ArticleACS omega2023
Attention-Based Graph Neural Network for Molecular Solubility Prediction.
Article in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- Can Graph Neural Networks Understand Chemical Elements?Journal of chemical information and modeling · 2026Article
- KAN-PROSPECT: a Kolmogorov-Arnold Networks-integrated framework for predicting the effects and adverse reactions of natural products via transfer learning.Scientific reports · 2026Article
- Predicting SJournal of chemical theory and computation · 2026Article
- ADFC-ATP: Attention-Guided Dual-View Fusion and Contrastive Pretraining for Robust Aquatic Toxicity Prediction.Journal of cellular and molecular medicine · 2026Article
- Artificial intelligence-driven drug discovery: a deep learning paradigm shift in pharmaceutical research and development.Frontiers in pharmacology · 2026Review
- μOR-ligand: target-aware view-based hybrid feature selection for μ-opioid receptor ligand functional classification.Journal of computer-aided molecular design · 2025Article
- Adjusted imbalance ratio leads to effective AI-based drug discovery against infectious disease.Scientific reports · 2025Article
- GraphDeep-hERG: Graph Neural Network PharmacoAnalytics for Assessing hERG-Related Cardiotoxicity.Pharmaceutical research · 2025Article
- A machine learning method for predicting molecular antimicrobial activity.Scientific reports · 2025Article
- Possum: identification and interpretation of potassium ion inhibitors using probabilistic feature vectors.Archives of toxicology · 2025Article
- Multimodal fused deep learning for drug property prediction: Integrating chemical language and molecular graph.Computational and structural biotechnology journal · 2024Article
- Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning.Journal of cheminformatics · 2024Article
- Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor.ACS chemical neuroscience · 2024Article
- Harnessing machine learning to predict cytochrome P450 inhibition through molecular properties.Archives of toxicology · 2024Article
- Machine Learned Classification of Ligand Intrinsic Activities at HumanbioRxiv : the preprint server for biology · 2024Article
- Prediction and Interpretability of Melting Points of Ionic Liquids Using Graph Neural Networks.ACS omega · 2024Article
- Fragment-pair based drug molecule solubility prediction through attention mechanism.Frontiers in pharmacology · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug discovery (DD) research is aimed at the discovery of new medications. Solubility is an important physicochemical property in drug development. Active pharmaceutical ingredients (APIs) are essential substances for high drug efficacy. During DD research, aqueous solubility (AS) is a key physicochemical attribute required for API characterization. High-precision in silico solubility prediction reduces the experimental cost and time of drug development. Several artificial tools have been employed for solubility prediction using machine learning and deep learning techniques. This study aims to create different deep learning models that can predict the solubility of a wide range of molecules using the largest currently available solubility data set. Simplified molecular-input line-entry system (SMILES) strings were used as molecular representation, models developed using simple graph convolution, graph isomorphism network, graph attention network, and AttentiveFP network. Based on the performance of the models, the AttentiveFP-based network model was finally selected. The model was trained and tested on 9943 compounds. The model outperformed on 62 anticancer compounds with metric Pearson correlation
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.