DeepMol – Deep Learning-Based Property Prediction and Generation of Molecules

A TensorFlow implementation of deep learning models for property prediction and generation of molecules, featurized by atom types and molecular geometry, trained on the QM9 data set. Written for my master's thesis at Freie Universität Berlin.

Requirements

• tensorflow
• sklearn
• numpy, scipy
• rdkit
• pybel, openbabel

Data Set

A shuffled and partitioned version of the data set that is ready-to-use for this project can be downloaded here.

Message Passing Neural Network

Property Prediction is performed with a message passing neural network (MPNN, see Neural Message Passing for Quantum Chemistry):

• A hidden state vector is assigned to each atom.
• Based on their interatomic distance, the atoms "pass messages" to each other via continuous convolutions.
• A Gated Recurrect Unit is used to update the hidden states based on the incoming messages.
• After multiple message passes, a read-out function (based on an LSTM and an attention mechanism) creates a vector embedding of the molecule which can be mapped to the respective property.

With implicit hydrogen and on a test set of 10k molecules, the model achieved a mean absolute error of 0.42kcal/mol on atomization energy.

Use train_mpnn.py to run trainings and evaluations.

Molecular Variational Autoencoder

A message passing-based encoder is extended by a fully-connected neural network decoder (see figure below).

When using it as an autoencoder, the model reconstructs heavy atoms with an accuracy of 99.9% and atom positions with a mean absolute error of 0.05Å.

When sampling molecules from the latent prior in VAE mode, the atoms have a mean absolute deviation of 0.4Å from their position after a DFT-based geometry optimization.

Loss

• atom types: cross entropy
• distances: mean squared error, weighted by the probability that the involved atoms exist in both molecules
• geometry: an additional term penalizes violations of the triangle inequality. It did not turn out to improve model performance though

Evaluation

• reconstruction accuracy of atom types
• reconstruction error of atom positions
• SMILES accuracy: for original and reconstruction, infer bonds, generate SMILES and compare
• generated mols: DFT-based geometry optimization is performed using orca (see respective notebook)

Use train_vae.py to run trainings and evaluations. To use the model as an autoencoder, set the parameter variational to false.