使用VAE生成药物分子

!pip -q install rdkit-pypi==2021.9.4

import ast

import pandas as pd
import numpy as np

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

import matplotlib.pyplot as plt
from rdkit import Chem, RDLogger
from rdkit.Chem import BondType
from rdkit.Chem.Draw import MolsToGridImage

RDLogger.DisableLog("rdApp.*")

csv_path = keras.utils.get_file(
    "/content/250k_rndm_zinc_drugs_clean_3.csv",
    "https://raw.githubusercontent.com/aspuru-guzik-group/chemical_vae/master/models/zinc_properties/250k_rndm_zinc_drugs_clean_3.csv",
)

df = pd.read_csv("/content/250k_rndm_zinc_drugs_clean_3.csv")
df["smiles"] = df["smiles"].apply(lambda s: s.replace("\n", ""))
df.head()

SMILE_CHARSET = '["C", "B", "F", "I", "H", "O", "N", "S", "P", "Cl", "Br"]'

bond_mapping = {"SINGLE": 0, "DOUBLE": 1, "TRIPLE": 2, "AROMATIC": 3}
bond_mapping.update(
    {0: BondType.SINGLE, 1: BondType.DOUBLE, 2: BondType.TRIPLE, 3: BondType.AROMATIC}
)
SMILE_CHARSET = ast.literal_eval(SMILE_CHARSET)

MAX_MOLSIZE = max(df["smiles"].str.len())
SMILE_to_index = dict((c, i) for i, c in enumerate(SMILE_CHARSET))
index_to_SMILE = dict((i, c) for i, c in enumerate(SMILE_CHARSET))
atom_mapping = dict(SMILE_to_index)
atom_mapping.update(index_to_SMILE)

BATCH_SIZE = 100
EPOCHS = 10

VAE_LR = 5e-4
NUM_ATOMS = 120  # Maximum number of atoms

ATOM_DIM = len(SMILE_CHARSET)  # Number of atom types
BOND_DIM = 4 + 1  # Number of bond types
LATENT_DIM = 435  # Size of the latent space


def smiles_to_graph(smiles):
    # Converts SMILES to molecule object
    molecule = Chem.MolFromSmiles(smiles)

    # Initialize adjacency and feature tensor
    adjacency = np.zeros((BOND_DIM, NUM_ATOMS, NUM_ATOMS), "float32")
    features = np.zeros((NUM_ATOMS, ATOM_DIM), "float32")

    # loop over each atom in molecule
    for atom in molecule.GetAtoms():
        i = atom.GetIdx()
        atom_type = atom_mapping[atom.GetSymbol()]
        features[i] = np.eye(ATOM_DIM)[atom_type]
        # loop over one-hop neighbors
        for neighbor in atom.GetNeighbors():
            j = neighbor.GetIdx()
            bond = molecule.GetBondBetweenAtoms(i, j)
            bond_type_idx = bond_mapping[bond.GetBondType().name]
            adjacency[bond_type_idx, [i, j], [j, i]] = 1

    # Where no bond, add 1 to last channel (indicating "non-bond")
    # Notice: channels-first
    adjacency[-1, np.sum(adjacency, axis=0) == 0] = 1

    # Where no atom, add 1 to last column (indicating "non-atom")
    features[np.where(np.sum(features, axis=1) == 0)[0], -1] = 1

    return adjacency, features


def graph_to_molecule(graph):
    # Unpack graph
    adjacency, features = graph

    # RWMol is a molecule object intended to be edited
    molecule = Chem.RWMol()

    # Remove "no atoms" & atoms with no bonds
    keep_idx = np.where(
        (np.argmax(features, axis=1) != ATOM_DIM - 1)
        & (np.sum(adjacency[:-1], axis=(0, 1)) != 0)
    )[0]
    features = features[keep_idx]
    adjacency = adjacency[:, keep_idx, :][:, :, keep_idx]

    # Add atoms to molecule
    for atom_type_idx in np.argmax(features, axis=1):
        atom = Chem.Atom(atom_mapping[atom_type_idx])
        _ = molecule.AddAtom(atom)

    # Add bonds between atoms in molecule; based on the upper triangles
    # of the [symmetric] adjacency tensor
    (bonds_ij, atoms_i, atoms_j) = np.where(np.triu(adjacency) == 1)
    for (bond_ij, atom_i, atom_j) in zip(bonds_ij, atoms_i, atoms_j):
        if atom_i == atom_j or bond_ij == BOND_DIM - 1:
            continue
        bond_type = bond_mapping[bond_ij]
        molecule.AddBond(int(atom_i), int(atom_j), bond_type)

    # Sanitize the molecule; for more information on sanitization, see
    # https://www.rdkit.org/docs/RDKit_Book.html#molecular-sanitization
    flag = Chem.SanitizeMol(molecule, catchErrors=True)
    # Let's be strict. If sanitization fails, return None
    if flag != Chem.SanitizeFlags.SANITIZE_NONE:
        return None

    return molecule

train_df = df.sample(frac=0.75, random_state=42)  # random state is a seed value
train_df.reset_index(drop=True, inplace=True)

adjacency_tensor, feature_tensor, qed_tensor = [], [], []
for idx in range(8000):
    adjacency, features = smiles_to_graph(train_df.loc[idx]["smiles"])
    qed = train_df.loc[idx]["qed"]
    adjacency_tensor.append(adjacency)
    feature_tensor.append(features)
    qed_tensor.append(qed)

adjacency_tensor = np.array(adjacency_tensor)
feature_tensor = np.array(feature_tensor)
qed_tensor = np.array(qed_tensor)


class RelationalGraphConvLayer(keras.layers.Layer):
    def __init__(
        self,
        units=128,
        activation="relu",
        use_bias=False,
        kernel_initializer="glorot_uniform",
        bias_initializer="zeros",
        kernel_regularizer=None,
        bias_regularizer=None,
        **kwargs
    ):
        super().__init__(**kwargs)

        self.units = units
        self.activation = keras.activations.get(activation)
        self.use_bias = use_bias
        self.kernel_initializer = keras.initializers.get(kernel_initializer)
        self.bias_initializer = keras.initializers.get(bias_initializer)
        self.kernel_regularizer = keras.regularizers.get(kernel_regularizer)
        self.bias_regularizer = keras.regularizers.get(bias_regularizer)

    def build(self, input_shape):
        bond_dim = input_shape[0][1]
        atom_dim = input_shape[1][2]

        self.kernel = self.add_weight(
            shape=(bond_dim, atom_dim, self.units),
            initializer=self.kernel_initializer,
            regularizer=self.kernel_regularizer,
            trainable=True,
            name="W",
            dtype=tf.float32,
        )

        if self.use_bias:
            self.bias = self.add_weight(
                shape=(bond_dim, 1, self.units),
                initializer=self.bias_initializer,
                regularizer=self.bias_regularizer,
                trainable=True,
                name="b",
                dtype=tf.float32,
            )

        self.built = True

    def call(self, inputs, training=False):
        adjacency, features = inputs
        # Aggregate information from neighbors
        x = tf.matmul(adjacency, features[:, None, :, :])
        # Apply linear transformation
        x = tf.matmul(x, self.kernel)
        if self.use_bias:
            x += self.bias
        # Reduce bond types dim
        x_reduced = tf.reduce_sum(x, axis=1)
        # Apply non-linear transformation
        return self.activation(x_reduced)

def get_encoder(
    gconv_units, latent_dim, adjacency_shape, feature_shape, dense_units, dropout_rate
):
    adjacency = keras.layers.Input(shape=adjacency_shape)
    features = keras.layers.Input(shape=feature_shape)

    # Propagate through one or more graph convolutional layers
    features_transformed = features
    for units in gconv_units:
        features_transformed = RelationalGraphConvLayer(units)(
            [adjacency, features_transformed]
        )
    # Reduce 2-D representation of molecule to 1-D
    x = keras.layers.GlobalAveragePooling1D()(features_transformed)

    # Propagate through one or more densely connected layers
    for units in dense_units:
        x = layers.Dense(units, activation="relu")(x)
        x = layers.Dropout(dropout_rate)(x)

    z_mean = layers.Dense(latent_dim, dtype="float32", name="z_mean")(x)
    log_var = layers.Dense(latent_dim, dtype="float32", name="log_var")(x)

    encoder = keras.Model([adjacency, features], [z_mean, log_var], name="encoder")

    return encoder


def get_decoder(dense_units, dropout_rate, latent_dim, adjacency_shape, feature_shape):
    latent_inputs = keras.Input(shape=(latent_dim,))

    x = latent_inputs
    for units in dense_units:
        x = keras.layers.Dense(units, activation="tanh")(x)
        x = keras.layers.Dropout(dropout_rate)(x)

    # Map outputs of previous layer (x) to [continuous] adjacency tensors (x_adjacency)
    x_adjacency = keras.layers.Dense(tf.math.reduce_prod(adjacency_shape))(x)
    x_adjacency = keras.layers.Reshape(adjacency_shape)(x_adjacency)
    # Symmetrify tensors in the last two dimensions
    x_adjacency = (x_adjacency + tf.transpose(x_adjacency, (0, 1, 3, 2))) / 2
    x_adjacency = keras.layers.Softmax(axis=1)(x_adjacency)

    # Map outputs of previous layer (x) to [continuous] feature tensors (x_features)
    x_features = keras.layers.Dense(tf.math.reduce_prod(feature_shape))(x)
    x_features = keras.layers.Reshape(feature_shape)(x_features)
    x_features = keras.layers.Softmax(axis=2)(x_features)

    decoder = keras.Model(
        latent_inputs, outputs=[x_adjacency, x_features], name="decoder"
    )

    return decoder

class Sampling(layers.Layer):
    def call(self, inputs):
        z_mean, z_log_var = inputs
        batch = tf.shape(z_log_var)[0]
        dim = tf.shape(z_log_var)[1]
        epsilon = tf.keras.backend.random_normal(shape=(batch, dim))
        return z_mean + tf.exp(0.5 * z_log_var) * epsilon

class MoleculeGenerator(keras.Model):
    def __init__(self, encoder, decoder, max_len, **kwargs):
        super().__init__(**kwargs)
        self.encoder = encoder
        self.decoder = decoder
        self.property_prediction_layer = layers.Dense(1)
        self.max_len = max_len

        self.train_total_loss_tracker = keras.metrics.Mean(name="train_total_loss")
        self.val_total_loss_tracker = keras.metrics.Mean(name="val_total_loss")

    def train_step(self, data):
        adjacency_tensor, feature_tensor, qed_tensor = data[0]
        graph_real = [adjacency_tensor, feature_tensor]
        self.batch_size = tf.shape(qed_tensor)[0]
        with tf.GradientTape() as tape:
            z_mean, z_log_var, qed_pred, gen_adjacency, gen_features = self(
                graph_real, training=True
            )
            graph_generated = [gen_adjacency, gen_features]
            total_loss = self._compute_loss(
                z_log_var, z_mean, qed_tensor, qed_pred, graph_real, graph_generated
            )

        grads = tape.gradient(total_loss, self.trainable_weights)
        self.optimizer.apply_gradients(zip(grads, self.trainable_weights))

        self.train_total_loss_tracker.update_state(total_loss)
        return {"loss": self.train_total_loss_tracker.result()}

    def _compute_loss(
        self, z_log_var, z_mean, qed_true, qed_pred, graph_real, graph_generated
    ):

        adjacency_real, features_real = graph_real
        adjacency_gen, features_gen = graph_generated

        adjacency_loss = tf.reduce_mean(
            tf.reduce_sum(
                keras.losses.categorical_crossentropy(adjacency_real, adjacency_gen),
                axis=(1, 2),
            )
        )
        features_loss = tf.reduce_mean(
            tf.reduce_sum(
                keras.losses.categorical_crossentropy(features_real, features_gen),
                axis=(1),
            )
        )
        kl_loss = -0.5 * tf.reduce_sum(
            1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var), 1
        )
        kl_loss = tf.reduce_mean(kl_loss)

        property_loss = tf.reduce_mean(
            keras.losses.binary_crossentropy(qed_true, qed_pred)
        )

        graph_loss = self._gradient_penalty(graph_real, graph_generated)

        return kl_loss + property_loss + graph_loss + adjacency_loss + features_loss

    def _gradient_penalty(self, graph_real, graph_generated):
        # Unpack graphs
        adjacency_real, features_real = graph_real
        adjacency_generated, features_generated = graph_generated

        # Generate interpolated graphs (adjacency_interp and features_interp)
        alpha = tf.random.uniform([self.batch_size])
        alpha = tf.reshape(alpha, (self.batch_size, 1, 1, 1))
        adjacency_interp = (adjacency_real * alpha) + (1 - alpha) * adjacency_generated
        alpha = tf.reshape(alpha, (self.batch_size, 1, 1))
        features_interp = (features_real * alpha) + (1 - alpha) * features_generated

        # Compute the logits of interpolated graphs
        with tf.GradientTape() as tape:
            tape.watch(adjacency_interp)
            tape.watch(features_interp)
            _, _, logits, _, _ = self(
                [adjacency_interp, features_interp], training=True
            )

        # Compute the gradients with respect to the interpolated graphs
        grads = tape.gradient(logits, [adjacency_interp, features_interp])
        # Compute the gradient penalty
        grads_adjacency_penalty = (1 - tf.norm(grads[0], axis=1)) ** 2
        grads_features_penalty = (1 - tf.norm(grads[1], axis=2)) ** 2
        return tf.reduce_mean(
            tf.reduce_mean(grads_adjacency_penalty, axis=(-2, -1))
            + tf.reduce_mean(grads_features_penalty, axis=(-1))
        )

    def inference(self, batch_size):
        z = tf.random.normal((batch_size, LATENT_DIM))
        reconstruction_adjacency, reconstruction_features = model.decoder.predict(z)
        # obtain one-hot encoded adjacency tensor
        adjacency = tf.argmax(reconstruction_adjacency, axis=1)
        adjacency = tf.one_hot(adjacency, depth=BOND_DIM, axis=1)
        # Remove potential self-loops from adjacency
        adjacency = tf.linalg.set_diag(adjacency, tf.zeros(tf.shape(adjacency)[:-1]))
        # obtain one-hot encoded feature tensor
        features = tf.argmax(reconstruction_features, axis=2)
        features = tf.one_hot(features, depth=ATOM_DIM, axis=2)
        return [
            graph_to_molecule([adjacency[i].numpy(), features[i].numpy()])
            for i in range(batch_size)
        ]

    def call(self, inputs):
        z_mean, log_var = self.encoder(inputs)
        z = Sampling()([z_mean, log_var])

        gen_adjacency, gen_features = self.decoder(z)

        property_pred = self.property_prediction_layer(z_mean)

        return z_mean, log_var, property_pred, gen_adjacency, gen_features

vae_optimizer = tf.keras.optimizers.Adam(learning_rate=VAE_LR)

encoder = get_encoder(
    gconv_units=[9],
    adjacency_shape=(BOND_DIM, NUM_ATOMS, NUM_ATOMS),
    feature_shape=(NUM_ATOMS, ATOM_DIM),
    latent_dim=LATENT_DIM,
    dense_units=[512],
    dropout_rate=0.0,
)
decoder = get_decoder(
    dense_units=[128, 256, 512],
    dropout_rate=0.2,
    latent_dim=LATENT_DIM,
    adjacency_shape=(BOND_DIM, NUM_ATOMS, NUM_ATOMS),
    feature_shape=(NUM_ATOMS, ATOM_DIM),
)

model = MoleculeGenerator(encoder, decoder, MAX_MOLSIZE)

model.compile(vae_optimizer)
history = model.fit([adjacency_tensor, feature_tensor, qed_tensor], epochs=EPOCHS)

molecules = model.inference(1000)

MolsToGridImage(
    [m for m in molecules if m is not None][:1000], molsPerRow=5, subImgSize=(260, 160)
)

def plot_latent(vae, data, labels):
    # display a 2D plot of the property in the latent space
    z_mean, _ = vae.encoder.predict(data)
    plt.figure(figsize=(12, 10))
    plt.scatter(z_mean[:, 0], z_mean[:, 1], c=labels)
    plt.colorbar()
    plt.xlabel("z[0]")
    plt.ylabel("z[1]")
    plt.show()


plot_latent(model, [adjacency_tensor[:8000], feature_tensor[:8000]], qed_tensor[:8000])