{"total":90,"rows":[{"id":"2211.09085","title":"Galactica: A Large Language Model for Science","created":"2022-11-16","cited_by_count":744,"area":null},{"id":"2209.05481","title":"A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language","created":"2022-09-12","cited_by_count":131,"area":null},{"id":"2107.12375","title":"Geometric Deep Learning on Molecular Representations","created":"2021-07-26","cited_by_count":113,"area":null},{"id":"2407.01603","title":"A Review of Large Language Models and Autonomous Agents in Chemistry","created":"2024-06-26","cited_by_count":99,"area":"agents"},{"id":"2306.06615","title":"Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective","created":"2023-06-11","cited_by_count":92,"area":null},{"id":"2401.14656","title":"Scientific Large Language Models: A Survey on Biological & Chemical Domains","created":"2024-01-26","cited_by_count":80,"area":null},{"id":"2306.13089","title":"GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning","created":"2023-05-28","cited_by_count":70,"area":null},{"id":"2203.14500","title":"MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design","created":"2022-03-28","cited_by_count":66,"area":null},{"id":"2303.11568","title":"Large AI Models in Health Informatics: Applications, Challenges, and the Future","created":"2023-03-21","cited_by_count":61,"area":null},{"id":"2106.10207","title":"Distributed Deep Learning in Open Collaborations","created":"2021-06-18","cited_by_count":58,"area":null},{"id":"2207.08806","title":"Unified 2D and 3D Pre-Training of Molecular Representations","created":"2022-07-14","cited_by_count":45,"area":null},{"id":"2110.01191","title":"Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs","created":"2021-10-04","cited_by_count":44,"area":null},{"id":"2307.07443","title":"Can Large Language Models Empower Molecular Property Prediction?","created":"2023-07-14","cited_by_count":44,"area":null},{"id":"2209.01307","title":"TransPolymer: a Transformer-based language model for polymer property predictions","created":"2022-09-03","cited_by_count":43,"area":null},{"id":"2310.04292","title":"Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets","created":"2023-10-06","cited_by_count":39,"area":null},{"id":"2310.07984","title":"Large Language Models for Scientific Synthesis, Inference and Explanation","created":"2023-10-12","cited_by_count":39,"area":null},{"id":"2109.09888","title":"Chemical-Reaction-Aware Molecule Representation Learning","created":"2021-09-21","cited_by_count":36,"area":null},{"id":"2210.16484","title":"A Systematic Survey of Chemical Pre-trained Models","created":"2022-10-29","cited_by_count":34,"area":null},{"id":"2307.10683","title":"Fractional Denoising for 3D Molecular Pre-training","created":"2023-07-20","cited_by_count":33,"area":null},{"id":"2106.05386","title":"Artificial Intelligence in Drug Discovery: Applications and Techniques","created":"2021-06-09","cited_by_count":28,"area":null},{"id":"2106.10234","title":"Dual-view Molecule Pre-training","created":"2021-06-17","cited_by_count":27,"area":null},{"id":"2304.05341","title":"Bayesian Optimization of Catalysis With In-Context Learning","created":"2023-04-11","cited_by_count":22,"area":null},{"id":"2110.05841","title":"Relative Molecule Self-Attention Transformer","created":"2021-10-12","cited_by_count":21,"area":null},{"id":"2112.04624","title":"Deep Molecular Representation Learning via Fusing Physical and Chemical Information","created":"2021-11-28","cited_by_count":21,"area":null},{"id":"2107.08773","title":"Learning Attributed Graph Representations with Communicative Message Passing Transformer","created":"2021-07-19","cited_by_count":20,"area":null},{"id":"2404.14986","title":"$\\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning","created":"2024-04-23","cited_by_count":20,"area":null},{"id":"2109.07258","title":"Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Setting","created":"2021-09-15","cited_by_count":19,"area":null},{"id":"2206.07048","title":"A smile is all you need: Predicting limiting activity coefficients from SMILES with natural language processing","created":"2022-06-15","cited_by_count":19,"area":null},{"id":"2406.01032","title":"LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning","created":"2024-06-03","cited_by_count":17,"area":"distill"},{"id":"2409.13194","title":"ChemDFM-X: Towards Large Multimodal Model for Chemistry","created":"2024-09-20","cited_by_count":16,"area":null},{"id":"2411.04165","title":"Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences","created":"2024-11-06","cited_by_count":13,"area":null},{"id":"2109.08830","title":"Multilingual Molecular Representation Learning via Contrastive Pre-training","created":"2021-09-18","cited_by_count":12,"area":null},{"id":"2209.04135","title":"SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients","created":"2022-09-09","cited_by_count":11,"area":null},{"id":"2302.07541","title":"Activity Cliff Prediction: Dataset and Benchmark","created":"2023-02-15","cited_by_count":11,"area":null},{"id":"2303.00313","title":"Deep Learning Methods for Small Molecule Drug Discovery: A Survey","created":"2023-03-01","cited_by_count":10,"area":null},{"id":"2310.06399","title":"Lo-Hi: Practical ML Drug Discovery Benchmark","created":"2023-10-10","cited_by_count":10,"area":null},{"id":"2401.03369","title":"Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry","created":"2024-01-07","cited_by_count":10,"area":null},{"id":"2211.14042","title":"Molecular Joint Representation Learning via Multi-modal Information","created":"2022-11-25","cited_by_count":8,"area":null},{"id":"2310.08863","title":"In-Context Learning for Few-Shot Molecular Property Prediction","created":"2023-10-13","cited_by_count":8,"area":null},{"id":"2410.11933","title":"Beyond Sequence: Impact of Geometric Context for RNA Property Prediction","created":"2024-10-15","cited_by_count":8,"area":null},{"id":"2508.03278","title":"Artificial Intelligence and Generative Models for Materials Discovery -- A Review","created":"2025-08-05","cited_by_count":8,"area":null},{"id":"2303.11593","title":"Difficulty in chirality recognition for Transformer architectures learning chemical structures from string","created":"2023-03-21","cited_by_count":7,"area":null},{"id":"2403.12995","title":"ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling","created":"2024-03-05","cited_by_count":7,"area":null},{"id":"2406.13137","title":"Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models","created":"2024-06-19","cited_by_count":7,"area":null},{"id":"2309.17366","title":"3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information","created":"2023-09-28","cited_by_count":6,"area":null},{"id":"2310.14216","title":"UniMAP: Universal SMILES-Graph Representation Learning","created":"2023-10-22","cited_by_count":6,"area":null},{"id":"2407.18897","title":"Small Molecule Optimization with Large Language Models","created":"2024-07-26","cited_by_count":6,"area":null},{"id":"2501.10282","title":"Computational Protein Science in the Era of Large Language Models (LLMs)","created":"2025-01-17","cited_by_count":6,"area":null},{"id":"2211.13979","title":"BatmanNet: Bi-branch Masked Graph Transformer Autoencoder for Molecular Representation","created":"2022-11-25","cited_by_count":4,"area":null},{"id":"2304.12239","title":"Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction","created":"2023-04-24","cited_by_count":4,"area":null},{"id":"2309.16374","title":"MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network","created":"2023-09-28","cited_by_count":4,"area":null},{"id":"2310.02744","title":"SALSA: Semantically-Aware Latent Space Autoencoder","created":"2023-10-04","cited_by_count":4,"area":null},{"id":"2210.13238","title":"Multimodal Model with Text and Drug Embeddings for Adverse Drug Reaction Classification","created":"2022-10-21","cited_by_count":3,"area":null},{"id":"2306.17702","title":"Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?","created":"2023-06-30","cited_by_count":3,"area":null},{"id":"2209.07423","title":"Can Pre-trained Models Really Learn Better Molecular Representations for AI-aided Drug Discovery?","created":"2022-08-21","cited_by_count":2,"area":null},{"id":"2211.02720","title":"Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks","created":"2022-11-04","cited_by_count":2,"area":null},{"id":"2408.09461","title":"Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches","created":"2024-08-18","cited_by_count":2,"area":null},{"id":"2506.01116","title":"ChemAU: Harness the Reasoning of LLMs in Chemical Research with Adaptive Uncertainty Estimation","created":"2025-06-01","cited_by_count":2,"area":"lr"},{"id":"2509.11782","title":"Multimodal Regression for Enzyme Turnover Rates Prediction","created":"2025-09-15","cited_by_count":2,"area":null},{"id":"2512.09757","title":"Circuits, Features, and Heuristics in Molecular Transformers","created":"2025-12-10","cited_by_count":2,"area":"mechinterp"},{"id":"2603.12845","title":"Multimodal Protein Language Models for Enzyme Kinetic Parameters: From Substrate Recognition to Conformational Adaptation","created":"2026-03-13","cited_by_count":2,"area":null},{"id":"2606.11508","title":"Probabilistic Contrastive Pretraining for Multi-task ADME Property Prediction","created":"2026-06-09","cited_by_count":2,"area":null},{"id":"2110.08918","title":"Using Clinical Drug Representations for Improving Mortality and Length of Stay Predictions","created":"2021-10-17","cited_by_count":1,"area":null},{"id":"2209.13492","title":"Unraveling Key Elements Underlying Molecular Property Prediction: A Systematic Study","created":"2022-09-26","cited_by_count":1,"area":null},{"id":"2402.13779","title":"Contextual Molecule Representation Learning from Chemical Reaction Knowledge","created":"2024-02-21","cited_by_count":1,"area":null},{"id":"2407.11439","title":"Repurformer: Transformers for Repurposing-Aware Molecule Generation","created":"2024-07-16","cited_by_count":1,"area":null},{"id":"2501.06608","title":"Dual-Modality Representation Learning for Molecular Property Prediction","created":"2025-01-11","cited_by_count":1,"area":null},{"id":"2509.22302","title":"SoDaDE: Solvent Data-Driven Embeddings with Small Transformer Models","created":"2025-09-26","cited_by_count":1,"area":null},{"id":"2511.16087","title":"AssayMatch: Learning to Select Data for Molecular Activity Models","created":"2025-11-20","cited_by_count":1,"area":null},{"id":"2607.28259","title":"TopoFormer: Topology Meets Attention for Graph Learning","created":"2026-07-30","cited_by_count":1,"area":null},{"id":"2608.03855","title":"Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language","created":"2026-08-04","cited_by_count":1,"area":null},{"id":"2208.07612","title":"Rapid Discovery of Graphene Nanocrystals Using DFT and Bayesian Optimization with Neural Network Kernel","created":"2022-08-16","cited_by_count":0,"area":null},{"id":"2307.13275","title":"CTAGE: Curvature-Based Topology-Aware Graph Embedding for Learning Molecular Representations","created":"2023-07-25","cited_by_count":0,"area":null},{"id":"2402.06964","title":"NLP for Knowledge Discovery and Information Extraction from Energetics Corpora","created":"2024-02-10","cited_by_count":0,"area":null},{"id":"2402.16854","title":"Attention Based Molecule Generation via Hierarchical Variational Autoencoder","created":"2024-01-18","cited_by_count":0,"area":null},{"id":"2405.08334","title":"Could Chemical LLMs benefit from Message Passing","created":"2024-05-14","cited_by_count":0,"area":null},{"id":"2406.06553","title":"Ensemble Model With Bert,Roberta and Xlnet For Molecular property prediction","created":"2024-05-30","cited_by_count":0,"area":null},{"id":"2406.18020","title":"MolFusion: Multimodal Fusion Learning for Molecular Representations via Multi-granularity Views","created":"2024-06-26","cited_by_count":0,"area":null},{"id":"2412.06847","title":"M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery","created":"2024-12-08","cited_by_count":0,"area":null},{"id":"2505.12848","title":"A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design","created":"2025-05-19","cited_by_count":0,"area":null},{"id":"2509.25198","title":"SOLD: SELFIES-based Objective-driven Latent Diffusion","created":"2025-09-03","cited_by_count":0,"area":"diffusion"},{"id":"2511.21566","title":"A decoupled alignment kernel for peptide membrane permeability predictions","created":"2025-11-26","cited_by_count":0,"area":null},{"id":"2512.00521","title":"Rep3Net: An Approach Exploiting Multimodal Representation for Molecular Bioactivity Prediction","created":"2025-11-29","cited_by_count":0,"area":null},{"id":"2601.06289","title":"How well can off-the-shelf LLMs elucidate molecular structures from mass spectra using chain-of-thought reasoning?","created":"2026-01-09","cited_by_count":0,"area":"lr"},{"id":"2601.07261","title":"Pseudodata-guided Invariant Representation Learning Boosts the Out-of-Distribution Generalization in Enzymatic Kinetic Parameter Prediction","created":"2026-01-12","cited_by_count":0,"area":null},{"id":"2602.04696","title":"Beyond Learning on Molecules by Weakly Supervising on Molecules","created":"2026-02-04","cited_by_count":0,"area":null},{"id":"2603.25062","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression","created":"2026-03-26","cited_by_count":0,"area":null},{"id":"2605.06303","title":"Molecules Meet Language: Confound-Aware Representation Learning and Chemical Property Steering in Transformer-VAE Latent Spaces","created":"2026-05-07","cited_by_count":0,"area":"mechinterp"},{"id":"2605.27144","title":"Is an Image Also Worth 16x16=256 Superpixels? A Framework for Attentional Image Classification","created":"2026-05-26","cited_by_count":0,"area":null},{"id":"2607.23607","title":"MS-GPT: Rethinking MS/MS De Novo Structure Elucidation as Spectrum-Induced Posterior Querying of a Molecule-Language Model","created":"2026-07-26","cited_by_count":0,"area":null}],"next_cursor":null}