Molecular Representation Learning

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1 paper in the last four weeks, down 83% on the four weeks before. 0.0% of all new papers.

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scTrilemma: Balancing Identity, Invariance, and Fidelity in Single-Cell Representation Learning
Sep 30, 2026Yunhak Oh, Yoonho Lee, Junseok Lee +9Cells Molecular Representation Learning
KAIST · Work done during an internship at HITS · HITS
Multi-View Molecular Representation Learning with Hierarchical Graphs and Contextualized Fingerprints
Sep 14, 2026Gwang-Hyeon Yun, Jong-Hoon Park, Bing Hu +3Molecular Representation Learning Molecular Property Prediction
Yonsei University · Wonju-si, Republic of Korea · University of Waterloo +1
Structural Hierarchy and Geometry in Molecular Representation Learning
Aug 30, 2026David Sulu, Lorenzo Di Fruscia, Jana M. WeberMolecular Representation Learning Moleculenet
Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands
Mol-JEPA: A multimodal Joint Embedding Predictive Architecture for Molecules
Aug 23, 2026Florian Rottach, Sebastian Schieferdecker, William Rudman +2Molecular Representation Learning Molecules
University of Tübingen · 2Boehringer Ingelheim · 3The University of Texas at Austin +1
BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells
Aug 6, 2026Yuhao Wang, Zelin Zang, Yuxuan Liu +2Joint-Embedding Predictive Architectures Molecular Representation Learning
Westlake University, Hangzhou, Zhejiang, China · Centre for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences (HKISI-CAS), Hong Kong SAR, China · State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China +1
Physics-Based Molecular Fingerprints from Spectral Graph Theory Provide Efficient Geometry-Aware Measures of Chemical Similarity
Aug 5, 2026Jacob W. Toney, Ayleen Y. Farnood, Samir Darouich +1Molecular Representation Learning Chemical Structures
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Center for Computational Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Institute for Theoretical Chemistry, University of Stuttgart, 70569 Stuttgart, Germany +2
Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language
Aug 4, 2026David Ming Segura, Jeremy Goumaz, Joshua W. Sin +2Molecular Representation Learning Language Modeling
Laboratory of Artificial Chemical Intelligence (LIAC), EPFL, Lausanne, Switzerland · Process Chemistry & Catalysis, Synthetic Molecules Technical Development, F. Hoffmann-La Roche AG, Basel, Switzerland · National Centre of Competence in Research (NCCR) Catalysis, EPFL, Lausanne, Switzerland
Learning Molecular Representations from Cellular Phenotypes with Structure Preservation
Aug 3, 2026Xuan Lin, Jingyu Sheng, Tengfei Ma +2Molecular Representation Learning Chemical Structures
School of Computer Science, Xiangtan University, Xiangtan, Hunan, China · College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China · School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China +1
Beyond Predictive Accuracy: A Reliability-Aware Audit of Molecular Representations for Human Olfaction
Jul 25, 2026Kai Lun Huang, Wei Chieh SunMolecular Representation Learning Smells
California State University, Fullerton · Department of Electrical and Computer Engineering California State University, Fullerton Fullerton, CA, USA · University of Washington +1
Multimodal Molecular Representation Learning with Graph Neural Networks, Deep & Cross Networks, and SMILES Embeddings
Jul 7, 2026Qiwei Han, Chi Zhou, Ruobing Wang +1Molecular Representation Learning Graph Neural Networks
Department of Chemistry, Duke University, Durham, NC 27708, USA · Department of Computer Science, Georgia Institute of Technology, Atlanta, GA 30332, USA
MOLAR: Learning Multimodal Molecular Representations from Noisy Labels
Jun 16, 2026Yingxu Wang, Kunyu Zhang, Nan Yin +2Molecular Representation Learning Molecules
Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, AI Diyafah St, 7909, Abu Dhabi, United Arab Emirates · Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China · International College, Zhengzhou University, Daxue North Road, 450000, Henan, China +2
Rethinking Molecular Text Representations for LLMs: An Empirical Study
Jun 2, 2026Arun Raja, Garrett M. Morris, Kian Ming A. ChaiMolecular Representation Learning Chemical Structures
University of Oxford · DSO National Laboratories
Effective Biological Representation Learning by Masking Gene Expression
May 29, 2026Kian Kenyon-Dean, Alina Selega, Ihab Bendidi +5Transcriptomics Genome-Wide Association Study
Recursion · Valence Labs · École Normale Supérieure PSL
The Geometry of Activity Cliffs: Representation Dependence and Multi-Scale Characterization of Activity Landscapes
May 29, 2026Pawel Dabrowski-Tumanski, Bartosz Topolski, Dariusz Plewczynski +1Molecular Representation Learning Ridge
†Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw 00-662, Poland · ¶Ingenix.AI · §Centre of New Technologies, University of Warsaw, Warsaw 02-097, Poland
Aligning Molecular Graph Explanations with Chemical Identity via InChIfied Invariants
May 23, 2026Emanuele Guidotti, Sara PuglioliMolecular Representation Learning Chemical Structures
University of Lugano, Switzerland · Philochem AG, Switzerland
VQ-Atom: Semantic Discretization of Local Atomic Environments for Molecular Representation Learning
May 16, 2026Takayuki KimuraMolecular Representation Learning Discretization
Atoms as Language, LLC
Cross-Domain Molecular Relational Learning: Leveraging Chemical Structure-Activity Analysis
May 16, 2026Peiliang Zhang, Jingling Yuan, Shiqing Wu +4Molecular Representation Learning Molecular Design
Wuhan University of Technology Wuhan, Hubei, China · Hubei Key Laboratory of Transportation Internet of Things State Key Laboratory of Silicate Materials for Architectures Wuhan University of Technology Wuhan, Hubei, China · Yonsei University Seoul, Republic of Korea +3
MoleCode unlocks structural intelligence in large language models
May 15, 2026Zhiyuan Yan, Chen Liu, Boxuan Zhao +8Molecular Representation Learning Moleculenet
Peking University Shenzhen Graduage School
On Improving Graph Neural Networks for QSAR by Pre-training on Extended-Connectivity Fingerprints
May 11, 2026Sam Money-Kyrle, Markus Dablander, Thierry Hanser +3Graph Neural Networks Molecular Representation Learning
Department of Statistics, University of Oxford, 24-29 St Giles’, Oxford, OX1 3LB, United Kingdom. · Mathematical Institute, University of Oxford, Andrew Wiles Building, Woodstock Road, Oxford, OX2 6GG, United Kingdom. · Molecular Informatics and AI, Lhasa Limited, Granary Wharf House,May 2 Canal Wharf, Leeds, LS11 5PS, United Kingdom.
Physical probes expose and alleviate chemical-environment collapse in molecular representations
May 11, 2026Jiebin Fang, Zidi Yan, Churu Mao +7Molecular Representation Learning Environment
Hainan Institute, Zhejiang University, Sanya 572025, China · Institute of Marine Biology and Pharmacology, Ocean College, Zhejiang University, Zhoushan 316021, China · School of Food and Pharmacy, Zhejiang Ocean University, Zhoushan 316021, China +2
Toward Better Geometric Representations for Molecule Generative Models
May 8, 2026Shaoheng Yan, Zian Li, Cai Zhou +3Molecular Generation Molecular Representation Learning
Institute for Artificial Intelligence, PKU · ByteDance AI Drug Discovery · School of Intelligence Science and Technology, PKU +3
Molecules Meet Language: Confound-Aware Representation Learning and Chemical Property Steering in Transformer-VAE Latent Spaces
May 7, 2026Zakaria Elabid, Jan Andrzejewski, Bartosz Brzoza +1Molecular Representation Learning Molecular Generation
Helmholtz-Zentrum Dresden-Rossendorf Center for Advanced Systems Understanding
Molecular Representations for Large Language Models
May 3, 2026Nicholas T. Runcie, Fergus Imrie, Charlotte M. DeaneMolecular Representation Learning Molecular Generation
Department of Statistics University of Oxford Oxford, UK
Hyper-Dimensional Fingerprints as Molecular Representations
Apr 30, 2026Jonas Teufel, Luca Torresi, André Eberhard +1Molecular Representation Learning Molecular Optimization
Karlsruhe Institute of Technology (KIT), Institute of Nanotechnology (INT), Kaiserstraße 12, Karlsruhe, Germany. · Karlsruhe Institute of Technology (KIT), Institute of Anthropomatics and Robotics (IAR), Kaiserstraße 12, Karlsruhe, Germany.
Advancing Ligand-based Virtual Screening and Molecular Generation with Pretrained Molecular Embedding Distance
Apr 27, 2026Shiyun Wa, Yifei Wang, Simone Sciabola +1Molecular Representation Learning Drug Design
Biogen, Cambridge, MA, USA
h-MINT: Modeling Pocket-Ligand Binding with Hierarchical Molecular Interaction Network
Apr 25, 2026Yanru Qu, Yijie Zhang, Wenjuan Tan +6Molecular Representation Learning Screening
Department of Computer Science, UIUC · School of Computer Science, McGill University · MILA-Québec AI Institute +4
Evaluating the Progression of Large Language Model Capabilities for Small-Molecule Drug Design
Apr 17, 2026Shriram Chennakesavalu, Kirill Shmilovich, Hayley Weir +5Molecular Representation Learning
Prescient Design, Genentech, South San Francisco, CA, USA
Tabular foundation models for in-context prediction of molecular properties
Apr 17, 2026Karim K. Ben Hicham, Jan G. Rittig, Martin Grohe +1Molecular Property Prediction Molecular Representation Learning
RWTH Aachen University, Process Systems Engineering (AVT.SVT), Aachen, Germany. · Lehrstuhl Informatik 7, RWTH Aachen University, Aachen, Germany. · JARA-CSD, Aachen, Germany. +1

Sep 30, 2026, cs.LGJ/K move · Enter open · S save

scTrilemma: Balancing Identity, Invariance, and Fidelity in Single-Cell Representation Learning

Yunhak Oh, Yoonho Lee, Junseok Lee, Namkyeong Lee+8

KAIST · Work done during an internship at HITS · HITS

Cells Molecular Representation Learning Conditional Variational Autoencoder

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Single-cell RNA-seq representation learning is fundamentally label-free: cell identities, states, and contexts are not fixed training targets, so what constitutes signal or nuisance is analysis-dependent. A single representation must therefore preserve biological identity and state, remain robust to nuisance context, and retain the gene-level variation needed for expression analysis, three demands we call the representation trilemma. To tackle this problem, we introduce scTrilemma, a latent-bottleneck VAE that routes expression-derived variation to the embedding, the decoder, or the prior rather than forcing all of it through one embedding. It gates gene tokens by expression, routes the cell representation through the decoder, and conditions the prior on unlabeled pseudo-bulk context, under a single reconstruction objective and without target annotations or auxiliary representation losses. In release-based zero-shot evaluation on successive CZ CELLxGENE Census releases, scTrilemma leads all three demands at once and preserves biological-state, differential-expression, and pathway structure across multiple disease settings. Latent interventions further show that context can be removed at almost no cost to the other demands, leaving identity against fidelity as the remaining tension. Code is publicly available at https://github.com/yunhak0/scTrilemma.

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