Papers molecular representation
“molecular representation” 태그가 달린 논문 168편 · 필터 해제
Molecular Machine Learning Using Euler Characteristic Transforms
The shape of a molecule determines its physicochemical and biological properties. However, it is often underrepresented in standard molecular representation learning approaches. Here, we propose using the Euler Character…
molecular representationRepresentation LearningTRIDENT: 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 LearningGeoRecon: 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+3AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery
Accurate molecular property prediction (MPP) is a critical step in modern drug development. However, the scarcity of experimental validation data poses a significant challenge to AI-driven research paradigms. Under few-s…
Drug DiscoveryFew-Shot LearningMolecular Property Predictionmolecular representation+12DNMRGym: An Annotated Experimental Dataset for Atom-Level Molecular Representation Learning in 2D NMR via Surrogate Supervision
Two-dimensional (2D) Nuclear Magnetic Resonance (NMR) spectroscopy, particularly Heteronuclear Single Quantum Coherence (HSQC) spectroscopy, plays a critical role in elucidating molecular structures, interactions, and el…
molecular representationRepresentation LearningPure Component Property Estimation Framework Using Explainable Machine Learning Methods
Accurate prediction of pure component physiochemical properties is crucial for process integration, multiscale modeling, and optimization. In this work, an enhanced framework for pure component property prediction by usi…
molecular representationProperty PredictionMulti-Modal Molecular Representation Learning via Structure Awareness
Accurate extraction of molecular representations is a critical step in the drug discovery process. In recent years, significant progress has been made in molecular representation learning methods, among which multi-modal…
Drug Discoverymolecular representationRepresentation LearningBOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models
Advances in deep learning and generative modeling have driven interest in data-driven molecule discovery pipelines, whereby machine learning (ML) models are used to filter and design novel molecules without requiring pro…
BenchmarkingHyperparameter OptimizationIn-Context LearningInductive Bias+3Synergistic Benefits of Joint Molecule Generation and Property Prediction
Modeling the joint distribution of data samples and their properties allows to construct a single model for both data generation and property prediction, with synergistic benefits reaching beyond purely generative or pre…
Drug Designmolecular representationPredictionProperty Prediction+1MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra
Establishing the relationship between 3D structures and the energy states of molecular systems has proven to be a promising approach for learning 3D molecular representations. However, existing methods are limited to mod…
molecular representationUniMatch: Universal Matching from Atom to Task for Few-Shot Drug Discovery
Drug discovery is crucial for identifying candidate drugs for various diseases.However, its low success rate often results in a scarcity of annotations, posing a few-shot learning problem. Existing methods primarily focu…
Drug DiscoveryFew-Shot LearningMeta-Learningmolecular representationKnowledge-aware contrastive heterogeneous molecular graph learning
Molecular representation learning is pivotal in predicting molecular properties and advancing drug design. Traditional methodologies, which predominantly rely on homogeneous graph encoding, are limited by their inability…
BenchmarkingContrastive LearningDrug DesignGraph Learning+5FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning
Molecular property prediction uses molecular structure to infer chemical properties. Chemically interpretable representations that capture meaningful intramolecular interactions enhance the usability and effectiveness of…
Graph-to-SequenceMolecular Property Predictionmolecular representationProperty Prediction+2MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability
Predicting molecular properties is essential for drug discovery, and computational methods can greatly enhance this process. Molecular graphs have become a focus for representation learning, with Graph Neural Networks (G…
Drug DiscoveryMixture-of-ExpertsMolecular Property Predictionmolecular representation+3Can Molecular Evolution Mechanism Enhance Molecular Representation?
Molecular evolution is the process of simulating the natural evolution of molecules in chemical space to explore potential molecular structures and properties. The relationships between similar molecules are often descri…
Molecular Property Predictionmolecular representationProperty PredictionRepresentation of Molecules via Algebraic Data Types : Advancing Beyond SMILES & SELFIES
We introduce a novel molecular representation through Algebraic Data Types (ADTs) - composite data structures formed through the combination of simpler types that obey algebraic laws. By explicitly considering how the da…
molecular representationProbabilistic ProgrammingGDiffRetro: Retrosynthesis Prediction with Dual Graph Enhanced Molecular Representation and Diffusion Generation
Retrosynthesis prediction focuses on identifying reactants capable of synthesizing a target product. Typically, the retrosynthesis prediction involves two phases: Reaction Center Identification and Reactant Generation. H…
molecular representationRetrosynthesisGenMol: A Drug Discovery Generalist with Discrete Diffusion
Drug discovery is a complex process that involves multiple scenarios and stages, such as fragment-constrained molecule generation, hit generation and lead optimization. However, existing molecular generative models can o…
Computational EfficiencyDrug Discoverymolecular representationMOL-Mamba: Enhancing Molecular Representation with Structural & Electronic Insights
Molecular representation learning plays a crucial role in various downstream tasks, such as molecular property prediction and drug design. To accurately represent molecules, Graph Neural Networks (GNNs) and Graph Transfo…
Drug DesignMambaMolecular Property Predictionmolecular representation+2SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision
SMILES, a crucial textual representation of molecular structures, has garnered significant attention as a foundation for pre-trained language models (LMs). However, most existing pre-trained SMILES LMs focus solely on th…
Language ModelingLanguage Modellingmolecular representationvalid