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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Latest papers 172

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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
Fairness Definitions and Metrics in Deep Reinforcement Learning for Drug Discovery in Healthcare: A Rapid Evidence Review
Jun 1, 2026Esmaeil Shakeri, Ronnie de Souza Santos, Behrouz FarReinforcement Learning Algorithmic Fairness
Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Canada
AgentPLM: Agentic Protein Language Models with Reasoning-Augmented Decoding for Protein Sequence Design
Jun 1, 2026Sahil Rahman, Maxx Richard RahmanRL for Language Models Direct Preference Optimization
Bedford College, London, United Kingdom · Saarland University, Saarbrücken, Germany

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Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps

RuiKang OuYang, Hanlin Yu, Xinyue Ai, Yutong He+6

University of Cambridge · University of Helsinki · Peking University+3

Flow Map Learning Generative Modeling Molecular Generation Flow-Based Generative Modeling

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Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, there is a lack of similar advances in estimating the model likelihood. In particular, most existing methods either rely on restrictive architectures that enable exact calculations, or use stochastic approximations such as Hutchinson's trace estimator that introduce substantial variance. In this work, we introduce SCAlable LikeLihood distillation of flOw maPs (SCALLOP). SCALLOP builds on the recently proposed F2D2, a likelihood flow map model that can generate samples and their densities in a small number of function evaluations. While F2D2 uses Hutchinson's estimator during training, we introduce an alternative and more scalable likelihood distillation objective that is Hutchinson-free and admits a vectorized formulation. Empirically, we demonstrate the effectiveness of SCALLOP as a Boltzmann generator in molecular science, and further validate its benefit on image datasets. SCALLOP significantly reduces both training variance and training time while consistently improving performance compared to F2D2, and is competitive with the state-of-the-art while achieving up to 10x inference speedup over the fastest baseline.

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Latest papers 172