jannisborn commited on
Commit
4b5d582
1 Parent(s): e83e5dc
Files changed (3) hide show
  1. app.py +1 -0
  2. model_cards/examples.csv +3 -1
  3. utils.py +9 -6
app.py CHANGED
@@ -21,6 +21,7 @@ logger.addHandler(logging.NullHandler())
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  MINIMIZATION_FUNCTIONS.pop("callable", None)
 
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  def run_inference(
 
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  MINIMIZATION_FUNCTIONS.pop("callable", None)
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+ MINIMIZATION_FUNCTIONS.pop("molwt", None)
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  def run_inference(
model_cards/examples.csv CHANGED
@@ -1 +1,3 @@
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- v0|["qed"]||1.2|100|10|4|8|4|1|0.1|3|4|42
 
 
 
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+ v0|["qed"]||1.2|100|10|4|8|4|1|0.1|3|4|42
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+ v0|["qed","sa"]||1.2|100|10|4|8|4|1|0.1|3|4|42
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+ v0|["affinity"]|MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTT|1.2|100|10|4|8|4|1|0.1|3|4|42
utils.py CHANGED
@@ -3,7 +3,7 @@ from collections import defaultdict
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  from typing import List, Callable
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  from gt4sd.properties import PropertyPredictorRegistry
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  from gt4sd.algorithms.prediction.paccmann.core import PaccMann, AffinityPredictor
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-
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  import mols2grid
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  import pandas as pd
@@ -13,15 +13,18 @@ logger.addHandler(logging.NullHandler())
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  def get_affinity_function(target: str) -> Callable:
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- return lambda mols: PaccMann(
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- AffinityPredictor(protein_targets=[target] * len(mols), ligands=mols)
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- ).sample(len(mols))
 
 
 
 
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  EVAL_DICT = {
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  "qed": PropertyPredictorRegistry.get_property_predictor("qed"),
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- "sas": PropertyPredictorRegistry.get_property_predictor("sas"),
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- "molwt": PropertyPredictorRegistry.get_property_predictor("molecular_weight"),
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  }
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  from typing import List, Callable
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  from gt4sd.properties import PropertyPredictorRegistry
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  from gt4sd.algorithms.prediction.paccmann.core import PaccMann, AffinityPredictor
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+ import torch
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  import mols2grid
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  import pandas as pd
 
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  def get_affinity_function(target: str) -> Callable:
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+ return lambda mols: torch.stack(
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+ list(
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+ PaccMann(
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+ AffinityPredictor(protein_targets=[target] * len(mols), ligands=mols)
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+ ).sample(len(mols))
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+ )
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+ ).tolist()
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  EVAL_DICT = {
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  "qed": PropertyPredictorRegistry.get_property_predictor("qed"),
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+ "sa": PropertyPredictorRegistry.get_property_predictor("sas"),
 
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  }
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