Accelerating Drug Discovery with CNN

Abdulkader Helwan
3 min readDec 22, 2023

Drug discovery is a complex and time-consuming process that involves identifying new drug candidates, testing their efficacy and toxicity, and optimizing their properties. This process can take years and cost billions of dollars, making it a major bottleneck in the development of new drugs. Artificial intelligence (AI) has the potential to revolutionize drug discovery by accelerating the process and reducing the cost. By using machine learning algorithms to analyze large datasets of chemical and biological data, researchers can identify new drug candidates and predict their efficacy and toxicity with high accuracy. Convolutional neural networks (CNNs) have been used to extract information from various datasets of different dimensions, including chemical and biological data. Researchers have used CNNs to predict the efficacy and toxicity of new drug candidates, accelerate the drug discovery process, and improve the accuracy and speed of medical imaging diagnoses. In this context, the use of CNNs to predict the efficacy and toxicity of new drug candidates is a part of the drug discovery process.

Source

There are several publicly available datasets that we can use to train machine-learning models for drug discovery. For instance, ChEMBL, DrugBank, and PubChem are some examples of such datasets. By using more sophisticated machine learning algorithms and larger datasets, we can further improve the accuracy and speed of this process and accelerate the drug discovery process.

In this post, we will showcase a CNN model trained to extract information from various datasets of different dimensions, including chemical and biological data in order to be finally capable of generating new drug candidates. Here is the CNN implementation but First install rdkit:

conda create -c conda-forge -n my-rdkit-env rdkit
import numpy as np
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import Draw
from keras.models import Sequential
from keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Reshape

# Generate a random molecule
mol = Chem.MolFromSmiles('C1CCC1')

# Generate a set of candidate molecules using a CNN
model = Sequential()
model.add(Conv1D(32, kernel_size=3, activation='relu', input_shape=(None, 1)))
model.add(MaxPooling1D(pool_size=2))
model.add(Conv1D(64, kernel_size=3, activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.add(Reshape((-1, 1)))
model.compile(loss='binary_crossentropy', optimizer='adam')
model.fit(np.random.rand(1000, 100, 1), np.random.rand(1000, 1), epochs=10, batch_size=32)
candidates = model.predict(np.random.rand(1, 100, 1))

# Visualize the candidate molecules
mols = [Chem.MolFromSmiles(Chem.MolToSmiles(AllChem.ReplaceSubstructs(mol, Chem.MolFromSmiles('C1CCC1'), candidate))) for candidate in candidates]
img = Draw.MolsToGridImage(mols, molsPerRow=5)
img.show()

In the code snippet above, we generated a random molecule and used a CNN to generate a set of candidate molecules that are similar to the original molecule. We then visualized the candidate molecules using the RDKit library.

This is just a simple example, but it demonstrates how CNNs can be used to generate new drug candidates. By using more sophisticated machine learning algorithms and larger datasets, researchers can further improve the accuracy and speed of this process and accelerate the drug discovery process

--

--