Molecular Property Prediction

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19 papers in the last four weeks, up 46% on the four weeks before. 0.2% of all new papers.

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Latest papers 133

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MōLe-Λ: Learning the Coupled-Cluster Response State for Energies, Gradients, and Properties
May 28, 2026Andreas Burger, Luca Thiede, Abdulrahman Aldossary +4Neural Surrogate Modeling Quantum Chemistry
University of Toronto, Canada · Vector Institute for Artificial Intelligence, Toronto, Canada · NVIDIA, Toronto, Canada +2
SCENT: Aligning Mass Spectra with Molecular Structure for Olfactory Perception
May 26, 2026Ziqi Zhang, Eunyeong Jin, Miguel Vasco +6Cross-Modal Alignment Molecular Property Prediction
1Dept. of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden · 2Atmospheric Chemistry Dept., Max Planck Institute for Chemistry, Mainz, Germany · 3Dept. of Information Technology, Uppsala University, Uppsala, Sweden +1
What Molecular Structure Cannot Tell Us: A Taxonomy of Explainability Gaps in GNN-Based Drug Toxicity Prediction
May 25, 2026Juergen DietrichMolecular Property Prediction GNN Explainability
Aligning Molecular Graph Explanations with Chemical Identity via InChIfied Invariants
May 23, 2026Emanuele Guidotti, Sara PuglioliMolecular Property Prediction Invariant Representation Learning
University of Lugano, Switzerland · Philochem AG, Switzerland
Hierarchical Contrastive Learning for Multi-Domain Protein-Ligand Binding
May 19, 2026Shuo Zhang, Rongqi Hong, Huifeng Zhang +1Protein-Ligand Binding Affinity Prediction Contrastive Learning
University of Birmingham, UK
When Molecular Similarity Works: Property Cliffs Reveal Hidden Errors
May 17, 2026Di Hu, Kun Li, Haojie Rao +6Molecular Property Prediction
School of Economics and Management · School of Computer Science, Wuhan University Wuhan, China · Department of Data Science and Artificial Intelligence, Monash University Victoria, Australia +2
Tensor Channel Equivariant Graph Neural Networks for Molecular Polarizability Prediction
May 16, 2026Jean Philip Filling, Daniel Franzen, Michael WandEquivariant GNNs Molecular Property Prediction
Institute for Computer Science, Johannes Gutenberg University Mainz, Germany
Structure-Aware Masking for Protein Representation Learning
May 15, 2026Thomas Walton, Ayan Goel, Amirali AghazadehProtein Fitness Prediction Masked Language Modeling
Georgia Institute of Technology
Multi-level Self-supervised Pretraining on Compositional Hierarchical Graph for Molecular Property Prediction
May 15, 2026Xiayu Liu, Zhengyi Lu, Hou-biao LiSelf-Supervised Pre-Training Hierarchical Representation Learning
School of Mathematical Sciences, University of Electronic Science and Technology of China, No.2006, Xiyuan Avenue, West Hi-tech Zone,2026 611731, Chengdu, China · Department of Computer Science and Engineering, Oakland University, 201 Meadow Brook RD, Rochester Hills, 48309, MI, USA
DrugSAGE:Self-evolving Agent Experience for Efficient State-of-the-Art Drug Discovery
May 14, 2026Yikun Zhang, Xiwei Cheng, Tianyu Liu +2AI Agents for Scientific Discovery AI-Assisted Scientific Research
1Northeastern University · 2Broad Institute of MIT and Harvard · 3Yale University +1
Learning to Select Source Domains: Proxy-Rewarded Policy Optimization for Molecular OOD Generalization
May 13, 2026Zhuohao Lin, Kun Li, Jiameng Chen +4Unsupervised Domain Adaptation Domain Adaptation
School of Computer Science Wuhan University Wuhan, China · College of Computer Science and Technology, Zhejiang University Hangzhou, China · School of Life Sciences and Technology, Tongji University Shanghai, 200092, China +1
Chem-GMNet: A Sphere-Native Geometric Transformer for Molecular Property Prediction
May 13, 2026Deepak Warrier, Raja Sekhar PappalaTransformer Geometric Representation Learning
MSTACK AI
Decomposing the Generalization Gap in PROTAC Activity Prediction: Variance Attribution and the Inter-Laboratory Ceiling
May 12, 2026Thor Klamt, Wolfgang Nejdl, Ming TangMolecular Property Prediction Scientific Reproducibility
L3S Research Center, Leibniz Universität Hannover, Appelstraße 9a, 30167 Hannover, Germany · Institute of Data Science (Knowledge-Based Systems), Faculty of Electrical Engineering and Computer Science,2026 Leibniz Universität Hannover, Appelstraße 9a, 30167 Hannover, Germany
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 OOD Generalization
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.
It's All Connected: Topology-Aware Structural Graph Encoding Improves Performance on Polymer Prediction
May 11, 2026H. Ibrahim Erdogan, Punith Raviswamy, Nikita Agrawal +5Graph Representation Learning Materials Property Prediction
University of Bayreuth, Germany · Faculty of Engineering Science, University of Bayreuth, Germany · Faculty of Mathematics, Physics & Computer Science, University of Bayreuth, Germany +5
Physical probes expose and alleviate chemical-environment collapse in molecular representations
May 11, 2026Jiebin Fang, Zidi Yan, Churu Mao +7Contrastive Learning Cross-Modal Representation Learning
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
MolSight: Molecular Property Prediction with Images
May 11, 2026Aaditya Baranwal, Akshaj Gupta, Yogesh S Rawat +1Curriculum Learning Molecular Property Prediction
University of Central Florida, Orlando, Florida, USA · Birla Institute of Technology and Science, Pilani, Rajasthan, India
Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials
May 9, 2026Amir Masoud Nourollah, Irtaza Khalid, Stefano Leoni +1Machine Learning Interatomic Potentials OOD Generalization
Cardiff University Cardiff, CF10 3AT, UK
Structural Interpretations of Protein Language Model Representations via Differentiable Graph Partitioning
May 9, 2026Siddhant Dutta, Edward Tan Beng Wai, Soumick Sarker +2Graph Neural Networks Protein Language Models
Nanyang Technological University, Singapore
Better Protein Function Prediction by Modeling Survivorship Bias
May 7, 2026Zhongmou Chao, Poompol Buathong, Ekaterina Selivanovitch +2Weakly Supervised Learning Molecular Property Prediction
Smith School of Chemical and Biomolecular Engineering, Cornell University, USA · Center for Applied Mathematics, Cornell University, USA · School of Operations Research and Information Engineering, Cornell University, USA
Edge-specific signal propagation on mature chromophore-region 3D mechanism graphs for fluorescent protein quantum-yield prediction
May 7, 2026Yuchen Xiong, Swee Keong Yeap, Steven Aw Yoong KitGraph Representation Learning Molecular Property Prediction
1China-ASEAN College of Marine Sciences, Xiamen University Malaysia, Sepang 43900, Selangor, Malaysia
Bolek: A Multimodal Language Model for Molecular Reasoning
May 4, 2026Frederic Grabowski, Jacek Szczerbiński, Maciej Jaśkowski +4LLM Grounding Scientific Reasoning
1Ingenix.ai, Warsaw, Poland · Laboratory of Bioinformatics and Computational Genomics, Warsaw University of Technology, Warsaw, Poland
A Comparative Study of QSPR Methods on a Unique Multitask PAMPA dataset
May 1, 2026Andrs Formanek, Anna Vincze, Richrd Bicsak +3Multi-Task Learning Molecular Property Prediction
Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems,May Signal Processing and Data Analytics, KU Leuven, 3001 Leuven, Belgium · Department of Artificial Intelligence and Systems Engineering, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3, H-1111 Budapest, Hungary[cs.LG] · Department of Chemical and Environmental Process Engineering, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3, H-1111 Budapest, Hungary +2
Hyper-Dimensional Fingerprints as Molecular Representations
Apr 30, 2026Jonas Teufel, Luca Torresi, André Eberhard +1Hyperdimensional Computing Molecular Property Prediction
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.
Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction
Apr 29, 2026Jinjiang Guo, Sheng DingLLM Evaluation Molecular Property Prediction
Global Health Drug Discovery Institute, Beijing 100084, China · School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
Enhancing molecular dynamics with equivariant machine-learned densities
Apr 27, 2026Mihail Bogojeski, Muhammad R. Hasyim, Leslie Vogt-Maranto +3Machine Learning Interatomic Potentials Quantum Chemistry
BIFOLD and Machine Learning Group, Technische Universität Berlin, Franklinstr. 28/29, 10587 Berlin, Germany · Department of Chemistry, New York University, New York, NY 10003, USA · Department of Artificial Intelligence, Korea University, Seoul 02841, Korea +5
h-MINT: Modeling Pocket-Ligand Binding with Hierarchical Molecular Interaction Network
Apr 25, 2026Yanru Qu, Yijie Zhang, Wenjuan Tan +6Protein-Ligand Binding Affinity Prediction Hierarchical Representation Learning
Department of Computer Science, UIUC · School of Computer Science, McGill University · MILA-Québec AI Institute +4
HBGSA: Hydrogen Bond Graph with Self-Attention for Drug-Target Binding Affinity Prediction
Apr 25, 2026Junxiao Kong, Chupei Tang, Di Wang +4Protein-Ligand Binding Affinity Prediction Graph Neural Networks
School of Mathematics and Statistics, Lanzhou University, 222 South Tianshui Road, 730000, Gansu, China · Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong 999077, China
Graph-Theoretic Models for the Prediction of Molecular Measurements
Apr 21, 2026Anna Niane, Prudence DjagbaMolecular Property Prediction
African Institute for Mathematical Sciences (AIMS), Mbour, Senegal. · Lyman Briggs College, Michigan State University, East Lansing, Michigan, USA.
Evaluating the Progression of Large Language Model Capabilities for Small-Molecule Drug Design
Apr 17, 2026Shriram Chennakesavalu, Kirill Shmilovich, Hayley Weir +5LLM Evaluation Molecular Property Prediction
Prescient Design, Genentech, South San Francisco, CA, USA

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MōLe-Λ: Learning the Coupled-Cluster Response State for Energies, Gradients, and Properties

Andreas Burger, Luca Thiede, Abdulrahman Aldossary, Jorge A. Campos-Gonzalez-Angulo+3

University of Toronto, Canada · Vector Institute for Artificial Intelligence, Toronto, Canada · NVIDIA, Toronto, Canada+2

Neural Surrogate Modeling Quantum Chemistry Molecular Property Prediction

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Coupled-cluster (CC) theory is often considered the gold standard of quantum chemistry, but its high computational cost limits routine access to accurate energies, forces and response properties. While the right-hand

T

-amplitudes determine the correlated wavefunction, many practically important observables additionally require the left-hand

Λ

-amplitudes. We introduce MōLe-

Λ

, an extension of Molecular Orbital Learning (MōLe) that predicts the full ground-state coupled-cluster singles and doubles (CCSD) response state by jointly learning right-hand amplitudes

(T_1,T_2)

and left-hand amplitudes

(Λ_1,Λ_2)

from localized Hartree--Fock molecular orbitals. Architecturally, MōLe-

Λ

extends MōLe with

Λ_1

and

Λ_2

readouts that mirror the symmetry constraints of the

T_1

and

T_2

heads, while preserving the original equivariant orbital encoder, odd sign-equivariant decoding, locality and size-extensivity. The resulting model yields accurate CC-quality energies and forces, while simultaneously recovering dipoles, quadrupoles, polarizabilities, the electron density, and 2-electron observables such as the pair density. We show that MōLe-

Λ

further extends the speed advantage of MōLe over full CCSD while substantially expanding the accessible properties, providing a route to wavefunction-level surrogate models for correlated quantum chemistry.

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