Small Molecule

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Motivation:

The development of new drugs (molecules) can be extremely time-consuming and costly. The use of deep learning models can alleviate the search for good candidate drugs, by predicting properties of known molecules (e.g., solubility, toxicity, affinity to target protein, etc.).

As the number of possible molecules is astronomical, the space in which we search for/explore molecules is just a fraction of the entire space. Therefore, it's arguably desirable to implement generative models that can learn to generate novel molecules (which would otherwise have never been explored).

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.

Generative Modelling

Generative modelling has the potential of uncovering novel therapeutics that modulate targets and thereby affect the downwstream metabolism.

Solubility Prediction

Aqueous solubility is a key factor in drug discovery, since if a molecule is not soluble. it will typically be poorly bioavailable, making it difficult to perform in-vivo studies with it, and hence deliver to patients.

Dataset

Generative Modelling

The dataset used in this tutorial is a quantum mechanics dataset (QM9), obtained from MoleculeNet. Although many feature and label columns come with the dataset, we'll only focus on the SMILES column.

QM9 dataset is a good first dataset to work with for generating graphs, as the maximum number of heavy (non-hydrogen) atoms found in a molecule is only nine.

1. QM9: https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/qm9.csv

2. Zink: https://raw.githubusercontent.com/aspuru-guzik-group/chemical_vae/master/models/zinc_properties/250k_rndm_zinc_drugs_clean_3.csv

Solubility Prediction

1. ESOL
2. AqSolDB
3. [DSL-100] (https://risweb.st-andrews.ac.uk/portal/en/datasets/dls100-solubility-dataset(3a3a5abc-8458-4924-8e6c-b804347605e8).html)

Feature Engineering

Generative Modelling

Representing a molecular graph. Molecules can naturally be expressed as undirected graphs G = (V, E), where V is a set of vertices (atoms), and a set of edges (bonds). As for this implementation, each graph (molecule) will be represented as an adjacency tensor A, which encodes existence/non-existence of atom-pairs with their one-hot encoded bond types stretching an extra dimension, and a feature tensor H, which for each atom, one-hot encodes its atom type. Notice, as hydrogen atoms can be inferred by RDKit, hydrogen atoms are excluded from A and H for easier modeling.

Solubility Prediction

We use easy to calculate RDKit descriptors, as described by Yalkowsky et al. . These descriptors are:

1. Octanol-Water partition coefficient
2. Molecular Weight
3. Num. of Rotatable Bonds
4. Aromatic Proportion = Num. Aromatic Atoms / Num Heavy Atoms

How to Use

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Command line

• Create your virtual environment
• Install libraries in the requirements.txt
• Getting models unzip models.zip

1. Train Models:
   • WGAN-GP: python main.py --wgan --name name of database
   • gVAE: python main.py --gvae --name name of database
2. Sample latent space:
   • WGAN-GP: python main.py --sample_wgan --name name of model
   • gVAE: python main.py --sample_gvae --name name of model
3. Visualize GVAE latent space
   • python main.py --latent --name name of model
4. Predict solubility
   • python main.py --solubility --name name of model --smiles smiles string

Building Docker Image

• docker build . -t docker-name-app --no-cache
• docker run -it -p 8501:8501 docker-app-name

Use built docker image

• docker pull adjon081/small-molecules:latest
• docker run -it -p 8501:8501 small-molecules-streamlit

Launch Streamlit App locally

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streamlit run app.py

Implemented Models

1. WGAN-GP with R-GCN for the generation of small molecules graphs
2. Relational Graph Convolutional Neural Network Variational AutoEncoder
3. Mol-CycleGAN (Currently implementing)
4. Random Forest solubility predictor

Source:

1. MolGAN: An implicit generative model for small molecular graphs

2. GraphVAE: Towards Generation of Small Graphs Using Variational AutoEncoders

3. Mol-CycleGAN: A generative model for molecular optimization

4. Junction Tree Variational AutoEncoders for Molecular Graph Generation

5. ESOL: Estimating Aqueous Solubility Directly from Molecular Structure

6. AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds

7. Is Experimental Data Quality the Limiting Factor in Predicting the Aqueous Solubility of Driglike Molecules

8. FastFlows: Flow-Based Models for Molecular Graph Generation

Objectives

1. Implement Generative models (VAE and GAN) for small molecule design
2. Add support for the latest diffusion models
3. Sample generative models latent space
4. Provide an API to use models
5. Add a solubility and toxicity prediction models

Conclusion:

What we've learned, and prospects. In this tutorial, a generative model for molecular graphs was succesfully implemented, which allowed us to generate novel molecules.

In the future, it would be interesting to implement generative models that can modify existing molecules (for instance, to optimize solubility or protein-binding of an existing molecule). For that however, a reconstruction loss would likely be needed, which is tricky to implement as there's no easy and obvious way to compute similarity between two molecular graphs.