Drug Molecule Generation with VAE
Author: Victor Basu
Date created: 2022/03/10
Last modified: 2024/12/17
Description: Implementing a Convolutional Variational AutoEncoder (VAE) for Drug Discovery.
Introduction
In this example, we use a Variational Autoencoder to generate molecules for drug discovery. We use the research papers Automatic chemical design using a data-driven continuous representation of molecules and MolGAN: An implicit generative model for small molecular graphs as a reference.
The model described in the paper Automatic chemical design using a data-driven continuous representation of molecules generates new molecules via efficient exploration of open-ended spaces of chemical compounds. The model consists of three components: Encoder, Decoder and Predictor. The Encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the Decoder converts these continuous vectors back to discrete molecule representations. The Predictor estimates chemical properties from the latent continuous vector representation of the molecule. Continuous representations allow the use of gradient-based optimization to efficiently guide the search for optimized functional compounds.
Figure (a) - A diagram of the autoencoder used for molecule design, including the joint property prediction model. Starting from a discrete molecule representation, such as a SMILES string, the encoder network converts each molecule into a vector in the latent space, which is effectively a continuous molecule representation. Given a point in the latent space, the decoder network produces a corresponding SMILES string. A multilayer perceptron network estimates the value of target properties associated with each molecule.
Figure (b) - Gradient-based optimization in continuous latent space. After training a
surrogate model f(z) to predict the properties of molecules based on their latent
representation z, we can optimize f(z) with respect to z to find new latent
representations expected to match specific desired properties. These new latent
representations can then be decoded into SMILES strings, at which point their properties
can be tested empirically.
For an explanation and implementation of MolGAN, please refer to the Keras Example WGAN-GP with R-GCN for the generation of small molecular graphs by Alexander Kensert. Many of the functions used in the present example are from the above Keras example.
Setup
RDKit is an open source toolkit for cheminformatics and machine learning. This toolkit come in handy if one is into drug discovery domain. In this example, RDKit is used to conveniently and efficiently transform SMILES to molecule objects, and then from those obtain sets of atoms and bonds.
Quoting from WGAN-GP with R-GCN for the generation of small molecular graphs):
"SMILES expresses the structure of a given molecule in the form of an ASCII string. The SMILES string is a compact encoding which, for smaller molecules, is relatively human-readable. Encoding molecules as a string both alleviates and facilitates database and/or web searching of a given molecule. RDKit uses algorithms to accurately transform a given SMILES to a molecule object, which can then be used to compute a great number of molecular properties/features."
!pip -q install rdkit-pypi==2021.9.4
import os
os.environ["KERAS_BACKEND"] = "tensorflow"
import ast
import pandas as pd
import numpy as np
import tensorflow as tf
import keras
from keras import layers
from keras import ops
import matplotlib.pyplot as plt
from rdkit import Chem, RDLogger
from rdkit.Chem import BondType
from rdkit.Chem.Draw import MolsToGridImage
RDLogger.DisableLog("rdApp.*")
Dataset
We use the ZINC – A Free Database of Commercially Available Compounds for Virtual Screening dataset. The dataset comes with molecule formula in SMILE representation along with their respective molecular properties such as logP (water–octanal partition coefficient), SAS (synthetic accessibility score) and QED (Qualitative Estimate of Drug-likeness).
csv_path = keras.utils.get_file(
"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(csv_path)
df["smiles"] = df["smiles"].apply(lambda s: s.replace("\n", ""))
df.head()
| smiles | logP | qed | SAS | |
|---|---|---|---|---|
| 0 | CC(C)(C)c1ccc2occ(CC(=O)Nc3ccccc3F)c2c1 | 5.05060 | 0.702012 | 2.084095 |
| 1 | C[C@@H]1CC(Nc2cncc(-c3nncn3C)c2)C[C@@H](C)C1 | 3.11370 | 0.928975 | 3.432004 |
| 2 | N#Cc1ccc(-c2ccc(O[C@@H](C(=O)N3CCCC3)c3ccccc3)... | 4.96778 | 0.599682 | 2.470633 |
| 3 | CCOC(=O)[C@@H]1CCCN(C(=O)c2nc(-c3ccc(C)cc3)n3c... | 4.00022 | 0.690944 | 2.822753 |
| 4 | N#CC1=C(SCC(=O)Nc2cccc(Cl)c2)N=C([O-])[C@H](C#... | 3.60956 | 0.789027 | 4.035182 |
---
## Hyperparameters
```python
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
Generate training set
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="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="float32",
)
self.built = True
def call(self, inputs, training=False):
adjacency, features = inputs
# Aggregate information from neighbors
x = ops.matmul(adjacency, features[:, None])
# Apply linear transformation
x = ops.matmul(x, self.kernel)
if self.use_bias:
x += self.bias
# Reduce bond types dim
x_reduced = ops.sum(x, axis=1)
# Apply non-linear transformation
return self.activation(x_reduced)
Build the Encoder and Decoder
The Encoder takes as input a molecule's graph adjacency matrix and feature matrix.
These features are processed via a Graph Convolution layer, then are flattened and
processed by several Dense layers to derive z_mean and log_var, the
latent-space representation of the molecule.
Graph Convolution layer: The relational graph convolution layer implements non-linearly transformed neighbourhood aggregations. We can define these layers as follows:
H_hat**(l+1) = σ(D_hat**(-1) * A_hat * H_hat**(l+1) * W**(l))
Where σ denotes the non-linear transformation (commonly a ReLU activation), A the
adjacency tensor, H_hat**(l) the feature tensor at the l-th layer, D_hat**(-1) the
inverse diagonal degree tensor of A_hat, and W_hat**(l) the trainable weight tensor
at the l-th layer. Specifically, for each bond type (relation), the degree tensor
expresses, in the diagonal, the number of bonds attached to each atom.
Source: WGAN-GP with R-GCN for the generation of small molecular graphs)
The Decoder takes as input the latent-space representation and predicts the graph adjacency matrix and feature matrix of the corresponding molecules.
def get_encoder(
gconv_units, latent_dim, adjacency_shape, feature_shape, dense_units, dropout_rate
):
adjacency = layers.Input(shape=adjacency_shape)
features = 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 = 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 = layers.Dense(units, activation="tanh")(x)
x = layers.Dropout(dropout_rate)(x)
# Map outputs of previous layer (x) to [continuous] adjacency tensors (x_adjacency)
x_adjacency = layers.Dense(np.prod(adjacency_shape))(x)
x_adjacency = layers.Reshape(adjacency_shape)(x_adjacency)
# Symmetrify tensors in the last two dimensions
x_adjacency = (x_adjacency + ops.transpose(x_adjacency, (0, 1, 3, 2))) / 2
x_adjacency = layers.Softmax(axis=1)(x_adjacency)
# Map outputs of previous layer (x) to [continuous] feature tensors (x_features)
x_features = layers.Dense(np.prod(feature_shape))(x)
x_features = layers.Reshape(feature_shape)(x_features)
x_features = layers.Softmax(axis=2)(x_features)
decoder = keras.Model(
latent_inputs, outputs=[x_adjacency, x_features], name="decoder"
)
return decoder
Build the Sampling layer
class Sampling(layers.Layer):
def __init__(self, seed=None, **kwargs):
super().__init__(**kwargs)
self.seed_generator = keras.random.SeedGenerator(seed)
def call(self, inputs):
z_mean, z_log_var = inputs
batch, dim = ops.shape(z_log_var)
epsilon = keras.random.normal(shape=(batch, dim), seed=self.seed_generator)
return z_mean + ops.exp(0.5 * z_log_var) * epsilon
Build the VAE
This model is trained to optimize four losses:
- Categorical crossentropy
- KL divergence loss
- Property prediction loss
- Graph loss (gradient penalty)
The categorical crossentropy loss function measures the model's reconstruction accuracy. The Property prediction loss estimates the mean squared error between predicted and actual properties after running the latent representation through a property prediction model. The property prediction of the model is optimized via binary crossentropy. The gradient penalty is further guided by the model's property (QED) prediction.
A gradient penalty is an alternative soft constraint on the 1-Lipschitz continuity as an improvement upon the gradient clipping scheme from the original neural network ("1-Lipschitz continuity" means that the norm of the gradient is at most 1 at every single point of the function). It adds a regularization term to the loss function.
class MoleculeGenerator(keras.Model):
def __init__(self, encoder, decoder, max_len, seed=None, **kwargs):
super().__init__(**kwargs)
self.encoder = encoder
self.decoder = decoder
self.property_prediction_layer = layers.Dense(1)
self.max_len = max_len
self.seed_generator = keras.random.SeedGenerator(seed)
self.sampling_layer = Sampling(seed=seed)
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 = ops.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 = ops.mean(
ops.sum(
keras.losses.categorical_crossentropy(
adjacency_real, adjacency_gen, axis=1
),
axis=(1, 2),
)
)
features_loss = ops.mean(
ops.sum(
keras.losses.categorical_crossentropy(features_real, features_gen),
axis=(1),
)
)
kl_loss = -0.5 * ops.sum(
1 + z_log_var - z_mean**2 - ops.minimum(ops.exp(z_log_var), 1e6), 1
)
kl_loss = ops.mean(kl_loss)
property_loss = ops.mean(
keras.losses.binary_crossentropy(qed_true, ops.squeeze(qed_pred, axis=1))
)
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 = keras.random.uniform(shape=(self.batch_size,), seed=self.seed_generator)
alpha = ops.reshape(alpha, (self.batch_size, 1, 1, 1))
adjacency_interp = (adjacency_real * alpha) + (
1.0 - alpha
) * adjacency_generated
alpha = ops.reshape(alpha, (self.batch_size, 1, 1))
features_interp = (features_real * alpha) + (1.0 - 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 - ops.norm(grads[0], axis=1)) ** 2
grads_features_penalty = (1 - ops.norm(grads[1], axis=2)) ** 2
return ops.mean(
ops.mean(grads_adjacency_penalty, axis=(-2, -1))
+ ops.mean(grads_features_penalty, axis=(-1))
)
def inference(self, batch_size):
z = keras.random.normal(
shape=(batch_size, LATENT_DIM), seed=self.seed_generator
)
reconstruction_adjacency, reconstruction_features = model.decoder.predict(z)
# obtain one-hot encoded adjacency tensor
adjacency = ops.argmax(reconstruction_adjacency, axis=1)
adjacency = ops.one_hot(adjacency, num_classes=BOND_DIM, axis=1)
# Remove potential self-loops from adjacency
adjacency = adjacency * (1.0 - ops.eye(NUM_ATOMS, dtype="float32")[None, None])
# obtain one-hot encoded feature tensor
features = ops.argmax(reconstruction_features, axis=2)
features = ops.one_hot(features, num_classes=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 = self.sampling_layer([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
Train the model
vae_optimizer = 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)
Epoch 1/10
250/250 ━━━━━━━━━━━━━━━━━━━━ 125s 481ms/step - loss: 2841.6440
Epoch 2/10
250/250 ━━━━━━━━━━━━━━━━━━━━ 113s 451ms/step - loss: 197.5607
Epoch 3/10
250/250 ━━━━━━━━━━━━━━━━━━━━ 115s 460ms/step - loss: 220.5820
Epoch 4/10
250/250 ━━━━━━━━━━━━━━━━━━━━ 109s 434ms/step - loss: 394.0200
Epoch 5/10
250/250 ━━━━━━━━━━━━━━━━━━━━ 109s 436ms/step - loss: 388.5954
Epoch 6/10
250/250 ━━━━━━━━━━━━━━━━━━━━ 108s 431ms/step - loss: 323.4093
Epoch 7/10
250/250 ━━━━━━━━━━━━━━━━━━━━ 108s 432ms/step - loss: 278.2234
Epoch 8/10
250/250 ━━━━━━━━━━━━━━━━━━━━ 110s 439ms/step - loss: 393.4183
Epoch 9/10
250/250 ━━━━━━━━━━━━━━━━━━━━ 114s 456ms/step - loss: 523.3671
Epoch 10/10
250/250 ━━━━━━━━━━━━━━━━━━━━ 111s 445ms/step - loss: 223.5443
---
## Inference
We use our model to generate new valid molecules from different points of the latent space.
### Generate unique Molecules with the model
```python
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])