Papers Property Prediction
“Property Prediction” 태그가 달린 논문 691편 · 필터 해제
Heat Kernel Goes Topological
Topological neural networks have emerged as powerful successors of graph neural networks. However, they typically involve higher-order message passing, which incurs significant computational expense. We circumvent this i…
Computational EfficiencyProperty PredictionAcquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures
The ability to transfer knowledge from prior experiences to novel tasks stands as a pivotal capability of intelligent agents, including both humans and computational models. This principle forms the basis of transfer lea…
3D GenerationComputational chemistryMeta-LearningMolecular Property Prediction+4Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model
Recently, a novel two-phase framework named mol-infer for inference of chemical compounds with prescribed abstract structures and desired property values has been proposed. The framework mol-infer is primarily based on u…
Molecular Property PredictionProperty PredictionTRIDENT: Tri-Modal Molecular Representation Learning with Taxonomic Annotations and Local Correspondence
Molecular property prediction aims to learn representations that map chemical structures to functional properties. While multimodal learning has emerged as a powerful paradigm to learn molecular representations, prior wo…
Molecular Property Predictionmolecular representationProperty PredictionRepresentation LearningLarge Language Model Agent for Modular Task Execution in Drug Discovery
We present a modular framework powered by large language models (LLMs) that automates and streamlines key tasks across the early-stage computational drug discovery pipeline. By combining LLM reasoning with domain-specifi…
Drug DiscoveryLanguage ModelingLanguage ModellingLarge Language Model+2A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools
Foundation models (FMs) are catalyzing a transformative shift in materials science (MatSci) by enabling scalable, general-purpose, and multimodal AI systems for scientific discovery. Unlike traditional machine learning m…
Continual LearningDomain GeneralizationLarge Language ModelProperty Prediction+1Pix2Geomodel: A Next-Generation Reservoir Geomodeling with Property-to-Property Translation
Accurate geological modeling is critical for reservoir characterization, yet traditional methods struggle with complex subsurface heterogeneity, and they have problems with conditioning to observed data. This study intro…
Generative Adversarial NetworkProperty PredictionCLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning
The prediction of crystal properties is essential for understanding structure-property relationships and accelerating the discovery of functional materials. However, conventional approaches relying on experimental measur…
Property PredictionRepresentation LearningDescriptor-based Foundation Models for Molecular Property Prediction
Fast and accurate prediction of molecular properties with machine learning is pivotal to scientific advancements across myriad domains. Foundation models in particular have proven especially effective, enabling accurate …
Molecular Property PredictionPredictionProperty PredictionEquivariance Everywhere All At Once: A Recipe for Graph Foundation Models
Graph machine learning architectures are typically tailored to specific tasks on specific datasets, which hinders their broader applicability. This has led to a new quest in graph machine learning: how to build graph fou…
AllNode ClassificationNode Property PredictionProperty PredictionGeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining
The pretraining-and-finetuning paradigm has driven significant advances across domains, such as natural language processing and computer vision, with representative pretraining paradigms such as masked language modeling …
DenoisingLanguage ModelingLanguage ModellingMasked Language Modeling+3Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining
Machine learning has revolutionized materials design, yet predicting complex properties like alloy ductility remains challenging due to the influence of processing conditions and microstructural features that resist quan…
Contrastive LearningLanguage ModelingLanguage ModellingProperty PredictionRobust Molecular Property Prediction via Densifying Scarce Labeled Data
A widely recognized limitation of molecular prediction models is their reliance on structures observed in the training data, resulting in poor generalization to out-of-distribution compounds. Yet in drug discovery, the c…
Drug DiscoveryMeta-LearningMolecular Property PredictionProperty PredictionBreaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?
Toxicity remains a leading cause of early-stage drug development failure. Despite advances in molecular design and property prediction, the task of molecular toxicity repair - generating structurally valid molecular alte…
Property PredictionvalidBioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models
We present BioLangFusion, a simple approach for integrating pre-trained DNA, mRNA, and protein language models into unified molecular representations. Motivated by the central dogma of molecular biology (information flow…
Inductive BiasMolecular Property PredictionMultiple Instance LearningProperty PredictionDualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules
Geometric graph neural networks (GNNs) that respect E(3) symmetries have achieved strong performance on small molecule modeling, but they face scalability and expressiveness challenges when applied to large biomolecules …
Property PredictionThe Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
Machine learning has promised to change the landscape of laboratory chemistry, with impressive results in molecular property prediction and reaction retro-synthesis. However, chemical datasets are often inaccessible to t…
Active LearningBenchmarkingFeature EngineeringMolecular Property Prediction+3Graph Neural Networks in Modern AI-aided Drug Discovery
Graph neural networks (GNNs), as topology/structure-aware models within deep learning, have emerged as powerful tools for AI-aided drug discovery (AIDD). By directly operating on molecular graphs, GNNs offer an intuitive…
Drug Discoverygraph constructionMeta-LearningMolecular Property Prediction+4Positional Encoding meets Persistent Homology on Graphs
The local inductive bias of message-passing graph neural networks (GNNs) hampers their ability to exploit key structural information (e.g., connectivity and cycles). Positional encoding (PE) and Persistent Homology (PH) …
Graph ClassificationGraph Representation LearningInductive BiasOut-of-Distribution Generalization+2Unlocking Chemical Insights: Superior Molecular Representations from Intermediate Encoder Layers
Pretrained molecular encoders have become indispensable in computational chemistry for tasks such as property prediction and molecular generation. However, the standard practice of relying solely on final-layer embedding…
Computational chemistryComputational EfficiencyProperty Prediction