Papers Molecule Captioning
“Molecule Captioning” 태그가 달린 논문 29편 · 필터 해제
BiMol-Diff: A Unified Diffusion Framework for Molecular Generation and Captioning
Bridging molecular structures and natural language is essential for controllable design. Autoregressive models struggle with long-range dependencies, while standard diffusion processes apply uniform corruption across pos…
Molecule CaptioningLanguage ModellingFactual and Edit-Sensitive Graph-to-Sequence Generation via Graph-Aware Adaptive Noising
Fine-tuned autoregressive models for graph-to-sequence generation (G2S) often struggle with factual grounding and edit sensitivity. To tackle these issues, we propose a non-autoregressive diffusion framework that generat…
Molecule CaptioningImproving Large Molecular Language Model via Relation-aware Multimodal Collaboration
Large language models (LLMs) have demonstrated their instruction-following capabilities and achieved powerful performance on various tasks. Inspired by their success, recent works in the molecular domain have led to the …
Molecule CaptioningCROP: Integrating Topological and Spatial Structures via Cross-View Prefixes for Molecular LLMs
Recent advances in molecular science have been propelled significantly by large language models (LLMs). However, their effectiveness is limited when relying solely on molecular sequences, which fail to capture the comple…
Molecule CaptioningXMolCap: Advancing Molecular Captioning through Multimodal Fusion and Explainable Graph Neural Networks
Large language models (LLMs) have significantly advanced computational biology by enabling the integration of molecular, protein, and natural language data to accelerate drug discovery. However, existing molecular captio…
Drug DiscoveryMolecule CaptioningAutomatic Annotation Augmentation Boosts Translation between Molecules and Natural Language
Recent advancements in AI for biological research focus on integrating molecular data with natural language to accelerate drug discovery. However, the scarcity of high-quality annotations limits progress in this area. Th…
Drug DiscoveryMolecule CaptioningSentenceText-based de novo Molecule GenerationMol-LLM: Multimodal Generalist Molecular LLM with Improved Graph Utilization
Recent advances in large language models (LLMs) have led to models that tackle diverse molecular tasks, such as chemical reaction prediction and molecular property prediction. Large-scale molecular instruction-tuning dat…
Chemical Reaction PredictionMolecular Property PredictionMolecule CaptioningPrediction+1Property Enhanced Instruction Tuning for Multi-task Molecule Generation with Large Language Models
Large language models (LLMs) are widely applied in various natural language processing tasks such as question answering and machine translation. However, due to the lack of labeled data and the difficulty of manual annot…
Machine TranslationMolecular Property PredictionMolecule CaptioningProperty Prediction+1MolReFlect: Towards Fine-grained In-Context Alignment between Molecules and Texts
Molecule discovery is a pivotal research field, impacting everything from the medicines we take to the materials we use. Recently, Large Language Models (LLMs) have been widely adopted in molecule understanding and gener…
DescriptiveMolecule CaptioningText-based de novo Molecule GenerationGeomCLIP: Contrastive Geometry-Text Pre-training for Molecules
Pretraining molecular representations is crucial for drug and material discovery. Recent methods focus on learning representations from geometric structures, effectively capturing 3D position information. Yet, they overl…
DenoisingMolecular Property PredictionMolecule CaptioningProperty Prediction+1Vector-ICL: In-context Learning with Continuous Vector Representations
Large language models (LLMs) have shown remarkable in-context learning (ICL) capabilities on textual data. We explore whether these capabilities can be extended to continuous vectors from diverse domains, obtained from b…
ClassificationGraph ClassificationIn-Context LearningLanguage Modeling+6Mol2Lang-VLM: Vision- and Text-Guided Generative Pre-trained Language Models for Advancing Molecule Captioning through Multimodal Fusion
This paper introduces Mol2Lang-VLM, an enhanced method for refining generative pre-trained language models for molecule captioning using multimodal features to achieve more accurate caption generation. Our approach lever…
Caption GenerationDecoderMolecule Captioning3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text Modeling
The integration of molecular and natural language representations has emerged as a focal point in molecular science, with recent advancements in Language Models (LMs) demonstrating significant potential for comprehensive…
Molecular Property PredictionMolecule CaptioningProperty PredictionReactXT: Understanding Molecular "Reaction-ship" via Reaction-Contextualized Molecule-Text Pretraining
Molecule-text modeling, which aims to facilitate molecule-relevant tasks with a textual interface and textual knowledge, is an emerging research direction. Beyond single molecules, studying reaction-text modeling holds p…
Molecule CaptioningPredictionRetrosynthesisAtomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation
Molecule-and-text cross-modal representation learning has emerged as a promising direction for enhancing the quality of molecular representation, thereby improving performance in various scientific fields. However, most …
Drug Discoverymolecular representationMolecule CaptioningRepresentation LearningBioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning
Recent research trends in computational biology have increasingly focused on integrating text and bio-entity modeling, especially in the context of molecules and proteins. However, previous efforts like BioT5 faced chall…
Drug DiscoveryForward reaction predictionMolecule CaptioningReagent Prediction+2Towards 3D Molecule-Text Interpretation in Language Models
Language Models (LMs) have greatly influenced diverse domains. However, their inherent limitation in comprehending 3D molecular structures has considerably constrained their potential in the biomolecular domain. To bridg…
Instruction FollowingLanguage ModelingLanguage ModellingMolecule Captioning+2InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery
The rapid evolution of artificial intelligence in drug discovery encounters challenges with generalization and extensive training, yet Large Language Models (LLMs) offer promise in reshaping interactions with complex mol…
Drug DiscoveryMolecule CaptioningMolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter
Language Models (LMs) have demonstrated impressive molecule understanding ability on various 1D text-related tasks. However, they inherently lack 2D graph perception - a critical ability of human professionals in compreh…
Contrastive LearningIUPAC Name PredictionLanguage ModelingLanguage Modelling+4BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery. However, current models exhibit several limitations, such as the generation of i…
Drug DiscoveryMolecule CaptioningText-based de novo Molecule Generation