Molecular Graph Generation

18 papers with code • 3 benchmarks • 2 datasets

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Most implemented papers

Junction Tree Variational Autoencoder for Molecular Graph Generation

wengong-jin/icml18-jtnn ICML 2018

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

Optimization of Molecules via Deep Reinforcement Learning

google-research/google-research 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).

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

molecularsets/moses 29 Nov 2018

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

GraphNVP: An Invertible Flow Model for Generating Molecular Graphs

pfnet-research/chainer-chemistry 28 May 2019

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

Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation

bowenliu16/rl_graph_generation NeurIPS 2018

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

shubhamguptaiitd/GraphRNN 31 May 2019

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

huawei-noah/noah-research 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.

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

gablg1/ORGAN 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.

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

DeepGraphLearning/GraphAF ICLR 2020

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

HyTruongSon/InvariantGraphNetworks-Pytorch 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.