Molecular Graph Generation

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

28 shown of 28 papers with code (54 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Junction Tree Variational Autoencoder for Molecular Graph Generation
  12 Feb 2018 11 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  We evaluate our model on multiple tasks ranging from molecular generation to optimization.
• Optimization of Molecules via Deep Reinforcement Learning
  19 Oct 2018 7 repositories listed
  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…
• Geometry-Complete Diffusion for 3D Molecule Generation and Optimization
  8 Feb 2023 3 repositories listed Syntology ran 3 of 10 samples · 7 unverified
  Importantly, we demonstrate that the geometry-complete denoising process of GCDM learned for 3D molecule generation enables the model to generate a significant proportion of valid and energetically-stable large…
• FastFlows: Flow-Based Models for Molecular Graph Generation
  28 Jan 2022 3 repositories listed
  We propose a framework using normalizing-flow based models, SELF-Referencing Embedded Strings, and multi-objective optimization that efficiently generates small molecules.
• Molecule Generation by Principal Subgraph Mining and Assembling
  29 Jun 2021 3 repositories listed Syntology ran 2 of 6 samples · 4 unverified
  Molecule generation is central to a variety of applications.
• GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
  28 May 2019 3 repositories listed Syntology ran 1 of 5 samples · 4 unverified
  We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model.
• Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
  29 Nov 2018 3 repositories listed Syntology ran 0 of 7 samples · 7 unverified
  Generative models are becoming a tool of choice for exploring the molecular space.
• Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation
  21 May 2023 2 repositories listed Syntology ran 3 of 4 samples · 1 unverified · 3 pointer-only (licence)
  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…
• Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks
  15 Feb 2023 2 repositories listed
  These results indicate that DrugGEN's de novo molecules have a high potential for interacting with the AKT1 protein at the level of its native ligands.
• Graph Generation with Diffusion Mixture
  7 Feb 2023 2 repositories listed Syntology ran 2 of 4 samples · 2 unverified · 4 pointer-only (licence)
  Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures.
• High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
  7 Jun 2021 2 repositories listed
  We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces.
• MoFlow: An Invertible Flow Model for Generating Molecular Graphs
  17 Jun 2020 2 repositories listed Syntology ran 1 of 2 samples · 1 unverified
  Generating molecular graphs with desired chemical properties driven by deep graph generative models provides a very promising way to accelerate drug discovery process.
• MolecularRNN: Generating realistic molecular graphs with optimized properties
  31 May 2019 2 repositories listed
  Designing new molecules with a set of predefined properties is a core problem in modern drug discovery and development.
• Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
  7 Jun 2018 2 repositories listed Syntology ran 1 of 13 samples · 12 unverified
  Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research.
• Instruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion Model
  19 Aug 2024 1 repository listed
  Recent advancements in computational chemistry have increasingly focused on synthesizing molecules based on textual instructions.
• GraphSPNs: Sum-Product Networks Benefit From Canonical Orderings
  18 Aug 2024 1 repository listed
  Deep generative models have recently made a remarkable progress in capturing complex probability distributions over graphs.
• 3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation
  11 Mar 2024 1 repository listed
  We propose 3M-Diffusion, a novel multi-modal molecular graph generation method, to generate diverse, ideally novel molecular structures with desired properties.
• A Simple and Scalable Representation for Graph Generation
  4 Dec 2023 1 repository listed Syntology ran 3 of 6 samples · 3 unverified · 6 pointer-only (licence)
  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.
• MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation
  15 May 2023 1 repository listed Syntology ran 14 of 23 samples · 9 unverified
  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…
• Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation
  1 Jan 2023 1 repository listed
  To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation.
• HyFactor: Hydrogen-count labelled graph-based defactorization Autoencoder
  6 Dec 2021 1 repository listed
  Graph-based architectures are becoming increasingly popular as a tool for structure generation.
• MARS: Markov Molecular Sampling for Multi-objective Drug Discovery
  18 Mar 2021 1 repository listed
  Searching for novel molecules with desired chemical properties is crucial in drug discovery.
• GraphEBM: Molecular Graph Generation with Energy-Based Models
  31 Jan 2021 1 repository listed
  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.
• Reinforced Molecular Optimization with Neighborhood-Controlled Grammars
  14 Nov 2020 1 repository listed
  A major challenge in the pharmaceutical industry is to design novel molecules with specific desired properties, especially when the property evaluation is costly.
• Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
  16 Jun 2020 1 repository listed Syntology ran 2 of 6 samples · 4 unverified · 4 pointer-only (licence)
  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.
• The general theory of permutation equivarant neural networks and higher order graph variational encoders
  8 Apr 2020 1 repository listed Syntology ran 0 of 17 samples · 17 unverified
  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.
• GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
  26 Jan 2020 1 repository listed
  Molecular graph generation is a fundamental problem for drug discovery and has been attracting growing attention.
• Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
  30 May 2017 1 repository listed
  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.

Syntology lines on 13 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.