update requirements and docker image

AMR-KG_Database.py

@@ -86,8 +86,7 @@
     divider="orange",
     help="Stats on the underlying data.",
 )
-DATA_DIR = "../data"
-df = pd.read_csv(f"{DATA_DIR}/processed/combined_bioassay_data.tsv", sep="\t")
+df = pd.read_csv("data/processed/combined_bioassay_data.tsv", sep="\t")
 
 
 def get_base_stats():

Dockerfile

@@ -1,5 +1,5 @@
 # Select base image (can be ubuntu, python, shiny etc)
-FROM python:3.12-slim
+FROM python:3.10-slim
 
 # Create user name and home directory variables. 
 # The variables are later used as $USER and $HOME. 
@@ -22,17 +22,16 @@ COPY requirements.txt $HOME/kg/requirements.txt
 COPY pages $HOME/kg/pages/
 COPY AMR-KG_Database.py $HOME/kg/AMR-KG_Database.py
 COPY data $HOME/kg/data/
-COPY models/ $HOME/kg/models/
 COPY start-script.sh $HOME/kg/start-script.sh
+RUN mkdir $HOME/kg/models
 
 RUN apt-get install curl -y
-RUN curl  https://zenodo.org/api/records/3407840/files/chem_phys_rf.pkl -o $HOME/kg/models/chem_phys_rf.pkl -L
-RUN curl  https://zenodo.org/api/records/3407840/files/ecfp_rf.pkl -o $HOME/kg/models/ecfp_rf.pkl -L
-RUN curl  https://zenodo.org/api/records/3407840/files/erg_rf.pkl -o $HOME/kg/models/erg_rf.pkl -L
-RUN curl  https://zenodo.org/api/records/3407840/files/maccs_rf.pkl -o $HOME/kg/models/maccs_rf.pkl -L
-RUN curl  https://zenodo.org/api/records/3407840/files/maccs_xgboost.pickle.dat -o $HOME/kg/models/maccs_xgboost.pickle.dat -L
+# RUN curl  https://zenodo.org/api/records/3407840/files/chem_phys_rf.pkl -o $HOME/kg/models/chem_phys_rf.pkl -L
+# RUN curl  https://zenodo.org/api/records/3407840/files/ecfp_rf.pkl -o $HOME/kg/models/ecfp_rf.pkl -L
+# RUN curl  https://zenodo.org/api/records/3407840/files/erg_rf.pkl -o $HOME/kg/models/erg_rf.pkl -L
+# RUN curl  https://zenodo.org/api/records/3407840/files/maccs_rf.pkl -o $HOME/kg/models/maccs_rf.pkl -L
 RUN curl  https://zenodo.org/api/records/3407840/files/mhfp6_rf.pkl -o $HOME/kg/models/mhfp6_rf.pkl -L
-RUN curl  https://zenodo.org/api/records/3407840/files/rdkit_rf.pkl -o $HOME/kg/models/rdkit_rf.pkl -L
+# RUN curl  https://zenodo.org/api/records/3407840/files/rdkit_rf.pkl -o $HOME/kg/models/rdkit_rf.pkl -L
 
 RUN pip install --no-cache-dir -r requirements.txt \
     && chmod +x start-script.sh \

pages/1_Chemical_Space_Exploration.py

@@ -53,7 +53,7 @@
     You can zoom in, pan, and hover over the points to view the compound structure."""
 )
 
-HtmlFile = open("./amrkg_chemspace.html", "r", encoding="utf-8")
+HtmlFile = open("amrkg_chemspace.html", "r", encoding="utf-8")
 source_code = HtmlFile.read()
 components.html(source_code, height=500, scrolling=True)
 
@@ -69,8 +69,7 @@
     help="Look for sub-structures in the database based in InChI keys.",
 )
 
-DATA_DIR = "../data"
-df = pd.read_csv(f"{DATA_DIR}/processed/combined_bioassay_data.tsv", sep="\t")
+df = pd.read_csv("data/processed/combined_bioassay_data.tsv", sep="\t")
 df["scaffold_inchikey"] = df["compound_inchikey"].str.split("-").str[0]
 
 user_smiles = st.text_input(

pages/2_Model_Prediction.py

@@ -57,215 +57,71 @@
     )
 
 
-# Model selection
-st.markdown("### Select the model to use for prediction:")
-
-cols = st.columns(2)
-with cols[0]:
-    model = st.radio(
-        "#### Model",
-        ("Random Forest", "XGBoost"),
-        index=0,
-        help="Select the model to use for prediction",
-        horizontal=True,
-    )
-
-    st.write("#### Select the fingerprint:")
-    fingerprint = st.radio(
-        "Fingerprint",
-        ("MHFP6", "ECFP4", "RDKIT", "MACCS", "ErG", "ChemPhys"),
-        index=0,
-        help="Select the fingerprint representation to use for prediction",
-    )
-
-    st.write("**The best model combination is Random Forest with MHFP6 fingerprint.**")
-
-with cols[1]:
-
-    metric_df = pd.read_csv("../data/test_metrics.tsv", sep="\t").reset_index(drop=True)
-    metric_df.rename(columns={"Unnamed: 0": "model_name"}, inplace=True)
-    metric_df["model"] = metric_df["model_name"].apply(
-        lambda x: (
-            x.split("_")[1].upper()
-            if len(x.split("_")) < 3
-            else x.split("_")[-1].upper()
-        )
-    )
-    metric_df["fingerprints"] = metric_df["model_name"].apply(
-        lambda x: (
-            x.split("_")[0].upper()
-            if len(x.split("_")) < 3
-            else x.split("_")[0].upper() + "_" + x.split("_")[1].upper()
-        )
-    )
-    metric_df["fingerprints"] = metric_df["fingerprints"].replace(
-        {
-            "ERG": "ErG",
-            "CHEM_PHYS": "ChemPhys",
-        }
-    )
-
-    colors = {
-        "MHFP6": "#3a2c20",
-        "ECFP4": "#b65c11",
-        "RDKIT": "#e7a504",
-        "MACCS": "#719842",
-        "ErG": "#3d8ebf",
-        "ChemPhys": "#901b1b",
-        "RF": "#3a2c20",
-        "XGBOOST": "#719842",
-    }
+st.write(
+    "**The best model combination is Random Forest with MHFP6 fingerprint. \
+    By default, this is used for prediction.**"
+)
 
-    metric_df["accuracy"] = metric_df["accuracy"] * 100
-    plt.figure(figsize=(5, 5))
-    sns.violinplot(x="model", y="accuracy", data=metric_df, palette=colors)
-    plt.xticks(fontsize=12)
-    plt.yticks(fontsize=12)
-    plt.xlabel("Model", fontsize=15)
-    plt.ylabel("Accuracy", fontsize=15)
-    st.pyplot(plt)
+st.header(
+    "🧠 Prediction results",
+    divider="orange",
+    help="Results of the model prediction.",
+)
 
 if st.button("Predict"):
     if uploaded_file is not None:
         smiles_df = pd.read_csv(uploaded_file, header=None)
     else:
         smiles_df = pd.DataFrame(text_input.split("\n"), columns=["smiles"])
 
-    if model == "Random Forest":
-        model_name = "rf"
-    else:
-        model_name = "xgboost"
-
-    if fingerprint == "MHFP6":
-        fingerprint_name = "mhfp6"
-    elif fingerprint == "ECFP4":
-        fingerprint_name = "ecfp4"
-    elif fingerprint == "RDKIT":
-        fingerprint_name = "rdkit"
-    elif fingerprint == "MACCS":
-        fingerprint_name = "maccs"
-    elif fingerprint == "ErG":
-        fingerprint_name = "erg"
-    else:
-        model = "chem_phys"
-
-    logger.info("⏳ Loading models")
+    model_name = "rf"
+    fingerprint_name = "mhfp6"
 
-    if model_name == "rf":
-        model = torch.load(f"../models/{fingerprint_name}_{model_name}.pkl")
-    else:
-        model = pickle.load(
-            open(f"../models/{fingerprint_name}_{model_name}.pickle.dat", "rb")
-        )
+    model = torch.load(f"./models/{fingerprint_name}_{model_name}.pkl")
 
-    logger.info("🔮 Processing SMILES to fingerprints")
+    # warnings.simplefilter("error", InconsistentVersionWarning)
 
-    mfpgen = rdFingerprintGenerator.GetMorganGenerator(
-        radius=4, fpSize=1024
-    )  # ECFP4 fingerprint
-    rdkgen = rdFingerprintGenerator.GetRDKitFPGenerator(
-        fpSize=1024
-    )  # RDKit fingerprint
     mhfp_encoder = MHFPEncoder(n_permutations=2048, seed=42)  # MHFP6 fingerprint
 
-    ecfp_fingerprints = []
-    rdkit_fingerprints = []
-    maccs_fingerprints = []
     mhfp6_fingerprints = []
-    erg_fingerprints = []
-    chem_phys_props = []
+
+    if smiles_df.empty:
+        st.write("No SMILES provided.")
+        st.stop()
 
     for smiles in smiles_df["smiles"].values:
         # Canonicalize the smiles
         try:
             can_smiles = CanonSmiles(smiles)
         except Exception as e:
-            can_smiles = smiles
+            can_smiles = mhfp6_fingerprints.append(None)
+            continue
 
         # Generate the mol object
         mol = MolFromSmiles(can_smiles)
 
         if not mol:
-            ecfp_fingerprints.append(None)
-            rdkit_fingerprints.append(None)
-            maccs_fingerprints.append(None)
-            chem_phys_props.append(None)
-            mhfp_encoder.append(None)
-            erg_fingerprints.append(None)
+            mhfp6_fingerprints.append(None)
             continue
 
-        ecfp_fingerprints.append(mfpgen.GetFingerprint(mol))
-        rdkit_fingerprints.append(rdkgen.GetFingerprint(mol))
-        maccs_fingerprints.append(MACCSkeys.GenMACCSKeys(mol))
         mhfp6_fingerprints.append(mhfp_encoder.encode(can_smiles, radius=3))
-        erg_fingerprints.append(rdReducedGraphs.GetErGFingerprint(mol))
 
         vals = Descriptors.CalcMolDescriptors(mol)
 
-        chem_phys_props.append(
-            {
-                "slogp": round(vals["MolLogP"], 2),
-                "smr": round(vals["MolMR"], 2),
-                "labute_asa": round(vals["LabuteASA"], 2),
-                "tpsa": round(vals["TPSA"], 2),
-                "exact_mw": round(vals["ExactMolWt"], 2),
-                "num_lipinski_hba": rdMolDescriptors.CalcNumLipinskiHBA(mol),
-                "num_lipinski_hbd": rdMolDescriptors.CalcNumLipinskiHBD(mol),
-                "num_rotatable_bonds": vals["NumRotatableBonds"],
-                "num_hba": vals["NumHAcceptors"],
-                "num_hbd": vals["NumHDonors"],
-                "num_amide_bonds": rdMolDescriptors.CalcNumAmideBonds(mol),
-                "num_heteroatoms": vals["NumHeteroatoms"],
-                "num_heavy_atoms": vals["HeavyAtomCount"],
-                "num_atoms": rdMolDescriptors.CalcNumAtoms(mol),
-                "num_stereocenters": rdMolDescriptors.CalcNumAtomStereoCenters(mol),
-                "num_unspecified_stereocenters": rdMolDescriptors.CalcNumUnspecifiedAtomStereoCenters(
-                    mol
-                ),
-                "num_rings": vals["RingCount"],
-                "num_aromatic_rings": vals["NumAromaticRings"],
-                "num_aliphatic_rings": vals["NumAliphaticRings"],
-                "num_saturated_rings": vals["NumSaturatedRings"],
-                "num_aromatic_heterocycles": vals["NumAromaticHeterocycles"],
-                "num_aliphatic_heterocycles": vals["NumAliphaticHeterocycles"],
-                "num_saturated_heterocycles": vals["NumSaturatedHeterocycles"],
-                "num_aromatic_carbocycles": vals["NumAromaticCarbocycles"],
-                "num_aliphatic_carbocycles": vals["NumAliphaticCarbocycles"],
-                "num_saturated_carbocycles": vals["NumSaturatedCarbocycles"],
-                "fraction_csp3": round(vals["FractionCSP3"], 2),
-                "num_brdigehead_atoms": rdMolDescriptors.CalcNumBridgeheadAtoms(mol),
-                "bertz_complexity": GraphDescriptors.BertzCT(mol),
-            }
-        )
-
-    smiles_df["ecfp4"] = ecfp_fingerprints
-    smiles_df["rdkit"] = rdkit_fingerprints
-    smiles_df["maccs"] = maccs_fingerprints
-    smiles_df["chem_phys"] = chem_phys_props
     smiles_df["mhfp6"] = mhfp6_fingerprints
-    smiles_df["erg"] = erg_fingerprints
-
-    logger.info("🏃 Running model")
 
     smiles_df_subset = smiles_df.dropna(subset=[fingerprint_name])[
         ["smiles", fingerprint_name]
     ]
-    if model_name == "rf":
-        predictions = model.predict(smiles_df_subset[fingerprint_name].tolist())
-        prediction_proba = model.predict_proba(
-            smiles_df_subset[fingerprint_name].tolist()
-        )
-        label_classes = model.classes_.tolist()
-    else:
-        predictions = model.predict(smiles_df_subset[fingerprint_name].values)
-        prediction_proba = model.predict_proba(
-            smiles_df_subset[fingerprint_name].values
-        )
-        label_classes = model.classes_.tolist()
 
-    logger.info("✅ Finished task")
+    if smiles_df_subset.empty:
+        st.write("No valid SMILES provided.")
+        st.stop()
+
+    predictions = model.predict(smiles_df_subset[fingerprint_name].tolist())
+    prediction_proba = model.predict_proba(smiles_df_subset[fingerprint_name].tolist())
+    label_classes = model.classes_.tolist()
 
-    st.write("### Predictions")
     smiles_df_subset["Prediction"] = predictions
     probs = []
     for idx, probability in enumerate(prediction_proba):
@@ -274,3 +130,4 @@
     smiles_df_subset["Probability"] = probs
 
     st.dataframe(smiles_df_subset[["smiles", "Prediction", "Probability"]])
+    st.write("Note: The compounds that could not generate fingerprints are not shown.")

requirements.txt

@@ -2,5 +2,7 @@ pandas
 rdkit
 mhfp==1.9.6
 seaborn==0.13.1
-torch
-streamlit==1.36.0
+streamlit==1.36.0
+scikit-learn==1.2.2
+torch==2.1.2
+joblib==1.3.2