Molecular Graph Generation

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1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Evolutionary One-Step Generators: Fast and Diverse Sampling for Discrete Design
Oct 6, 2026Marcus Vukojevic, Erik Nielsen, Veronica Lachi +2Constrained Generative Modeling Graph Generation
University of Trento, Italy · UiT The Arctic University of Norway, Tromsø, Norway
Latent Flow Matching for Molecular Graph Generation
Oct 5, 2026Mathis Goupillon, Roman Bresson, Konstantinos Divriotis +1Flow Matching Molecular Graph Generation
Mohamed bin Zayed University of Artificial Intelligence
Efficient Graph Generation via Direct Prediction and Flow Matching
Oct 4, 2026Susie LuFlow Matching Graph Generation
MIT
Boltzmann-Expected Molecular Design with Decoupled Annealing Flows
Jul 21, 2026Selma Moqvist, Richard Beckmann, Ross Irwin +2Molecular Optimization Molecular Graph Generation
Department of Computer Science and Engineering Chalmers University of Technology & University of Gothenburg SE-41296 Gothenburg, Sweden · Molecular AI Discovery Sciences, R&D AstraZeneca SE-43183 Mölndal, Sweden
MotifRole-Diff: Risk-Optimal Role-Aware Corruption for Masked Molecular Graph Diffusion
Jul 20, 2026Tasfia Nuzhat Ornee, Elias Hossain, Ivan Garibay +1Masked Diffusion Models Molecular Graph Generation
University of Central Florida
Autoregressive latent diffusion for 3D molecule generation
Jul 10, 2026Federico Ottomano, Gaopeng Ren, Yingzhen Li +2Latent Diffusion Models Molecular Graph Generation
Department of Chemistry Imperial College London · Department of Computing Imperial College London
Proximal Policy Optimization for Amortized Discrete Sampling
Jun 14, 2026Anna Zykova-Myzina, Timofei Gritsaev, Daniil Tiapkin +1Molecular Graph Generation Generative Flow Networks
HSE University · Constructor University · CMAP, CNRS, École polytechnique, IPP
Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport
Jun 5, 2026Malte Franke, Stefan P. Schmid, Zarko Ivkovic +2Molecular Optimization Molecular Graph Generation
ETH Zürich · NCCR Catalysis
Uncertainty-Calibrated Diffusion for Reliable 3D Molecular Graph Generation
Jun 1, 2026Fang Wan, Jingxiang Qu, Yi LiuDiffusion Sampling Molecular Graph Generation
State University of New York at Stony Brook
Representation-Guided Discrete Molecular Graph Retrosynthesis
May 23, 2026Jiahai Huang, Anjie Qiao, Zhen Wang +2Diffusion Model Guidance Molecular Graph Generation
Sun Yat-sen University Guangzhou, China · University of Science and Technology of China Hefei, China
GraViti: Graph-Level Variational Autoencoders with Relaxed Permutation Invariance
May 15, 2026Roman Bresson, Konstantinos Divriotis, Johannes F. Lutzeyer +2Variational Autoencoders Molecular Graph Generation
Mohamed bin Zayed University of Artificial Intelligence · LIX, CNRS, École Polytechnique, IP Paris
Controllable Molecular Generative Foundation Models
May 14, 2026Yihan Zhu, Yuhan Liu, Weijiang Li +2Molecular Optimization Molecular Graph Generation
University of Notre Dame
Unlocking High-Fidelity Molecular Generation from Mass Spectra via Dual-Stream Line Graph Diffusion
May 7, 2026Xujun Che, Xiuxia Du, Depeng XuGraph Neural Networks Molecular Graph Generation
Department of Software and Information Systems,2026 University of North Carolina at Charlotte, Charlotte, NC 28223 · Center for Environmental Monitoring and Informatics Technologies for Public Health University of North Carolina at Charlotte, Charlotte, NC 28223 · Department of Bioinformatics and Genomics,May University of North Carolina at Charlotte, Charlotte, NC 28223
Interpolating Discrete Diffusion Models with Controllable Resampling
Apr 19, 2026Marcel Kollovieh, Sirine Ayadi, Stephan GünnemannDiffusion Model Sampling Molecular Graph Generation
School of Computation, Information and Technology, Technical University of Munich
Variational Bayesian Flow Network for Graph Generation
Jan 30, 2026Yida Xiong, Jiameng Chen, Xiuwen Gong +3Graph Generation Molecular Graph Generation
School of Computer Science, Wuhan University, Wuhan, China · Department of Computing, Macquarie University, Sydney, Australia · Griffith University, Gold Coast, Australia +1
Transport-Coupled Bayesian Flows for Molecular Graph Generation
Oct 11, 2025Yida Xiong, Jiameng Chen, Kun Li +4Molecular Graph Generation Molecular Generation
School of Computer Science, Wuhan University, Wuhan, China · Wuhan University Shenzhen Research Institute, Shenzhen, China

Oct 6, 2026, cs.LGJ/K move · Enter open · S save

Evolutionary One-Step Generators: Fast and Diverse Sampling for Discrete Design

Marcus Vukojevic, Erik Nielsen, Veronica Lachi, Andrea Passerini+1

University of Trento, Italy · UiT The Arctic University of Norway, Tromsø, Norway

Constrained Generative Modeling Graph Generation Molecular Graph Generation One-Step Generative Modeling Molecular Generation

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Several discrete design tasks, such as molecular discovery, require diverse collections of useful candidates at low computational cost. High validity alone does not guarantee a useful candidate library: repeatedly generating the same valid structures leaves few distinct alternatives. Training for both feasibility and diversity is challenging because many relevant criteria can only be evaluated after hard decoding. To address this challenge, we propose EGO (Evolutionary Generators with One-step inference), a framework for training compact generators directly on discrete outputs. The method combines distribution matching with structural constraints and optional diversity or history-dependent rewards, using antithetic low-rank evolution strategies without requiring criterion-specific differentiable surrogates. Once trained, the generator produces the entire graph in a single neural-network evaluation. On molecular generation benchmarks, our compact generator achieves over

50\times

the valid-and-unique yield per estimated dense operation compared to recent one-step flow-map baselines while retaining high chemical validity. In scaffold completion, EGO achieves an observed

44.3\times

speedup over MoLeR in generation to SMILES and produces approximately

10\times

as many filter-passing proposals within matched time budgets for generation and screening. Beyond chemistry, EGO produces

1.54\times

as many distinct held-out elite architectures as relaxed gradient training on NAS-Bench-101. The low generation cost may enable real-time candidate generation across discrete design tasks, supporting interactive exploration of constrained design spaces and rapid construction of candidate sets for downstream evaluation.

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