Molecular Generation

Follow this topic Ask about this topic

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.

Conditionally Site-Independent Neural Evolution of Antibody Sequences
Feb 21, 2026Stephen Zhewen Lu, Aakarsh Vermani, Kohei Sanno +4Markov Chains Molecular Generation
University of California, Berkeley · Mila - Québec AI Institute · Université de Montréal +4
Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generative Modeling
Feb 18, 2026Chuqin Geng, Li Zhang, Mark Zhang +3Constrained Generative Modeling Satisfiability Modulo Theories
School of Computer Science, McGill University, Montreal, Canada · Department of Computer Science, University of Toronto, Toronto, Canada
Retrieval-Augmented Foundation Models for Matched Molecular Pair Transformations to Recapitulate Medicinal Chemistry Intuition
Feb 18, 2026Bo Pan, Peter Zhiping Zhang, Hao-Wei Pang +4Molecular Generation Drug Discovery
Department of Computer Science, Emory University, Atlanta, GA, USA · Merck & Co., Inc., Rahway, NJ, USA
Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer
Feb 17, 2026Hayato Kunugi, Mohsen Rahmani, Yosuke Iyama +8Molecular Generation Quantum Generative Modeling
Inovation to Implementation Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1 Murasaki-cho, Takatsuki, 569-1125, Japan. · D-Wave Systems Inc., 3033 Beta Ave, Burnaby, V5G 4M9, Canada.
MAC-AMP: A Closed-Loop Multi-Agent Collaboration System for Multi-Objective Antimicrobial Peptide Design
Feb 16, 2026Gen Zhou, Sugitha Janarthanan, Lianghong Chen +1Molecular Generation Multi-Agent LLM Systems
Department of Computer Science, Western University, London, ON, Canada · Department of Biochemistry, Western University, London, ON, Canada
QSAR-Guided Generative Framework for the Discovery of Synthetically Viable Odorants
Dec 28, 2025Tim C. Pearce, Ahmed IbrahimMolecular Generation Variational Autoencoders
Biomedical Engineering Research Group, School of Engineering, University of Leicester, University Road, Leicester LE1 7RH, United Kingdom · School of Clinical Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0SP, United Kingdom
Boltzmann generators for amorphous particle systems
Dec 18, 2025Louis Grenioux, Leonardo Galliano, Ludovic Berthier +2Boltzmann Sampling Continuous Normalizing Flows
CMAP, CNRS, École polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France · Center for Computational Mathematics, Flatiron Institute, New York, NY, USA · Laboratoire de Physique de l’École normale supérieure ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, 75005 Paris, France +2
NMIRacle: Multi-modal Generative Molecular Elucidation from IR and NMR Spectra
Dec 17, 2025Federico Ottomano, Yingzhen Li, Alex M. GanoseMultimodal Generation Molecular Generation
Department of Chemistry, Imperial College London · Department of Computing, Imperial College London
FRAGMENTA: Efficient End-to-end Fragmentation-based Generative Model with Agentic Tuning for Drug Lead Optimization in Small Data Regime
Nov 25, 2025Yuto Suzuki, Paul Awolade, Daniel V. LaBarbera +1Molecular Generation Molecular Optimization
Department of Computer Science and Engineering University of Colorado Denver Denver, CO, USA · Skaggs School of Pharmacy University of Colorado Anschutz Medical Campus Aurora, CO, USA
STAR-VAE: A Scalable Latent-Variable Transformer for Controllable Molecular Generation
Nov 4, 2025Bum Chul Kwon, Ben Shapira, Moshiko Raboh +7Molecular Optimization Transformer
IBM Research, Cambridge, MA, USA · IBM Research, Haifa, Israel · IBM Research, Yorktown Heights, NY, USA
RNAGenScape: Property-Guided, Optimized Generation of mRNA Sequences with Manifold Langevin Dynamics
Oct 14, 2025Danqi Liao, Chen Liu, Xingzhi Sun +8Molecular Generation Molecular Optimization
Yale University
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
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
Curriculum-Augmented GFlowNets For mRNA Sequence Generation
Oct 4, 2025Aya Laajil, Abduragim Shtanchaev, Sajan Muhammad +4Molecular Generation Generative Flow Networks
Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE
SPID: Distilled Protein Backbone Generation
Oct 3, 2025Liyang Xie, Haoran Zhang, Zhendong Wang +2Diffusion Model Distillation Few-Step Diffusion Sampling
The University of Texas at Austin · The Memorial Sloan Kettering Cancer Center
Why Pool When You Can Flow? Active Learning with GFlowNets
Aug 31, 2025Renfei Zhang, Mohit Pandey, Artem Cherkasov +1Active Learning Virtual Screening
School of Computer Science, Simon Fraser University, Burnaby, BC, Canada · Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, Canada · Diagen AI +1
Cross-Modality Controlled Molecule Generation with Diffusion Language Model
Aug 20, 2025Yunzhe Zhang, Yifei Wang, Khanh Vinh Nguyen +1Diffusion Model Guidance Multimodal Generation
Brandeis University, Waltham, MA, USA
A Large Scale Investigation of Scaling Limits in Chemical Language Models
Aug 19, 2025Roshan Balaji, Kamran Chitsaz, Quentin Fournier +2Chemical Language Models Language Model Scaling Laws
BioSystems Engineering and Control Lab · Wadhwani School of Data Science and AI, IIT Madras · The Centre for Integrative Biology and Systems medicinE (IBSE) +5
Generative molecule evolution using 3D pharmacophore for efficient Structure-Based Drug Design
Jul 27, 2025Yi He, Ailun Wang, Zhi Wang +3Structure-Based Drug Design Molecular Generation
ByteDance Seed
ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning
Jun 9, 2025Ziwen Wang, Jiajun Fan, Ruihan Guo +3Protein Language Models Molecular Generation
Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, IL, USA · DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, IL, USA
PDFBench: A Benchmark for De novo Protein Design from Function
May 25, 2025Jiahao Kuang, Nuowei Liu, Jie Wang +3Benchmark Design Molecular Generation
School of Computer Science and Technology, East China Normal University · Institute of Artificial Intelligence (TeleAI), China Telecom · College of Foreign Languages and Literatures, Fudan University
OmegAMP: Targeted AMP Discovery via Biologically Informed Generation
Apr 24, 2025Diogo Soares, Leon Hetzel, Paulina Szymczak +6Molecular Generation Conditional Generative Modeling
Institute of AI for Health, Helmholtz Munich · School of Computation, Information and Technology, Technical University of Munich · Institute of Computational Biology, Helmholtz Munich +8
Active Learning Enables Generation of Molecules that Advance the Known Pareto Front
Jan 3, 2025Evan R. Antoniuk, Peggy Li, Nathan Keilbart +3Molecular Optimization OOD Generalization
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
Small Molecule Optimization with Large Language Models
Jul 26, 2024Philipp Guevorguian, Menua Bedrosian, Tigran Fahradyan +3Molecular Optimization Evolutionary Optimization
YerevaNN research lab, Yerevan, Armenia.

Feb 21, 2026J/K move · Enter open · S save

Conditionally Site-Independent Neural Evolution of Antibody Sequences

Stephen Zhewen Lu, Aakarsh Vermani, Kohei Sanno, Jiarui Lu+3

University of California, Berkeley · Mila - Québec AI Institute · Université de Montréal+4

Markov Chains Molecular Generation Protein Fitness Prediction

Open paper AskPDF arXiv

Common deep learning approaches for antibody engineering focus on modeling the marginal distribution of sequences. By treating sequences as independent samples, however, these methods overlook affinity maturation as a rich and largely untapped source of information about the evolutionary process by which antibodies explore the underlying fitness landscape. In contrast, classical phylogenetic models explicitly represent evolutionary dynamics but lack the expressivity to capture complex epistatic interactions. We bridge this gap with CoSiNE, a continuous-time Markov chain parameterized by a deep neural network. Mathematically, we prove that CoSiNE provides a first-order approximation to the intractable sequential point mutation process, capturing epistatic effects with an error bound that is quadratic in branch length. Empirically, CoSiNE outperforms state-of-the-art language models in zero-shot variant effect prediction by explicitly disentangling selection from context-dependent somatic hypermutation. Finally, we introduce Guided Gillespie, a classifier-guided sampling scheme that steers CoSiNE at inference time, enabling efficient optimization of antibody binding affinity toward specific antigens.