app.py

import streamlit as st
import os
import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
from rdkit import Chem
from streamlit_ketcher import st_ketcher
from io import StringIO


from openadmet_models.models.gradient_boosting.lgbm import LGBMRegressorModel
from openadmet_models.features.combine import FeatureConcatenator
from openadmet_models.features.molfeat_properties import DescriptorFeaturizer
from openadmet_models.features.molfeat_fingerprint import FingerprintFeaturizer



def _is_valid_smiles(smi):
    if smi is None or smi == "":
        return False
    try:
        m = Chem.MolFromSmiles(smi)
        if m is None:
            return False
        else:
            return True
    except:
        return False


def sdf_str_to_rdkit_mol(sdf):
    from io import BytesIO

    bio = BytesIO(sdf.encode())
    suppl = Chem.ForwardSDMolSupplier(bio, removeHs=False)
    mols = [mol for mol in suppl if mol is not None]
    return mols


@st.cache_data
def convert_df(df):
    # IMPORTANT: Cache the conversion to prevent computation on every rerun
    return df.to_csv().encode("utf-8")



def get_model(path, target, model_type):
    

    model_path = os.path.join(path, f"{model_type}/{target.lower()}_model.json")
    model_file = os.path.join(path, f"{model_type}/{target.lower()}_model.pkl")

    print(model_path, model_file)

    if not os.path.exists(model_path) or not os.path.exists(model_file):
        return None

    model = LGBMRegressorModel.deserialize(model_path, model_file)
    featurizer = FeatureConcatenator(featurizers=[FingerprintFeaturizer(fp_type="ecfp:4"), DescriptorFeaturizer(descr_type="mordred")])
    return model, featurizer

# Set the title of the Streamlit app
st.title("OpenADMET Streamlit DEMO")

# Set the title of the Streamlit app
st.title("OpenADMET Streamlit DEMO")

st.markdown("## Background")

st.markdown(
    "**The [OpenADMET](https://openadmet.org) initiative provides a suite of open-source machine learning models to predict ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, facilitating drug discovery and development.**"
)

st.markdown(
    "This web app enables researchers and scientists to leverage OpenADMET’s models without needing to write or run code, making predictive analytics more accessible."
)
st.markdown("---")
st.markdown("## Input :clipboard:")

input = st.selectbox(
    "How would you like to enter your input?",
    ["Upload a CSV file", "Draw a molecule", "Enter SMILES", "Upload an SDF file"],
    key="input",
)

multismiles = False
if input == "Draw a molecule":
    smiles = st_ketcher(None)
    if _is_valid_smiles(smiles):
        st.success("Valid molecule", icon="✅")
    else:
        st.error("Invalid molecule", icon="🚨")
        st.stop()
    smiles = [smiles]
    queried_df = pd.DataFrame(smiles, columns=["SMILES"])
    smiles_column_name = "SMILES"
    smiles_column = queried_df[smiles_column_name]
elif input == "Enter SMILES":
    smiles = st.text_input("Enter a SMILES string", key="smiles_user_input")
    if _is_valid_smiles(smiles):
        st.success("Valid SMILES string", icon="✅")
    else:
        st.error("Invalid SMILES string", icon="🚨")
        st.stop()
    smiles = [smiles]
    queried_df = pd.DataFrame(smiles, columns=["SMILES"])
    smiles_column_name = "SMILES"
    smiles_column = queried_df[smiles_column_name]
elif input == "Upload a CSV file":
    # Create a file uploader for CSV files
    uploaded_file = st.file_uploader(
        "Choose a CSV file to upload your predictions to", type="csv", key="csv_file"
    )

    # If a file is uploaded, parse it into a DataFrame
    if uploaded_file is not None:
        queried_df = pd.read_csv(uploaded_file)
    else:
        st.stop()
    # Select a column from the DataFrame
    smiles_column_name = st.selectbox("Select a SMILES column", queried_df.columns, key="df_smiles_column")
    multismiles = True
    smiles_column = queried_df[smiles_column_name]

    # check if the smiles are valid
    valid_smiles = [_is_valid_smiles(smi) for smi in smiles_column]
    if not all(valid_smiles):
        st.error(
            "Some of the SMILES strings are invalid, please check the input", icon="🚨"
        )
        st.stop()
    st.success(
        f"All SMILES strings are valid (n={len(valid_smiles)}), proceeding with prediction",
        icon="✅",
    )

elif input == "Upload an SDF file":
    # Create a file uploader for SDF files
    uploaded_file = st.file_uploader(
        "Choose a SDF file to upload your predictions to", type="sdf"
    )
    # read with rdkit
    if uploaded_file is not None:
        # To convert to a string based IO:
        try:
            stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
            # To read file as string:
            string_data = stringio.read()
            mols = sdf_str_to_rdkit_mol(string_data)
            smiles = [Chem.MolToSmiles(m) for m in mols]
            queried_df = pd.DataFrame(smiles, columns=["SMILES"])
        except:
            st.error("Error reading the SDF file, please check the input", icon="🚨")
            st.stop()
    else:
        st.error("No file uploaded", icon="🚨")
        st.stop()

    st.success(
        f"All molecule entries are valid (n={len(queried_df)}), proceeding with prediction",
        icon="✅",
    )
    smiles_column_name = "SMILES"
    smiles_column = queried_df[smiles_column_name]
    multismiles = True

st.markdown("## Model parameters :nut_and_bolt:")




targets = ['CYP3A4', 'CYP2D6', 'CYP2C9']
models = {"ecfp:4 Mordred LGBM":"ecfp4_mordred_lgbm", "ChemProp":"chemprop"}
models_inversed = {v: k for k, v in models.items()}

model_names = list(models.keys())

endpoints = ["pIC50"]

# Select a target value from the preset list
target_value = st.selectbox("Select a biological target ", targets, key="target")
# endpoints


# Select a target value from the preset list
endpoint_value = st.selectbox("Select a property ", endpoints, key="endpoint")

model_value = st.selectbox("Select a model type ", model_names, key="model")


if target_value != "CYP3A4":
    st.write("Only CYP3A4 is currently supported")
    st.stop()

if endpoint_value != "pIC50":
    st.write("Only pIC50 is currently supported")
    st.stop()

if model_value != "ecfp:4 Mordred LGBM":
    st.write("Only ecfp:4 Mordred LGBM is currently supported")
    st.stop()

model, featurizer = get_model("./models", target_value, models[model_value])




if model is None:
    st.write(f"No model found for {target_value} {endpoint_value}")
    st.stop()
    # retry with a different target or endpoint

st.markdown("## Prediction 🚀")


st.write(
    f"Predicting **{target_value} {endpoint_value}** using model:\n\n `{model_value}`"
)

# featurize the smiles
X, _ = featurizer.featurize(smiles_column)
# predict the properties
preds = model.predict(X)

# not implemented yet
err = None


pred_column_name = f"{target_value}_computed-{endpoint_value}"
unc_column_name = f"{target_value}_computed-{endpoint_value}_uncertainty"
queried_df[pred_column_name] = preds
queried_df[unc_column_name] = err

st.markdown("---")
if multismiles:
    # plot the predictions and errors
    # Histogram first
    fig, ax = plt.subplots()

    sorted_df = queried_df.sort_values(by=pred_column_name)
    n_bins = int(len(sorted_df[pred_column_name]) / 10)
    if n_bins < 5:  # makes the histogram slightly more interpretable with low data
        n_bins = 5

    ax.hist(sorted_df[pred_column_name], bins=n_bins)

    ax.set_ylabel("Count")
    ax.set_xlabel(f"Computed {endpoint_value}")
    ax.set_title(f"Histogram of computed {endpoint_value} for target: {target_value}")

    st.pyplot(fig)

    # then a barplot
    fig, ax = plt.subplots()

    ax.bar(range(len(sorted_df)), sorted_df[pred_column_name])

    ax.set_xticks([])
    ax.set_xlabel(f"Query compounds")
    ax.set_ylabel(f"Computed {endpoint_value}")

    ax.set_title(f"Barplot of computed {endpoint_value} for target: {target_value}")

    st.pyplot(fig)

    # if endpoint_value == "pIC50":
    #     from rdkit.Chem.Descriptors import MolWt
    #     import seaborn as sns

    #     # then a scatterplot of uncertainty vs MW
    #     queried_df["MW"] = [
    #         MolWt(Chem.MolFromSmiles(smi)) for smi in sorted_df[smiles_column_name]
    #     ]
    #     fig, ax = plt.subplots()

    #     ax = sns.scatterplot(
    #         x="MW",
    #         y=pred_column_name,
    #         hue=unc_column_name,
    #         palette="coolwarm",
    #         data=queried_df,
    #     )

    #     norm = plt.Normalize(
    #         queried_df[unc_column_name].min(), queried_df[unc_column_name].max()
    #     )
    #     sm = plt.cm.ScalarMappable(cmap="coolwarm", norm=norm)
    #     sm.set_array([])

    #     # Remove the legend and add a colorbar
    #     cbar = ax.figure.colorbar(sm, ax=ax)
    #     ax.annotate(
    #         f"Computed {endpoint_value} uncertainty",
    #         xy=(1.2, 0.3),
    #         xycoords="axes fraction",
    #         rotation=270,
    #     )

    #     ax.set_title(
    #         f"Scatterplot of predicted {endpoint_value} versus MW\ntarget: {target_value}"
    #     )
    #     ax.set_xlabel(f"Molecular weight (Da)")
    #     ax.set_ylabel(f"Computed {endpoint_value}")
    #     st.pyplot(fig)

else:
    # just print the prediction
    preds = queried_df[pred_column_name].values[0]
    smiles = queried_df["SMILES"].values[0]
    if err:
        err = queried_df[unc_column_name].values[0]
        errstr = f"± {err:.2f}"
    else:
        errstr = ""

    st.markdown(
        f"Predicted {target_value} {endpoint_value} for {smiles} is {preds:.2f} {errstr}."
    )

# allow the user to download the predictions
csv = convert_df(queried_df)
st.download_button(
    label="Download data as CSV",
    data=csv,
    file_name=f"predictions_{model_value}.csv",
    mime="text/csv",
)