# Molecular Graph Generation

18 papers with code • 3 benchmarks • 2 datasets

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# Junction Tree Variational Autoencoder for Molecular Graph Generation

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

10

# Optimization of Molecules via Deep Reinforcement Learning

19 Oct 2018

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).

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

29 Nov 2018

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

3

# GraphNVP: An Invertible Flow Model for Generating Molecular Graphs

28 May 2019

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

3

# 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.

2

# MolecularRNN: Generating realistic molecular graphs with optimized properties

31 May 2019

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

2

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

7 Jun 2021

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

2

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

30 May 2017

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.

1

# 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.

1

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

8 Apr 2020

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.

1