Molecular Generation

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16 papers in the last four weeks, up 220% on the four weeks before. 0.2% of all new papers.

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MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules
Jul 1, 2026Tong Xu, Xinzhe Cao, Zhihui Zhu +2Molecular Generation
Zhejiang University · ZJU-Hangzhou Global Scientific and Technological Innovation Center · University of Oxford
Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark
Jun 29, 2026Matthias Blaschke, Daniel Kienzle, Zsuzsanna Koczor-Benda +3Molecular Optimization Benchmark Design
University of Augsburg · CAAPS† · Germany +2
Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps
Jun 27, 2026RuiKang OuYang, Hanlin Yu, Xinyue Ai +7Flow Map Learning Generative Modeling
University of Cambridge · University of Helsinki · Peking University +3
Pepti-drift: Scalable Safe-Active Peptide Generation Without Inference-Time Guidance
Jun 26, 2026Takashi Fujiwara, Hikaru Shindo, Kaushalya Madhawa +6Molecular Generation Protein Design
Department of AI Systems Medicine Institute of Integrated Research Institute of Science Tokyo · SB Intuitions Tokyo, JAPAN
Multi-Objective Molecular Generation with Frequency-Controlled Evolutionary Dynamics
Jun 25, 2026Elia Colleoni, Paolo Guida, Didier Barradas-Bautista +1Multi-Objective Evolutionary Optimization Molecular Generation
Clean Energy Research Center (CERP) Physical Science and Engineering (PSE) Division King Abdullah University of Science and Technology Thuwal, Saudi Arabia 23955 · Kaust Visualization Lab (KVL) Core Lab Division King Abdullah University of Science and Technology Thuwal, Saudi Arabia 23955
Autoregressive Boltzmann Generators
Jun 25, 2026Danyal Rehman, Charlie B. Tan, Yoshua Bengio +2Autoregressive Generation Molecular Generation
Mila – Québec AI Institute · Broad Institute of MIT & Harvard · Aithyra +4
Uncertainty-aware reinforcement learning for chemical language models
Jun 23, 2026Borja Medina, Jon Paul JanetMolecular Generation RL for Generative Models
Molecular AI, Discovery Sciences, BioPharmaceuticals R&D AstraZeneca AB Gothenburg, Sweden
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
JEDEL: Zero-Shot DNA-Encoded Library Design for Early-Stage Drug Discovery
Jun 21, 2026Zygimantas Jocys, Zhanxing Zhu, Henriette M. G. Willems +1Molecular Generation Drug Discovery
School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, United Kingdom. · The ALBORADA Drug Discovery Institute, University of Cambridge,2026 Hills Road, Cambridge, CB2 0AH, United Kingdom.
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.
Calibrating Generative Models to Feature Distributions with MMD Finetuning
Jun 17, 2026Nathaniel L. Diamant, Brian L. TrippeGenerative Modeling Model Calibration
Stanford University
Be Your Own Teacher: Steering Protein Language Models via Unsupervised Reward Optimization
Jun 17, 2026Lanqing Li, Shentong Mo, Yang Yu +1RL for Language Models Reinforcement Learning
The Chinese University of Hong Kong · MBZUAI · Hong Kong University of Science and Technology
Agentic Discovery of Non-Canonical Antimicrobial Peptides with AMPGAN v3
Jun 15, 2026Jay Jung, Xiaohan Zhang, Shenghan Song +8AI Agents for Scientific Discovery Molecular Generation
Department of Computer Science, University of Vermont, Burlington, VT, USA · Borch Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, USA · Department of Comparative Pathobiology, Purdue University, West Lafayette, IN, USA +3
Circuit Tracing in Autoregressive Protein Language Models
Jun 14, 2026Darin Tsui, William Deinzer, Daniel Saeedi +1Circuit Discovery Protein Fitness Prediction
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atltanta, GA.
Reward Transport: Property Control in Flow Matching via Noise-Space Alignment
Jun 13, 2026Kehan Guo, Yili Shen, Yujun Zhou +4Flow Matching Molecular Optimization
University of Notre Dame · Carnegie Mellon University
PepALD: Macrocyclic Peptide Generation via Autoregressive Latent Diffusion
Jun 12, 2026Junming Zhang, Siyu Yi, Wei Ju +1Diffusion Model Alignment Autoregressive Diffusion
College of Computer Science, Sichuan University, Chengdu, 610065, China · School of Mathematics, Sichuan University, Chengdu, 610065, China · School of Artificial Intelligence, Sichuan University, Chengdu, 610065, China +1
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
APCyc: Property-Informed Design of Cyclic Peptides via Automated Cyclization
Jun 11, 2026Yifan Zhao, Lang Qin, Jintai ChenMolecular Optimization Molecular Generation
AI Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, China · AI-Peptide Drug Design Joint Laboratory The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, China
Comprehensive pKa Data Augmentation from Limited Real Data through an Engineered Models-Quantum Framework
Jun 10, 2026Wang Rui, Liu DinghaoSynthetic Data Augmentation Quantum Annealing
Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China · Department of Chemical Engineering, Tsinghua University, Beijing 100084, P. R. China · School of Science, China Pharmaceutical University, Nanjing 211198, P. R. China
Augmenting Molecular Language Models with Local $n$ -gram Memory
Jun 10, 2026Xinni Zhang, Zijing Liu, He Cao +2Memory-Augmented Language Models Language Modeling
1The Chinese University of Hong Kong · 2International Digital Economy Academy
Flexible Flows for Biological Sequence Design
Jun 9, 2026Yogesh Verma, Dani Korpela, Harri Lähdesmäki +1Flow Matching Molecular Generation
Aalto University, Finland · Aalto University and YaiYai Ltd
Closing the Prior-Posterior Loop: Self-Reflective Molecular Design with Analysis-Driven LLM Iteration
Jun 8, 2026Junyi Gong, Zijie Qiu, Ben Zhong TangMolecular Optimization LLM Self-Refinement
Faculty of Chemistry, Shenzhen MSU-BIT University · School of Science and Engineering, Chinese University of Hong Kong (Shenzhen) · Department of Chemistry, Hong Kong University of Science and Technology
Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules
Jun 7, 2026Riccardo De Santi, Bruce Lee, Cristian Perez Jensen +6Synthetic Data Pretraining Normalizing Flows
ETH Zurich ETH AI Center · University of Pennsylvania · Caltech FutureHouse
De novo molecular generation with optical property preconditioning at the token level
Jun 6, 2026Haozhe Huang, Manuel Gonzalez Lastre, Hyun Suk Park +3Materials Science Molecular Generation
Department of Computer Science, University of Toronto, 40 St George St., Toronto, ON M5S 2E4, Canada · 2Vector Institute for Artificial Intelligence, W1140-108 College St., Schwartz Reisman Innovation Campus, Toronto, ON M5G 0C6, Canada · 3Departamento de Física Teórica de la Materia Condensada, Universidad Autónoma de Madrid, 28049, Madrid, Spain. +6
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
ShallowBench: Benchmarking Generative Drug Design Models on Shallow-Pocket Targets
Jun 4, 2026Saket Reddy, Shiwei LiuBenchmark Design Structure-Based Drug Design
University of Illinois - Urbana-Champaign
Plug-and-Play Guidance for Discrete Diffusion Models via Gradient-Informed Logit Correction
Jun 4, 2026Hongkun Dou, Zike Chen, Fengji Li +2Diffusion Model Guidance Molecular Generation
Beihang University, Beijing, China · Zhongguancun Academy, Beijing, China.
Agentic Molecular Recovery via Molecule-Aware Exploration
Jun 4, 2026Suwan Yoon, Changhee LeeLLM Self-Correction Molecular Generation
Department of Artificial Intelligence, Korea University / Seoul, Korea
Rethinking Molecular Text Representations for LLMs: An Empirical Study
Jun 2, 2026Arun Raja, Garrett M. Morris, Kian Ming A. ChaiLLM Evaluation Molecular Generation
University of Oxford · DSO National Laboratories

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MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules

Tong Xu, Xinzhe Cao, Zhihui Zhu, Keyan Ding+1

Zhejiang University · ZJU-Hangzhou Global Scientific and Technological Innovation Center · University of Oxford

Molecular Generation

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Current molecular generation benchmarks emphasize task complexity, molecule novelty, and property alignment; they largely overlook a critical concern: the potential safety risks of AI-generated molecules. In practice, many generative models may produce molecules with toxic, reactive, or otherwise hazardous characteristics - posing hidden dangers that remain insufficiently addressed. To address this gap, we introduce MolSafeEval, a benchmark dedicated to evaluating and analyzing the safety risks of molecular generation. Unlike prior approaches that rely on narrow toxicity predictors, MolSafeEval integrates heterogeneous safety knowledge - ranging from toxicological databases to hazard rules - into a structured molecular safety knowledge graph. This graph serves as a foundation for large language model-based reasoning, enabling systematic detection and explanation of unsafe features in generated compounds. We further categorize molecular generative models into four representative task types - unconditional generation, property optimization, target protein-based design, and text-based generation - and provide standardized datasets and safety evaluation protocols for each. By systematically revealing the safety vulnerabilities of current generative approaches, MolSafeEval offers a new lens for benchmarking molecular models and provides essential guidance toward safer, more trustworthy molecular design.

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Latest papers 174