protein.from_molecule

Deprecations

• Deprecate functional._size_to_index. Use torch.repeat_interleave instead.