{"list":"other","total":66,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2303.08774","title":"GPT-4 Technical Report","created":"2023-03-15","cited_by_count":18396},{"id":"2302.13971","title":"LLaMA: Open and Efficient Foundation Language Models","created":"2023-02-27","cited_by_count":14132},{"id":"2204.02311","title":"PaLM: Scaling Language Modeling with Pathways","created":"2022-04-05","cited_by_count":5326},{"id":"2312.00752","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","created":"2023-12-01","cited_by_count":4718},{"id":"2303.18223","title":"A Survey of Large Language Models","created":"2023-03-31","cited_by_count":2576},{"id":"2111.00396","title":"Efficiently Modeling Long Sequences with Structured State Spaces","created":"2021-10-31","cited_by_count":2465},{"id":"1805.04833","title":"Hierarchical Neural Story Generation","created":"2018-05-13","cited_by_count":1193},{"id":"2102.09844","title":"E(n) Equivariant Graph Neural Networks","created":"2021-02-19","cited_by_count":965},{"id":"2203.17003","title":"Equivariant Diffusion for Molecule Generation in 3D","created":"2022-03-31","cited_by_count":677},{"id":"2006.10503","title":"SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks","created":"2020-06-18","cited_by_count":607},{"id":"2210.10341","title":"BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining","created":"2022-10-19","cited_by_count":538},{"id":"2304.15004","title":"Are Emergent Abilities of Large Language Models a Mirage?","created":"2023-04-28","cited_by_count":523},{"id":"2203.02923","title":"GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation","created":"2022-03-06","cited_by_count":516},{"id":"2206.11990","title":"Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs","created":"2022-06-23","cited_by_count":264},{"id":"1906.04015","title":"Cormorant: Covariant Molecular Neural Networks","created":"2019-06-06","cited_by_count":263},{"id":"2206.01729","title":"Torsional Diffusion for Molecular Conformer Generation","created":"2022-06-01","cited_by_count":263},{"id":"2006.05531","title":"GEOM: Energy-annotated molecular conformations for property prediction and molecular generation","created":"2020-06-09","cited_by_count":243},{"id":"2305.01140","title":"Geometric Latent Diffusion Models for 3D Molecule Generation","created":"2023-05-02","cited_by_count":186},{"id":"2202.02541","title":"TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials","created":"2022-02-05","cited_by_count":182},{"id":"2204.11817","title":"Translation between Molecules and Natural Language","created":"2022-04-25","cited_by_count":177},{"id":"1906.00957","title":"Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules","created":"2019-06-02","cited_by_count":172},{"id":"2102.05013","title":"Spherical Message Passing for 3D Graph Networks","created":"2021-02-09","cited_by_count":168},{"id":"2212.10789","title":"Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing","created":"2022-12-21","cited_by_count":166},{"id":"2105.09016","title":"E(n) Equivariant Normalizing Flows","created":"2021-05-19","cited_by_count":165},{"id":"2105.03902","title":"Learning Gradient Fields for Molecular Conformation Generation","created":"2021-05-09","cited_by_count":163},{"id":"1707.02633","title":"Controlling Linguistic Style Aspects in Neural Language Generation","created":"2017-07-09","cited_by_count":149},{"id":"2307.08423","title":"Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems","created":"2023-07-17","cited_by_count":121},{"id":"2106.07802","title":"GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles","created":"2021-06-08","cited_by_count":107},{"id":"2209.00865","title":"Diffusion-based Molecule Generation with Informative Prior Bridges","created":"2022-09-02","cited_by_count":106},{"id":"2310.07276","title":"BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations","created":"2023-10-11","cited_by_count":100},{"id":"2302.09048","title":"MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation","created":"2023-02-17","cited_by_count":98},{"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},{"id":"1904.00314","title":"Molecular geometry prediction using a deep generative graph neural network","created":"2019-03-31","cited_by_count":91},{"id":"2102.10240","title":"Learning Neural Generative Dynamics for Molecular Conformation Generation","created":"2021-02-20","cited_by_count":88},{"id":"2311.16208","title":"InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery","created":"2023-11-27","cited_by_count":86},{"id":"2305.10688","title":"MolXPT: Wrapping Molecules with Text for Generative Pre-training","created":"2023-05-18","cited_by_count":81},{"id":"2401.14656","title":"Scientific Large Language Models: A Survey on Biological & Chemical Domains","created":"2024-01-26","cited_by_count":80},{"id":"2210.01765","title":"One Transformer Can Understand Both 2D & 3D Molecular Data","created":"2022-10-04","cited_by_count":77},{"id":"1909.11459","title":"A Generative Model for Molecular Distance Geometry","created":"2019-09-25","cited_by_count":73},{"id":"2209.15408","title":"Equivariant Energy-Guided SDE for Inverse Molecular Design","created":"2022-09-30","cited_by_count":73},{"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},{"id":"2206.08515","title":"ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs","created":"2022-06-17","cited_by_count":69},{"id":"2203.14500","title":"MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design","created":"2022-03-28","cited_by_count":66},{"id":"2203.04810","title":"Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets","created":"2022-03-09","cited_by_count":64},{"id":"2105.07246","title":"An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming","created":"2021-05-15","cited_by_count":59},{"id":"2305.05708","title":"Language models can generate molecules, materials, and protein binding sites directly in three dimensions as XYZ, CIF, and PDB files","created":"2023-05-09","cited_by_count":59},{"id":"2309.03907","title":"DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs","created":"2023-05-18","cited_by_count":40},{"id":"2210.16484","title":"A Systematic Survey of Chemical Pre-trained Models","created":"2022-10-29","cited_by_count":34},{"id":"2110.01717","title":"Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs","created":"2021-09-30","cited_by_count":31},{"id":"2304.02213","title":"Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT","created":"2023-04-05","cited_by_count":30},{"id":"2212.02229","title":"GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction","created":"2022-11-18","cited_by_count":28},{"id":"2305.12347","title":"Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation","created":"2023-05-21","cited_by_count":27},{"id":"2310.03320","title":"BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs","created":"2023-10-05","cited_by_count":25},{"id":"2305.13266","title":"Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D","created":"2023-05-05","cited_by_count":24},{"id":"1905.13741","title":"Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation","created":"2019-05-31","cited_by_count":23},{"id":"1910.03131","title":"Generating valid Euclidean distance matrices","created":"2019-10-07","cited_by_count":19},{"id":"2006.07078","title":"TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search","created":"2020-06-12","cited_by_count":15},{"id":"2310.06083","title":"Transformers and Large Language Models for Chemistry and Drug Discovery","created":"2023-10-09","cited_by_count":13},{"id":"2309.05203","title":"From Artificially Real to Real: Leveraging Pseudo Data from Large Language Models for Low-Resource Molecule Discovery","created":"2023-09-11","cited_by_count":11},{"id":"2309.05853","title":"ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation","created":"2023-09-11","cited_by_count":8},{"id":"2309.16235","title":"Language models in molecular discovery","created":"2023-09-28","cited_by_count":6},{"id":"2302.07868","title":"Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks","created":"2023-02-15","cited_by_count":5},{"id":"2310.02066","title":"De Novo Drug Design with Joint Transformers","created":"2023-10-03","cited_by_count":2}],"next_cursor":null}