Molecular Graph Generation
26 papers with code • 3 benchmarks • 2 datasets
ZINC
MOSES
Most implemented papers
Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks
DrugGEN can be used to design completely novel and effective target-specific drug candidate molecules for any druggable protein, given target features and a dataset of experimental bioactivities.
Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation
graph-0/jodo
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To capture the correlation between molecular graphs and geometries in the diffusion process, we develop a Diffusion Graph Transformer to parameterize the data prediction model that recovers the original data from noisy data.
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.
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
cambridge-mlg/weighted-retraining
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NeurIPS 2020
We introduce an improved method for efficient black-box optimization, which performs the optimization in the low-dimensional, continuous latent manifold learned by a deep generative model.
MoFlow: An Invertible Flow Model for Generating Molecular Graphs
Generating molecular graphs with desired chemical properties driven by deep graph generative models provides a very promising way to accelerate drug discovery process.
Reinforced Molecular Optimization with Neighborhood-Controlled Grammars
A major challenge in the pharmaceutical industry is to design novel molecules with specific desired properties, especially when the property evaluation is costly.
GraphEBM: Molecular Graph Generation with Energy-Based Models
divelab/DIG
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ICLR Workshop EBM 2021
We note that most existing approaches for molecular graph generation fail to guarantee the intrinsic property of permutation invariance, resulting in unexpected bias in generative models.
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery
yutxie/mars
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ICLR 2021
Searching for novel molecules with desired chemical properties is crucial in drug discovery.
