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   Articles with "molecular representation" as a keyword



Conformational Space Profiling Enhances Generic Molecular Representation for AI‐Powered Ligand‐Based Drug Discovery

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recommendations! Published in 2024 at "Advanced Science"

DOI: 10.1002/advs.202403998

Abstract: The molecular representation model is a neural network that converts molecular representations (SMILES, Graph) into feature vectors, and is an essential module applied across a wide range of artificial intelligence‐driven drug discovery scenarios. However, current… read more here.

Keywords: molecular representation; drug discovery; conformational space; space ... See more keywords
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Catalogue of Plausible Molecular Models for the Molecular Dynamics of Asphaltenes and Resins Obtained from Quantitative Molecular Representation

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recommendations! Published in 2019 at "Energy & Fuels"

DOI: 10.1021/acs.energyfuels.9b02605

Abstract: Computer simulation studies aimed at elucidating the phase behavior of crude oils inevitably require atomistically detailed models of representative molecules. For the lighter fractions of crudes, such molecules are readily available, as the chemical composition… read more here.

Keywords: molecular dynamics; molecular representation; plausible molecular; catalogue plausible ... See more keywords

FunQG: Molecular Representation Learning Via Quotient Graphs

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recommendations! Published in 2022 at "Journal of chemical information and modeling"

DOI: 10.1021/acs.jcim.3c00445

Abstract: To accurately predict molecular properties, it is important to learn expressive molecular representations. Graph neural networks (GNNs) have made significant advances in this area, but they often face limitations like neighbors-explosion, under-reaching, oversmoothing, and oversquashing.… read more here.

Keywords: quotient graphs; representation learning; quotient; molecular representation ... See more keywords

Fuzz Testing Molecular Representation Using Deep Variational Anomaly Generation

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recommendations! Published in 2025 at "Journal of Chemical Information and Modeling"

DOI: 10.1021/acs.jcim.4c01876

Abstract: Researchers are developing increasingly robust molecular representations, motivating the need for thorough methods to stress-test and validate them. Here, we use a variational auto-encoder (VAE), an unsupervised deep learning model, to generate anomalous examples of… read more here.

Keywords: anomaly generation; molecular representation; representation; fuzz testing ... See more keywords

Exploring BERT for Reaction Yield Prediction: Evaluating the Impact of Tokenization, Molecular Representation, and Pretraining Data Augmentation

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recommendations! Published in 2025 at "Journal of chemical information and modeling"

DOI: 10.1021/acs.jcim.5c00359

Abstract: Predicting reaction yields in synthetic chemistry remains a significant challenge. This study systematically evaluates the impact of tokenization, molecular representation, pretraining data, and adversarial training on a BERT-based model for yield prediction of Buchwald-Hartwig and… read more here.

Keywords: reaction; pretraining data; chemistry; yield prediction ... See more keywords

Unified and explainable molecular representation learning for imperfectly annotated data from the hypergraph view

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recommendations! Published in 2025 at "Nature Communications"

DOI: 10.1038/s41467-025-63730-6

Abstract: Molecular representation learning (MRL) has shown promise in accelerating drug development by predicting chemical properties. However, imperfectly annotation among datasets pose challenges in model design and explainability. In this work, we formulate molecules and corresponding… read more here.

Keywords: explainable molecular; representation learning; learning imperfectly; molecular representation ... See more keywords

Curvature-augmented graph neural networks for molecular representation learning

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recommendations! Published in 2025 at "Physica Scripta"

DOI: 10.1088/1402-4896/ae06d4

Abstract: Graph Neural Networks (GNNs) have become the dominant paradigm for molecular representation learning. However, traditional GNNs have their representation capacities fundamentally limited by the 1-dimensional Weisfeiler-Lehman (1-WL) test, preventing them from distinguishing structurally different molecules.… read more here.

Keywords: graph neural; representation learning; molecular representation; neural networks ... See more keywords

ComABAN: refining molecular representation with the graph attention mechanism to accelerate drug discovery

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recommendations! Published in 2022 at "Briefings in bioinformatics"

DOI: 10.1093/bib/bbac350

Abstract: An unsolved challenge in developing molecular representation is determining an optimal method to characterize the molecular structure. Comprehension of intramolecular interactions is paramount toward achieving this goal. In this study, ComABAN, a new graph-attention-based approach,… read more here.

Keywords: graph attention; molecular representation; representation; drug discovery ... See more keywords

Consistent semantic representation learning for out-of-distribution molecular property prediction

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recommendations! Published in 2025 at "Briefings in Bioinformatics"

DOI: 10.1093/bib/bbaf147

Abstract: Abstract Invariant molecular representation models provide potential solutions to guarantee accurate prediction of molecular properties under distribution shifts out-of-distribution (OOD) by identifying and leveraging invariant substructures inherent to the molecules. However, due to the complex… read more here.

Keywords: consistent semantic; representation; property prediction; molecular representation ... See more keywords

BFGTP: A BERT-Guided Two-Stage Molecular Representation Learning Framework for Toxicity Prediction

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recommendations! Published in 2025 at "IEEE Journal of Biomedical and Health Informatics"

DOI: 10.1109/jbhi.2025.3556766

Abstract: Accurate prediction of molecular toxicity is vital for drug development. Most mainstream methods rely on fingerprints or graph-based feature extraction, the emergence of large language models (LLMs) offers new prospects for molecular representation learning in… read more here.

Keywords: toxicity prediction; representation learning; toxicity; prediction ... See more keywords

A Novel Molecular Representation Learning for Molecular Property Prediction with a Multiple SMILES-Based Augmentation

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recommendations! Published in 2022 at "Computational Intelligence and Neuroscience"

DOI: 10.1155/2022/8464452

Abstract: Deep learning has brought a rapid development in the aspect of molecular representation for various tasks, such as molecular property prediction. The prediction of molecular properties is a crucial task in the field of drug… read more here.

Keywords: property prediction; molecular representation; prediction; multiple smiles ... See more keywords