Molecular Graph Generation
18 papers with code • 3 benchmarks • 2 datasets
ZINC
MOSES
Most implemented papers
Junction Tree Variational Autoencoder for Molecular Graph Generation
wengong-jin/icml18-jtnn
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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
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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
bowenliu16/rl_graph_generation
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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
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
gablg1/ORGAN
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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
HyTruongSon/InvariantGraphNetworks-Pytorch
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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.
