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5a492032d5
* Initial commit of chiral changes Initial checkin of chiral feature code Add chiral metric * Update the way chiral features are incorporated into the model Move initialization to new func use default pytorch reset parameters fix initialization for chirals config rename argument of confidence head fix initialization for chirals * refactor: src nest, rename rf2aa to modelhub * refactor: initial commit without projects * Initial commit of chiral changes * Initial checkin of chiral feature code * Add chiral metric * Remove option for double residual connection. Add kq_norm oiptions to base (20250125) config. * Restoring flag * config * rename argument of confidence head * Update the way chiral features are incorporated into the model * config * rename argument of confidence head * Update the way chiral features are incorporated into the model * Initial commit of chiral changes Initial checkin of chiral feature code Add chiral metric * Update the way chiral features are incorporated into the model Move initialization to new func use default pytorch reset parameters fix initialization for chirals config rename argument of confidence head fix initialization for chirals * refactor: new modelhub --------- Co-authored-by: fdimaio <dimaio@uw.edu> Co-authored-by: HaotianZhangAI4Science <haotianzhang@zju.edu.cn>
33 lines
1.1 KiB
YAML
33 lines
1.1 KiB
YAML
dataset:
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_target_: datahub.datasets.datasets.StructuralDatasetWrapper
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save_failed_examples_to_dir: ${paths.data.failed_examples_dir}
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cif_parser_args:
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cache_dir: null
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load_from_cache: false
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save_to_cache: false
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dataset:
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_target_: datahub.datasets.datasets.PandasDataset
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# we will use the example_id as the unique column
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id_column: example_id
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# return all keys (do not subset)
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return_key: null
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transform:
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# common Transform pipeline components for all PDB datasets
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_target_: ${datasets.pipeline_target}
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is_inference: False
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protein_msa_dirs: ${paths.data.protein_msa_dirs}
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rna_msa_dirs: ${paths.data.rna_msa_dirs}
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n_recycles: ${datasets.n_recycles_train}
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crop_size: ${datasets.crop_size}
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n_msa: ${datasets.n_msa}
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diffusion_batch_size: ${datasets.diffusion_batch_size_train}
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max_atoms_in_crop: ${datasets.max_atoms_in_crop}
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weights:
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_target_: datahub.samplers.calculate_weights_for_pdb_dataset_df
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beta: 0.5
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alphas:
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a_prot: 3.0 # 3 for AF-3
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a_nuc: 0.0 # 3 for AF-3
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a_ligand: 1.0 # 1 for AF-3
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a_loi: 5.0 # 5 for AF-3 |