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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Physics-Aligned Electronic Ground-State Learning Improves Generalization
Oct 7, 2026Eike S. Eberhard, Xaver Kainz, Viktor Kotsev +2Physics-Informed ML OOD Generalization
Technical University of Munich (TUM) · Munich Data Science Institute (MDSI) · Munich Center for Machine Learning (MCML) +1
Atom-JEPA: Joint-Embedding Predictive Architecture for 3D Atomistic Systems
Oct 6, 2026Kasper Helverskov Petersen, Rasmus Hannibal Tirsgaard, François R J Cornet +2Joint-Embedding Predictive Architecture Materials Property Prediction
Department of Applied Mathematics and Computer Science Technical University of Denmark
Can phenotypic activity be predicted without experimental readouts?
Oct 6, 2026Télio Cropsal, Rocío MercadoMolecular Property Prediction Molecular Representation Learning
AI Laboratory for Molecular Engineering (AIME) Department of Computer Science and Engineering Chalmers University of Technology & University of Gothenburg, Gothenburg, Sweden · AI Laboratory for Molecular Engineering (AIME) Department of Computer Science and Engineering Chalmers University of Technology & University of Gothenburg and Science for Life Laboratory (SciLifeLab), Gothenburg, Sweden
Mathematical Invariant-Enabled Topological Neural Networks for Molecular and Materials Property Prediction
Oct 6, 2026Yiming Ren, Xiang Liu, Mustafa Hajij +2Materials Property Prediction Molecular Property Prediction
Department of Mathematics, University of Georgia, Athens, GA 30602, USA. · Department of Data Science, University of San Francisco, CA 94117, USA. · Department of Computer Science and Technology, Cambridge University, Cambridge, United Kingdom. +2
Few-Shot Bioactivity Prediction with Meta-Learning under Assay Heterogeneity
Oct 5, 2026Michal Kmicikiewicz, Tommy Rochussen, Vincent Fortuin +1Meta-Learning Few-Shot Learning
Institute of AI for Health, Helmholtz Munich · School of Computation, Information and Technology, Technical University of Munich · Munich Center for Machine Learning +4
StabilityArc: Decoding Protein Sequence Embeddings into Generalizable Stability Landscapes
Sep 30, 2026Aaron L. Feller, Andrew D. Ellington, Claus O. WilkeTransfer Learning Molecular Property Prediction
The University of Texas at Austin Interdisciplinary Life Sciences · The University of Texas at Austin Integrative Biology
Molecular Property Prediction under Structural Shift with Tabular Foundation Models
Sep 30, 2026Jinmo Lee, Dooho Lee, Minho Jeong +1Tabular Foundation Models Molecular Property Prediction
Nums AI
AssayRouter: Historical Utility Priors for Frozen Molecular Predictor Routing
Sep 29, 2026Dong Xu, Zhangfan Yang, Jiantao Wu +5Adaptive Model Routing Task Transferability Estimation
School of Artificial Intelligence, Shenzhen University · EasternDawn · School of Computer Science, University of Nottingham Ningbo
A General Harness for Protein Foundation Model Fitness Prediction
Sep 28, 2026Yang Tan, Qijia Tian, Gangyu Sun +5Protein Fitness Prediction Protein Language Models
Shanghai Jiao Tong University · Shanghai Innovation Institute · Shanghai Artificial Intelligence Laboratory
One Sequence, Many Decodings: CAGenMol-2 Recasts Drug Design as Masked Molecular Inference
Sep 28, 2026Yanting Li, Enyan Dai, Lei Wang +2Molecular Optimization Masked Diffusion Models
The Hong Kong University of Science and Technology (Guangzhou) · Jinan University, Guangzhou
CalibHyper: Chance-Corrected Relational Hypergraphs for Few-Shot Molecular Property Prediction
Sep 27, 2026Linyu Li, Zhi Jin, Yuanpeng He +7Multi-Label Classification Few-Shot Learning
School of Computer Science, Peking University · Key Laboratory of High Confidence Software Technologies, Peking University, Ministry of Education · School of Computer Science, Wuhan University +1
Feature Space Selection and Heterogeneous Effect Estimation for Blood-Brain Barrier Permeability: A Random Forest to the Generalized Random Forest Pipeline
Sep 24, 2026Tshemollo Rapolai, Seite Makgai, Mohammad ArashiConditional Average Treatment Effect Estimation Molecular Property Prediction
Department of Statistics, University of Pretoria, Pretoria, South Africa · Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, P.O. Box 1159, Mashhad, 91775, Iran
Response-state Learning for Transferable Vibrational Spectroscopic Characterization with Electron Prior
Sep 24, 2026Zetong Li, Zhuosong Xie, Hengyu Fan +6Equivariant Neural Networks Raman Spectroscopy
Innovation Center for AI and Drug Discovery, School of Pharmacy, East China Normal University, Shanghai 200062, China.
The Mechanics of Delta Learning: Target Design for Generalizable Scientific Machine Learning
Sep 23, 2026Kareem M. Gameel, Ihor Neporozhnii, Sjoerd Hoogland +1Residual Learning Scientific ML
Department of Physical and Environmental Sciences, University of Toronto Scarborough Alliance for AI-Accelerated Materials Discovery (A3MD) Toronto, Ontario, Canada · Alliance for AI-Accelerated Materials Discovery (A3MD) Toronto, Ontario, Canada · Department of Chemistry, University of Toronto Department of Physical and Environmental Sciences, University of Toronto Scarborough Alliance for AI-Accelerated Materials Discovery (A3MD) Toronto, Ontario, Canada
SMILESGNN: Interpretable Clinical Toxicity Prediction via SMILES-Graph Cross-Attention Fusion
Sep 23, 2026Quang Minh Nguyen, Thuy Quynh Nguyen, Duc Minh Le +3Toxicity Detection Molecular Property Prediction
National Economics University Hanoi, Vietnam · VNU University of Science Hanoi, Vietnam
MT-ProtBERT: Multi-task Learning ProtBERT for Intrinsically Disordered Proteins Classification with Scarce Data
Sep 21, 2026Jian Sun, Kingshuk Ghosh, Lilianna Houston +1Protein Language Models Multi-Task Learning
Department of Computer Science, Ritchie School of Engineering and Computer Science, University of Denver, 2155 E Wesley Ave, Denver, CO 80210 · Department of Surgery, David Geffen School of Medicine at the University of California, Los Angeles, 10833 Le Conte Ave, Los Angeles, CA 90095 · Department of Physics and Astronomy, at the University of Denver, CO 80210 +1
Procedural Pretraining for Molecular Property Prediction
Sep 15, 2026Moritz Friedemann, Zachary Shinnick, Philip Torr +1Synthetic Data Pretraining Self-Supervised Pre-Training
University of Oxford · Adelaide University · Slater Labs
Multi-View Molecular Representation Learning with Hierarchical Graphs and Contextualized Fingerprints
Sep 14, 2026Gwang-Hyeon Yun, Jong-Hoon Park, Bing Hu +3Graph SSL Hierarchical Representation Learning
Yonsei University · Wonju-si, Republic of Korea · University of Waterloo +1
Predicting Collision Cross Sections with GRACE: Geometric Residual Adduct Conditioning via Early-fusion
Sep 14, 2026Parthasarathy Suryanarayanan, Susanta Das, Shreyans Sethi +3Molecular Property Prediction Mass Spectrometry
IBM Research, Yorktown Heights, NY 10598, United States · Center for Computational Life Sciences, Cleveland Clinic Research, The Cleveland Clinic, Cleveland, OH 44195, United States · Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
$\text{GSF-}\chi$ : Global Stereochemical Fields for Chiral Graph Transformers
Sep 14, 2026Jiaqing Xie, Yuxin Wang, Xipeng QiuRotary Positional Embeddings Molecular Property Prediction
Shanghai Innovation Institute · Fudan University
What an odour descriptor corpus can and cannot measure: valence, attenuation, and the ceiling of the public record
Sep 14, 2026Stylianos Kampakis, Fabio RovaiInter-Rater Reliability Molecular Property Prediction
Tesseract Academy, London, UK
Coherent Floquet quantum reservoirs for molecular property prediction
Sep 10, 2026Luofei Wang, Da Zhang, Congren Wang +5Dynamical Systems Quantum Reservoir Computing
Center for Quantum Technology Research and Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China · School of Computer Science and Technology, Shandong University, Qingdao 266230, China
An Explainable Machine Learning Framework for Predicting Blood-Brain Barrier Permeability Using Molecular Descriptors
Sep 9, 2026Fatemeh MahmoudiMolecular Property Prediction
Department of Materials Science and Engineering, Sharif University of Technology, Azadi St., Tehran, Iran
ProtLingo: Efficient Protein Language Modeling via Conditional Memory and Expert Routing
Sep 7, 2026Mingrui Li, Sixian Shen, Minzhang Li +4Mixture-of-Experts Language Models Protein Fitness Prediction
ShanghaiTech University
Molecular Déjà Vu: Digit-Level Retrieval of Molecular Properties in Frontier Language Models
Sep 4, 2026Matthias Busch, Marius Tacke, Sviatlana V. Lamaka +4LLM Evaluation LLM Reliability
Institute for Artificial Intelligence and Simulation in Mechanics, Hamburg University of Technology, Eißendorfer Straße, 21073 Hamburg, Germany · Institute of Material Systems Modeling, Helmholtz-Zentrum Hereon, Max-Planck-Straße, 21502 Geesthacht, Germany · Institute of Surface Science, Helmholtz-Zentrum Hereon, Max-Planck-Straße, 21502 Geesthacht, Germany +3
Analysis of Prompt Engineering for Drug Toxicity Prediction
Sep 3, 2026Mia MacGregor, Aakash Welgamage Don, Mark BartlettPrompt Sensitivity LLM Prompting
School of Computing, Engineering & Technology, Robert Gordon University, Aberdeen, UK. · School of Pharmacy, Applied Sciences & Public Health, Robert Gordon University, Aberdeen, UK.
CliffRank: A Dual-Branch Framework for Activity-Cliff Ranking Prediction
Sep 1, 2026Kewei Li, Rongying Zhang, Peiyu Yang +4Learning to Rank Pairwise Ranking
College of Computer Science and Technology, Jilin University, Changchun, 130012, China. · Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China. · Greenwich High School, Greenwich, CT, 06830, United States of America. +3
CoMPASS: Collaborative Molecular Property Prediction via Adaptive Small-Large Model Synergy
Aug 31, 2026Wentao Li, Jiangjie Qiu, Yijun Li +2Graph Neural Networks Language Model Calibration
Beijing Key Laboratory of Artificial Intelligence for Advanced Chemical Engineering Materials · State Key Laboratory of Chemical Engineering and Low-Carbon Technology · Department of Chemical Engineering, Tsinghua University
MolLedger: An Additive Graph Neural Network with Chemically Grounded ADME Attributions
Aug 31, 2026Christina X. JiFeature Attribution Graph Neural Networks
Hamilton College
ToxLens: A Reproducible Graph-Learning Framework for Leakage-Aware, Uncertainty-Calibrated Molecular Toxicity Prediction
Aug 31, 2026Magnus H. Strømme, Alex G. C. de Sá, David B. AscherGraph Representation Learning Toxicity Detection
The Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Queensland, 4072, Australia · Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

Physics-Aligned Electronic Ground-State Learning Improves Generalization

Eike S. Eberhard, Xaver Kainz, Viktor Kotsev, Abdulrahman Aldossary+1

Technical University of Munich (TUM) · Munich Data Science Institute (MDSI) · Munich Center for Machine Learning (MCML)+1

Physics-Informed ML OOD Generalization Quantum Chemistry Computational Materials Science Molecular Property Prediction

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Machine-learned interatomic potentials (MLIPs) excel at in-distribution tasks, accelerating drug and material development, yet they struggle to generalize out-of-distribution. We propose to push the cost-accuracy Pareto frontier by designing observable-agnostic electronic ground-state descriptor models (GSMs) with computational costs situated between MLIPs and Kohn-Sham density functional theory (KS-DFT). We align the learning objectives and architectures of GSMs with the governing equations of KS-DFT by enforcing physical constraints and removing optimization pressure on unphysical or irrelevant degrees of freedom. In our size-extrapolation experiments from QM9 to QM40, our combined contributions OrthoNormal-Loss (ON-Loss) and Grassmann Restricted Occupied-Orbital Training (GROOT) reach a 79.1% energy and 83.4% force mean absolute error (MAE) reduction over previous state-of-the-art density GSMs. For Hamiltonian GSMs, ON-Loss and Residual Optimal-gauge Conditioning-aware KS-Eq. Training (ROCKET) together reduce the energy and force MAEs of the strongest baseline by 99.8% and 95.9%, respectively. Using a self-consistency rejection criterion, we filter out extrapolation errors on QMugs, rejecting fewer than 0.4% of predictions while reaching an energy MAE of 0.07 mHa. Finally, we demonstrate the efficiency of label-free self-consistency fine-tuning, and transfer GSMs to reactive chemistry in Transition1x, reaching energy errors below chemical accuracy.

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Latest papers 133