# Molecular Graph Generation

18 papers with code • 3 benchmarks • 2 datasets

## Most implemented papers

# Junction Tree Variational Autoencoder for Molecular Graph Generation

We evaluate our model on multiple tasks ranging from molecular generation to optimization.

# Optimization of Molecules via Deep Reinforcement Learning

We present a framework, which we call Molecule Deep $Q$-Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (double $Q$-learning and randomized value functions).

# Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

Generative models are becoming a tool of choice for exploring the molecular space.

# GraphNVP: An Invertible Flow Model for Generating Molecular Graphs

We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model.

# Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation

Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research.

# MolecularRNN: Generating realistic molecular graphs with optimized properties

Designing new molecules with a set of predefined properties is a core problem in modern drug discovery and development.

# High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning

We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces.

# Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models

In unsupervised data generation tasks, besides the generation of a sample based on previous observations, one would often like to give hints to the model in order to bias the generation towards desirable metrics.

# GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation

Molecular graph generation is a fundamental problem for drug discovery and has been attracting growing attention.

# The general theory of permutation equivarant neural networks and higher order graph variational encoders

Previous work on symmetric group equivariant neural networks generally only considered the case where the group acts by permuting the elements of a single vector.