molecularai/paroutes

Home of the PaRoutes framework for benchmarking multi-step retrosynthesis predictions.

paroutes's Issues

Could you tell me where is the file "route_distances"? I can't find it.

the file "route_distances" is used as follow, path is "analysis/route_clusters.py"

import route_distances.lstm.defaults as defaults
from route_distances.route_distances import route_distances_calculator
from route_distances.clustering import ClusteringHelper
from route_distances.utils.type_utils import RouteDistancesCalculator

How to correctly load PaRoutes models?

Your benchmark is very interesting and I would like to do some experiments with it, but I haven't found instructions on how to use your pre-trained models.
Would you mind telling me whether the following code correctly loads and uses your models?

import numpy as np
import pandas as pd
import h5py
from tensorflow import keras
from rdkit.Chem import AllChem
from rdchiral.main import rdchiralRunText

def get_fingerprint(smiles: str) -> np.ndarray:
    mol = AllChem.MolFromSmiles(smiles)
    assert mol is not None
    fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=2048)  # QUESTION: is this the right fingerprint?
    return np.array(fp, dtype=float)

# Load templates
df_templates = pd.read_hdf("./data/uspto_rxn_n5_unique_templates.hdf5", key="table")

# Load model, defining custom metrics because without these it gave an error...
model = keras.models.load_model(
    "./data/uspto_rxn_n5_keras_model.hdf5", 
    custom_objects={
        "top10_acc": keras.metrics.TopKCategoricalAccuracy(k=10, name="top10_acc"),
        "top50_acc": keras.metrics.TopKCategoricalAccuracy(k=10, name="top50_acc"),
    }
)

# Example use case: run the best reaction for the first 2 targets
test_smiles = ["O=C(O)COCCOCCOCCOCCOCCOCCOCC(F)(F)F", "COc1cc(N)c(Cl)cc1C(=O)NCCCC1CN(Cc2ccccc2)CCO1"]
x = np.stack([get_fingerprint(s) for s in test_smiles])
template_probs = model(x).numpy()
most_likely_reactions = template_probs.argmax(axis=1)

for i, sm in enumerate(test_smiles):
    reactants = rdchiralRunText(df_templates["retro_template"].values[most_likely_reactions[i]], sm)
    print(f"{i}: {reactants} >> {sm}")

This code runs and produces the following output (in particular, the second reaction fails). Is this the output that you would expect?

0: ['CCOC(=O)CBr.OCCOCCOCCOCCOCCOCCOCC(F)(F)F'] >> O=C(O)COCCOCCOCCOCCOCCOCCOCC(F)(F)F
1: [] >> COc1cc(N)c(Cl)cc1C(=O)NCCCC1CN(Cc2ccccc2)CCO1

Thank you in advance for answering my question. Great manuscript and keep up the good open source work! 💯