GDMol: Generative Double‐Masking Self‐Supervised Learning for Molecular Property Prediction
Sign Up to like & getrecommendations! Published in 2024 at "Molecular Informatics"
DOI: 10.1002/minf.202400146
Abstract: Background: Effective molecular feature representation is crucial for drug property prediction. Recent years have seen increased attention on graph neural networks (GNNs) that are pre‐trained using self‐supervised learning techniques, aiming to overcome the scarcity of… read more here.
Keywords: self supervised; prediction; property prediction; molecular property ... See more keywords
Graph-Aware AURALSTM: An Attentive Unified Representation Architecture with BiLSTM for Enhanced Molecular Property Prediction.
Sign Up to like & getrecommendations! Published in 2025 at "Molecular diversity"
DOI: 10.1007/s11030-025-11197-4
Abstract: Predicting molecular properties with high accuracy is essential across scientific fields, from drug discovery and biotechnology to materials science and environmental research. In biomedical sciences, accurate molecular property prediction is crucial for elucidating disease mechanisms,… read more here.
Keywords: graph aware; architecture; aura lstm; attentive unified ... See more keywords
ResGAT: Residual Graph Attention Networks for molecular property prediction
Sign Up to like & getrecommendations! Published in 2024 at "Memetic Computing"
DOI: 10.1007/s12293-024-00423-5
Abstract: Molecular property prediction is an important step in the drug discovery pipeline. Numerous computational methods have been developed to predict a wide range of molecular properties. While recent approaches have shown promising results, no single… read more here.
Keywords: graph attention; attention networks; property prediction; graph ... See more keywords
Molecular Property Prediction and Molecular Design Using a Supervised Grammar Variational Autoencoder
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.1c01573
Abstract: Some of the most common applications of machine learning (ML) algorithms dealing with small molecules usually fall within two distinct domains, namely, the prediction of molecular properties and the design of novel molecules with some… read more here.
Keywords: prediction molecular; molecular properties; variational autoencoder; grammar variational ... See more keywords
Roughness of Molecular Property Landscapes and Its Impact on Modellability
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DOI: 10.1021/acs.jcim.2c00903
Abstract: In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space. The roughness (or smoothness) of these molecular property landscapes is one… read more here.
Keywords: property landscapes; landscapes impact; roughness molecular; property ... See more keywords
KnoMol: A Knowledge-Enhanced Graph Transformer for Molecular Property Prediction
Sign Up to like & getrecommendations! Published in 2024 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.4c01092
Abstract: Molecular property prediction (MPP) techniques are pivotal in reducing drug development costs by preemptively predicting bioactivity and ADMET properties. Despite the application of numerous deep learning approaches, enhancing the representational capacity of these models remains… read more here.
Keywords: transformer; property prediction; knowledge; molecular property ... See more keywords
Molecular Motif Learning as a pretraining objective for molecular property prediction.
Sign Up to like & getrecommendations! Published in 2025 at "Nature communications"
DOI: 10.1038/s41467-025-66685-w
Abstract: Molecular property prediction is crucial for drug discovery in biopharmaceuticals since it helps identify promising compounds, optimizing the efficacy of developing new therapies. Despite its importance, existing deep learning-based methods for this task are often… read more here.
Keywords: molecular motif; motif learning; prediction; property prediction ... See more keywords
MoleculeFormer is a GCN-transformer architecture for molecular property prediction
Sign Up to like & getrecommendations! Published in 2025 at "Communications Biology"
DOI: 10.1038/s42003-025-09064-x
Abstract: Artificial intelligence is increasingly important in drug discovery, particularly in molecular property prediction. Graph Neural Networks can model molecular structures as graphs, using structural data to predict molecular properties and biological activities effectively. However, molecular… read more here.
Keywords: transformer architecture; prediction; transformer; property prediction ... See more keywords
Fast and effective molecular property prediction with transferability map
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DOI: 10.1038/s42004-024-01169-4
Abstract: Effective transfer learning for molecular property prediction has shown considerable strength in addressing insufficient labeled molecules. Many existing methods either disregard the quantitative relationship between source and target properties, risking negative transfer, or require intensive… read more here.
Keywords: transfer learning; property; transferability; property prediction ... See more keywords
Molecular property prediction in the ultra‐low data regime
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DOI: 10.1038/s42004-025-01592-1
Abstract: Data scarcity remains a major obstacle to effective machine learning in molecular property prediction and design, affecting diverse domains such as pharmaceuticals, solvents, polymers, and energy carriers. Although multi-task learning (MTL) can leverage correlations among… read more here.
Keywords: property; property prediction; prediction ultra; low data ... See more keywords
Knowledge-based BERT: a method to extract molecular features like computational chemists
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac131
Abstract: Molecular property prediction models based on machine learning algorithms have become important tools to triage unpromising lead molecules in the early stages of drug discovery. Compared with the mainstream descriptor- and graph-based methods for molecular… read more here.
Keywords: extract molecular; method; molecular features; molecular property ... See more keywords