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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>
84 lines
3.6 KiB
YAML
84 lines
3.6 KiB
YAML
# @package _global_
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name: af3-elements-as-ligand-atom-names
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# For explanation of the "override" syntax, see: https://hydra.cc/docs/upgrades/1.0_to_1.1/defaults_list_override/
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defaults:
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- override /trainer: af3
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- override /datasets: af3
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- override /model: af3
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tags:
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# list of tags to add to the run ( & on wandb to easily find & filter runs)
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- atom-names
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- experiment
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project: af3
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ckpt_path: /projects/ml/modelhub/inference/weights_with_no_confidence_2025_1_21_new_modelhub.ckpt
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model:
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lr_scheduler:
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base_lr: 0.9e-3 # 1/2 of original learning rate (1.8e-3)
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datasets:
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train:
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pdb:
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probability: 1.0
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sub_datasets:
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interface:
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dataset:
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transform:
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use_element_for_atom_names_of_atomized_tokens: True
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dataset:
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# Same as AF-3 training, but limit to protein-ligand interfaces
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filters:
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# (from before)
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- "deposition_date < '2021-09-30'"
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- "resolution < 9.0"
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- "num_polymer_pn_units <= 300"
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- "cluster.notnull()"
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- >
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~(pn_unit_1_non_polymer_res_names.notnull() and
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pn_unit_1_non_polymer_res_names.str.contains(
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'${resolve_import:cifutils.constants,AF3_EXCLUDED_LIGANDS_REGEX}',
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regex=True))
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- >
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~(pn_unit_2_non_polymer_res_names.notnull() and
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pn_unit_2_non_polymer_res_names.str.contains(
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'${resolve_import:cifutils.constants,AF3_EXCLUDED_LIGANDS_REGEX}',
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regex=True))
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- "is_inter_molecule"
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# only protein-ligand interfaces
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- "(n_prot == 1 and n_nuc == 0 and n_ligand == 1)"
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pn_unit:
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dataset:
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transform:
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use_element_for_atom_names_of_atomized_tokens: True
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dataset:
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# Same as AF-3 training, but limit to protein-ligand interfaces
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filters:
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# (from before)
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- "deposition_date < '2021-09-30'"
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- "resolution < 9.0"
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- "num_polymer_pn_units <= 300"
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- "cluster.notnull()"
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- "~(q_pn_unit_non_polymer_res_names.notnull() and q_pn_unit_non_polymer_res_names.str.contains('${resolve_import:cifutils.constants,AF3_EXCLUDED_LIGANDS_REGEX}', regex=True))"
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# only proteins or ligands
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- "(n_prot == 1 or n_ligand == 1)"
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# Datasets set to null are ignored
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monomer_distillation: null
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val:
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af3_validation:
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dataset:
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transform:
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use_element_for_atom_names_of_atomized_tokens: True
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dataset:
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filters:
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- "n_tokens_total < 400"
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- "interfaces_to_score.str.contains('protein-ligand')"
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# Exclude example where RDKit errors
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- example_id not in ["{['validation']}{7qbs}{1}{[]}"]
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