Molecular Graph Generation
26 papers with code • 3 benchmarks • 2 datasets
ZINC
MOSES
Latest papers
3M-Diffusion: Latent Multi-Modal Diffusion for Text-Guided Generation of Molecular Graphs
However, practical applications call for methods that generate diverse, and ideally novel, molecules with the desired properties.
A Simple and Scalable Representation for Graph Generation
yunhuijang/geel
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Recently, there has been a surge of interest in employing neural networks for graph generation, a fundamental statistical learning problem with critical applications like molecule design and community analysis.
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.
MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation
violet-sto/molhf
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However, limited attention is paid to hierarchical generative models, which can exploit the inherent hierarchical structure (with rich semantic information) of the molecular graphs and generate complex molecules of larger size that we shall demonstrate to be difficult for most existing models.
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.
Geometry-Complete Diffusion for 3D Molecule Generation and Optimization
bioinfomachinelearning/bio-diffusion
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However, such methods are unable to learn important geometric and physical properties of 3D molecules during molecular graph generation, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which negatively impacts their ability to effectively scale to datasets of large 3D molecules.
Graph Generation with Diffusion Mixture
harryjo97/drum
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Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures.
Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation
graph-0/cdgs
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To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation.
FastFlows: Flow-Based Models for Molecular Graph Generation
aspuru-guzik-group/selfies
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We propose a framework using normalizing-flow based models, SELF-Referencing Embedded Strings, and multi-objective optimization that efficiently generates small molecules.
HyFactor: Hydrogen-count labelled graph-based defactorization Autoencoder
Laboratoire-de-Chemoinformatique/hyfactor
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ChemRxiv 2021
Graph-based architectures are becoming increasingly popular as a tool for structure generation.
