3D Molecular Generation

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4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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RIDE: Reference-Anchored Inference-Time Diffusion Editing for Scaffold Hopping
Sep 28, 2026Ruoxi Gao, Frazier N. Baker, Trieu Nguyen +1Diffusion Guidance Structure-Based Drug Design
Department of Computer Science and Engineering, The Ohio State University · Department of Biomedical Informatics, The Ohio State University · Translational Data Analytics Institute, The Ohio State University +1
Ensemble-Conditioned Molecular Design
Sep 14, 2026Ross Irwin, Alessandro Tibo, Jon Paul Janet +1Molecular Optimization Structure-Based Drug Design
Molecular AI Discovery Sciences, R&D AstraZeneca Gothenburg, Sweden · Department of Computer Science and Engineering Chalmers University of Technology and University of Gothenburg Gothenburg, Sweden
Fixed-Dimensional Latent Flow for Generating Variable-Size 3D Molecules
Sep 8, 2026Weichi Yao, Cameron Gruich, Bryan R. Goldsmith +13D Molecular Generation Molecular Generation
Michigan Institute for Data & AI in Society University of Michigan, Ann Arbor, USA · Department of Chemical Engineering University of Michigan, Ann Arbor, USA · Department of Statistics University of Michigan, Ann Arbor, USA
PocketVE: Stable and Property-Guided Structure-Based Drug Design with Variance-Exploding Diffusion
Sep 8, 2026Peining Zhang, Jinbo BiDiffusion Model Guidance Structure-Based Drug Design
University of Connecticut Storrs, Connecticut 06269, USA
Language-Informed Flow Matching for Trend-Guided Structure-Based 3D Molecular Generation
Aug 31, 2026Tianyu Gao, Zhikai Su, Jiashu Li +5Flow Matching Structure-Based Drug Design
1City University of Hong Kong · 2The University of Hong Kong · 3Stanford University
SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups
Jul 29, 2026Yikun Bai, Binghang Lu, Yikai Liu +7Flow Matching Lie Group Methods
Department of Mathematics, Purdue University · School of Electrical and Computer Engineering, Purdue University · Department of Biochemistry, Purdue University +5
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
Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints
Jul 20, 2026Thomas MacDougall, Maksim Kuznetsov, Roman Schutski +5Constrained Generative Modeling LLM Evaluation
Insilico Medicine Canada Inc. · Insilico Medicine Hong Kong Ltd. · Insilico Medicine AI Limited
SinAE: A Single-Architecture Flow-Matching Autoencoder for Cross-Domain Atomic Systems
Jul 14, 2026Yuxuan Ren, Fan Yang, Jianhua Yao +1Autoencoders 3D Molecular Generation
Department of Computer Science National University of Singapore · Tencent AI for Life Science Lab
Generating Developable 3D Molecules via Pocket-Conditioned Diffusion and Property-Aware Optimization
Jul 14, 2026Ruoxi Gao, Jiangweizhi Peng, Ziqi Chen +10Molecular Optimization Structure-Based Drug Design
Computer Science and Engineering, The Ohio State University, Columbus, OH 43210. · Industrial and System Engineering, University of Minnesota, Minneapolis, MN 55455. · Google Cloud, Google LLC, Mountain View, CA 94043. +6
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
SynLaD: Latent Diffusion for Generating Synthesizable Molecules Conditioned on 3D Pharmacophore Profiles
Jul 1, 2026Miruna Cretu, John Bradshaw, Patricia Suriana +6Latent Diffusion Models 3D Molecular Generation
University of Cambridge, Cambridge, UK · Prescient Design (AI for Drug Discovery), Genentech, South San Francisco, USA · Work done during an internship at Prescient Design
Sesame: Structure-Aware Molecular Generation via Spatial Density-Map Conditioning
Jun 22, 2026Konstantin Yatsenko, Arvind ThiagarajanMolecular Optimization Structure-Based Drug Design
Tessel Biosciences, Inc., 750 Main Street, Cambridge, MA 02139
Multigrid Training for Molecular Generation using Graph Neural Networks
Jun 21, 2026Zixuan Ling, Paula Mercurio, Di LiuStructure-Based Drug Design 3D Molecular Generation
Department of Mathematics, Michigan State University, East Lansing, MI. · Department of Mathematics, Hamline University, Saint Paul, MN.
Emyx: Fast and efficient all-atom protein generation
Jun 12, 2026Nicholas J. Williams, Ward Haddadin, Matteo P. Ferla +6Conditional Flow Matching 3D Molecular Generation
Xyme, Oxford, UK
Smoothing Dark Areas in Molecular Latent Diffusion
Jun 11, 2026Xi Wang, Jiahan Li, Yuxuan Xia +3Variational Autoencoders Latent Diffusion Models
New York University · Stanford University
Uncertainty Estimation for Molecular Diffusion Models
Jun 11, 2026Paul Seij, Christian A. Naesseth, Stephan Mandt +13D Molecular Generation Molecular Generation
UvA-Bosch Delta Lab, University of Amsterdam · University of California, Irvine
Few-step Cofolding with All-Atom Flow Maps
Jun 7, 2026Gianluca Scarpellini, Ron Shprints, Peter Holderrieth +7Protein Structure Prediction Diffusion Distillation
1Genesis Molecular AI · Massachusetts Institute of Technology · Carnegie Mellon University +2
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
ProHiFlo: Hierarchical Flow Matching with Functional Guidance for De Novo Protein Generation
Jun 3, 2026Chuanzhen Wang, Meade Cleti, Pete JanoFlow Matching 3D Molecular Generation
3Tongji University · 1Arizona State University · University of Wisconsin-Madison
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
Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling
May 31, 2026Guang Lin, Shikui Tu, Lei XuMolecular Optimization Diffusion Model Fine-Tuning
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, Guangdong, China
Manifold Diffusion for Structure Generation of Transition Metal Complexes
May 30, 2026Luca Schaufelberger, Kjell Jorner3D Molecular Generation Molecular Generation
Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich. · NCCR Catalysis, Switzerland.
AtomComposer: Discovering Chemical Space from First Principles with Reinforcement Learning
May 27, 2026Bjarke Hastrup, Francois Cornet, Tejs Vegge +1AI Agents for Scientific Discovery 3D Molecular Generation
Dept. of Energy Conversion and Storage, Technical University of Denmark, Denmark · Dept. of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark · Pioneer Center for Accelerating P2X Materials Discovery (CAPeX), Kgs. Lyngby, Denmark
Generative Pseudo-Force Fields for Molecular Generation
May 18, 2026Stefaan Simon Pierre Hessmann, Khaled Kahouli, Stefan Gugler +4Machine Learning Interatomic Potentials Diffusion Model Sampling
Machine Learning Group, Technische Universität Berlin, Berlin, Germany · BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany · Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany +6
Generating Physically Consistent Molecules with Energy-Based Models
May 18, 2026Christoph Griesbacher, Lea Bogensperger, Andreas Habring +1Energy-Based Models 3D Molecular Generation
Graz University of Technology · University of Zurich
Fine-tuning Pocket-Aware Diffusion Models via Denoising Policy Optimization
May 17, 2026Yuan Xue, Daniel Kudenko, Megha KhoslaMolecular Optimization Diffusion Model Fine-Tuning
L3S Research Center, Hanover, Germany · Delft University of Technology, Delft, Netherlands
From Holo Pockets to Electron Density: GPT-style Drug Design with Density
May 9, 2026Jiahao Chen, Letian Gao, Yanhao Zhu +4Structure-Based Drug Design 3D Molecular Generation
Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China · Beijing Key Laboratory of Research on Large Models and Intelligent Governance, Beijing, China · Engineering Research Center of Next Generation Intelligent Search and Recommendation, MOE, Beijing, China +3
Toward Better Geometric Representations for Molecule Generative Models
May 8, 2026Shaoheng Yan, Zian Li, Cai Zhou +3Geometric Representation Learning 3D Molecular Generation
Institute for Artificial Intelligence, PKU · ByteDance AI Drug Discovery · School of Intelligence Science and Technology, PKU +3
FlashMol: High-Quality Molecule Generation in as Few as Four Steps
May 7, 2026Xinyuan Wei, Zian Li, Shaoheng Yan +2Diffusion Distillation Few-Step Diffusion Sampling
Institute for Artificial Intelligence, Peking University · Yuanpei College, Peking University · School of Intelligence Science and Technology, Peking University +2
Proteo-R1: Reasoning Foundation Models for De Novo Protein Design
May 1, 2026Fang Wu, Weihao Xuan, Heli Qi +26Generative Modeling 3D Molecular Generation
Stanford University
Quotient-Space Diffusion Models
Apr 23, 2026Yixian Xu, Yusong Wang, Shengjie Luo +4Generative Modeling 3D Molecular Generation
State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, China · State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University · Zhongguancun Academy, Beijing, China +2
Structure-guided molecular design with contrastive 3D protein-ligand learning
Apr 21, 2026Carles Navarro, Philipp Tholke, Gianni de FabritiisContrastive Learning Virtual Screening
Acellera Labs, C/ Doctor Trueta 183, 08005 Barcelona, Spain · Computational Science Laboratory, Universitat Pompeu Fabra, PRBB, C/ Doctor Aiguader 88, 08003 Barcelona, Spain · Instituci´o Catalana de Recerca i Estudis Avan¸cats (ICREA), Passeig Llu´ıs Companys 23, 08010 Barcelona, Spain
Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation
Oct 6, 2025Johanna Sommer, John Rachwan, Nils Fleischmann +2Flow Matching Efficient Inference
Pruna AI · Technical University of Munich, Germany
MolMiner: Toward Controllable, 3D-Aware, Fragment-Based Molecular Design
Nov 10, 2024Raul Ortega-Ochoa, Tejs Vegge, Jes Frellsen3D Molecular Generation Autoregressive Generation
Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark · CAPeX Pioneer Center for Accelerating P2X Materials Discovery · Department of Applied Mathematics and Computer Science, Technical University of Denmark

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RIDE: Reference-Anchored Inference-Time Diffusion Editing for Scaffold Hopping

Ruoxi Gao, Frazier N. Baker, Trieu Nguyen, Xia Ning

Department of Computer Science and Engineering, The Ohio State University · Department of Biomedical Informatics, The Ohio State University · Translational Data Analytics Institute, The Ohio State University+1

Diffusion Guidance Structure-Based Drug Design 3D Molecular Generation

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Scaffold hopping is a critical task in drug discovery, which seeks to discover new, structurally distinct molecules that share key functional groups and similar 3D shape with a reference binding ligand. Existing diffusion-based scaffold hopping methods formulate the problem as conditional generation of scaffolds given the functional groups. However, they lack a principled mechanism to jointly enforce 2D structural novelty and preserve the 3D shape of the reference ligand. Here, we introduce RIDE, a Reference-anchored Inference-time Diffusion Editing framework for scaffold hopping. RIDE recovers the reference diffusion noise trajectory conditioned on the binding pocket and functional groups, selects an optimal trajectory segment for editing via noise perturbation, and conducts a value-guided scaffold sampling to generate new scaffolds. Extensive experimental results demonstrate that, compared to baselines, RIDE consistently generates scaffolds with lower 2D similarity and higher 3D similarity to the reference, with an average improvements of 11.7% and 7.3%, respectively. Further analysis reveals that RIDE can accommodate various reward functions, and can preserve 3D similarity even when this is not explicitly included in the reward. Two case studies illustrate RIDE's ability to generate distinct scaffolds with different structures and properties, and its ability to introduce substantial 2D variation while maintaining very high 3D similarity. RIDE is publicly available at https://anonymous.4open.science/r/RIDE-C8A0.

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