Molecular Generation

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Molecular generation studies how to create new chemical structures or 3D conformations that meet goals such as binding a protein, having useful properties, or matching spectral evidence. Common approaches include autoregressive chemical language models, graph and 3D diffusion or flow-matching models, and fragment-based generators; reinforcement learning, search, and active learning can steer their outputs toward desired properties. Researchers compare methods on benchmarks such as QM9, ZINC, MOSES, CrossDocked, and MassSpecGym, evaluating validity, novelty, diversity, property performance, and structural fit. A central challenge is balancing goal-directed optimization with chemical plausibility, diversity, and synthesizability, while ensuring that gains on computational proxies hold up in independent or experimental tests.

Latest papers

235 papers32 papers in the last four weeks, up 60% on the four weeks before. 0.2% of all new papers.

EVA-Flow: Learning Shared Chemistry for Unified Environment-Aware Molecular Conformation Generation
Oct 26, 2026Bing Yan, Anuroop Sriram, Zachary Ward Ulissi +33D Molecular Generation Molecular Generation
FAIR at Meta · New York University
Interpretable and Steerable Molecular Graph Generation using Concept Bottlenecks
Oct 25, 2026Sarabeshwar Balaji, Ritesh Kumar, Lav R. VarshneyMolecular Graph Generation Molecular Generation
Indian Institute of Science Education and Research Bhopal, India · CSIR-Central Scientific Instruments Organisation, Chandigarh, India · Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India +1
REFLEX: RNA Ensemble Generation via Flexibility-Calibrated Stochastic Bridge
Oct 19, 2026Wanli Ma, Tianmeng Hu, Ke LiMolecular Generation Bioinformatics
University of Exeter · Earlham Institute
TopoCompile: Molecular Generation with Exact Global Count Constraints
Oct 17, 2026Xiang Wu, Rong-Hua Li, Quanqing Xu +2Molecular Generation Molecular Graph Generation
CocoRNA: Collective RNA Design with Cooperative Multi-agent Reinforcement Learning
Oct 16, 2026Tianmeng Hu, Biao Luo, Ke LiInverse Design Molecular Generation
Living Systems Institute, University of Exeter, United Kingdom · Department of Computer Science, University of Exeter, United Kingdom · School of Automation, Central South University, China
GOLD: Geometric Optimized Latent Diffusion for Structure-Aware RNA Inverse Folding
Oct 16, 2026Qi Si, Xuyang Liu, Penglei Wang +6Molecular Generation Inverse Design
Shanghai Academy of Artificial Intelligence for Science · School of Biomedical Engineering, Shanghai Jiao Tong University · Community Science Department, Kuaishou +2
MolLean: A Process-Verifiable Language for Chemical Reasoning
Oct 14, 2026Jiatong Li, Yatao BianChemical Reasoning in LLMs Molecular Generation
National University of Singapore
MolHop: Scaffold-Hopping Molecular Design with Agentic Reinforcement Learning
Oct 14, 2026Yanyi Su, Chenyan Wang, Yulei Gong +3Molecule Design Molecular Generation
State Key Laboratory of Physical Chemistry of Solid Surface, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China · Shanghai Artificial Intelligence Laboratory, Shanghai, China · Institute of Artificial Intelligence, Xiamen University, Xiamen, China +1
MIRRA: Fast, High-Fidelity Molecular Generation with Chemistry-Informed Discrete Flow Matching
Oct 12, 2026Ge Sun, Juan J. de PabloMolecular Generation Molecular Graph Generation
Courant Institute of Mathematical Sciences, New York University, New York, NY 10012 · Tandon School of Engineering, New York University, Brooklyn, NY 11201 · Department of Physics, New York University, New York, NY 10003
AutoNovo: Agentic Program Search Discovers Transferable Nucleic Acid Design Algorithms
Oct 12, 2026Joel Shor, Subhashini VenugopalanMolecule Design Molecular Generation
AI Bio Design, Allen Institute & Move37 Labs · Google Research
DC-Flow: Target-Conditioned Cyclic Peptide Design via Delayed-Coupling Flow Matching
Oct 11, 2026Yitian Wang, Minjie Cheng, Yaning Cui +3Protein Structure Generation Molecular Generation
Gaoling School of Artificial Intelligence, Renmin University of China · DP Technology · Cyclon Therapeutics +2
FragFlex: Fragment-Based Flexible Molecular Generation for Exploring Vast Chemical Space
Oct 11, 2026Sihyun Park, David Ryan KoesMolecular Graph Generation Molecular Generation
Carnegie Mellon University · University of Pittsburgh
Blending experimental electron density information to protein-ligand interaction
Oct 10, 2026Seonghyun Park, Shuan Chen, Jun Jin Choong +6Molecular Representation Learning Representation Learning
KAIST · SB Intuitions
Joint Discrete and SE(3) Diffusion for Protein Interface Design
Oct 10, 2026Zhenqiao Song, Jianwen Xie, Lei LiProtein Binder Design Molecular Generation
Language Technologies Institute, Carnegie Mellon University · Lambda Inc.
MAST: Motif-Augmented Diffusion with Search Tree for Spectroscopic Molecular Structure Elucidation
Oct 8, 2026Chenghao Jia, Mengdi Liu, Hong Chang +2Molecular Generation Diffusion Sampling
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, China · University of Chinese Academy of Sciences, China
Zatom-2: Multitask Pretraining on Atomistic Data for Generative Modeling across Domains
Oct 8, 2026Miruna Cretu, Alex Abrudan, Antonia Panescu +11Generative Modeling Molecular Generation
University of Cambridge · Yale University · MIT +3
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
Navigating Route Latent Space for Synthesizable Molecular Design
Oct 6, 2026Tao Li, Tuan Vinh, Monika Raj +3Molecule Design Molecular Optimization
Department of Computer Science, Emory University · MRC Brain Network Dynamics Unit, University of Oxford · Department of Chemistry, Emory University +2
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
From Benchmark to Bench: Can Agents Survive Real-World Drug Discovery?
Oct 5, 2026Pierre Llompart, Levent Guner, Helen Lai +2AI Agents for Scientific Discovery LLM Agent Orchestration
AMD Silo AI, Stockholm, Sweden · Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK · Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden +1
Bridging the Evidence-to-Execution Gap:A Reflective Agent for Multi-Objective Peptide Design
Oct 5, 2026Haosen Zhang, Yang YangAI Agents for Scientific Discovery Large Language Model-Guided Optimization
School of Agriculture and Biology, Shanghai Jiao Tong University · School of Computer Science, Shanghai Jiao Tong University
DiMOS: Doob-Guided Inference-Time Multi-Objective Search for Scientific Design
Oct 5, 2026Ziqing Wang, Qijie Zhu, Weimin Wu +4Masked Diffusion Models Inference-Time Search
Northwestern University
Learning Latent Protein Languages for Autoregressive Generation
Oct 2, 2026Mahdi Pourmirzaei, Farzaneh Esmaili, Amir Ziashahabi +2Protein Structure Generation Protein Language Models
University of Missouri · University of Southern California · Independent Researcher
Higher-Order Molecular Grammars for Generative and Foundation Models in Chemistry
Oct 1, 2026Yiming Huang, Yujie Zeng, Vijay Prakash Dwivedi +4Graph Representation Learning Molecular Generation
Department of Computing, Imperial College London, London, UK · School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK · Department of Computer Science, Stanford University, Stanford, CA, USA +1
CODesign: Consistency from Data to Trajectory in All-Atom Protein Binder Co-Design
Oct 1, 2026Yuanle Mo, Bo Qiang, Haitao Lin +4Molecular Generation Protein Design
Valhalla Technology · The Chinese University of Hong Kong · University of Washington +2
Grand Canonical Generators
Sep 30, 2026Andreas Burger, Malte Franke, Luka Mucko +2Generative Modeling Molecular Generation
University of Toronto NVIDIA Vector Institute Toronto, Canada · ETH Zurich NCCR Catalysis Zurich, Switzerland · University of Toronto NVIDIA Vector Institute Acceleration Consortium CIFAR Toronto, Canada
Analysis of Quantized and Efficiently Adapted Protein Language Models
Sep 30, 2026Ilan Yaniv Zeisler, Sebastian Clancy, Pouriya Bayat +8Protein Language Models Quantization-Aware LoRA
Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada. · Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada. · Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. +2
RetroGEF: Dynamic Graph Edit Flow for Single-Step Retrosynthesis
Sep 29, 2026Xiaozhuang Song, Xuemin Chen, Xinjian Zhao +2One-Step Generative Modeling Molecular Generation
The Chinese University of Hong Kong, Shenzhen · Shanghai AI Laboratory
RNA Design via Conditioned Flow Matching and Finite-Policy Reinforcement Learning
Sep 29, 2026Zefeng Lin, Xianyong Fang, Tianfan Fu +1Inverse Design Molecular Generation
School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China · School of Computer Science and Technology, Anhui University, Hefei, Anhui, China · State Key Laboratory for Novel Software Technology at Nanjing University, School of Computer Science, Nanjing University, Nanjing, Jiangsu, China
AbGaze: Attentive Geometric Representation Learning for End-to-End Antibody Design
Sep 28, 2026Jiashuo Wang, Siqi Fan, Yizhen Luo +1Geometric Representation Learning Molecular Generation
Institute for AI Industry Research (AIR), Tsinghua University · Department of Computer Science and Technology, Tsinghua University · PharMolix Inc.

Oct 26, 2026J/K move · Enter open · S save

EVA-Flow: Learning Shared Chemistry for Unified Environment-Aware Molecular Conformation Generation

Bing Yan, Anuroop Sriram, Zachary Ward Ulissi, Kyunghyun Cho+2

FAIR at Meta · New York University

3D Molecular Generation Molecular Generation Conformer Conditional Generative Modeling Flow-Based Generative Modeling

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Molecular 3D structures underpin simulations of thermodynamics, kinetics, crystallization, and nucleation, making their accurate prediction central to drug discovery and materials design. However, molecular geometry can change dramatically across environments. Existing models either ignore environmental effects or train separate models for each environment. We hypothesize that transfer learning across contexts is possible for conformer generation and that a general model will perform better when training data are limited, as in ligand binding and crystallization. We introduce EVA-Flow, a unified framework for environment-aware molecular conformation generation that learns shared chemical structure and adapts it through environment-conditioned generative modeling. Across vacuum, protein-ligand docking, solvation, and crystal packing, pretraining and cross-environment finetuning provide their largest gains in the three data-scarce settings. Evaluation against multiple reference conformations shows that EVA-Flow covers a broad range of solvation conformations. Analyses of molecules shared across environments support its ability to generate different conformations for the same molecule in different environments.