ArticleNature communications2021
Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Rational High-Throughput Screening of Cocrystals: An Explainable Deep Learning Framework Based on Morgan Fingerprints and Cross-Attention.ACS physical chemistry Au · 2026Article
- Machine Learning Accelerates Crystallization for Structure Determination.Angewandte Chemie (International ed. in English) · 2026Article
- Challenges and Opportunities in Machine Learning for Light-Emitting Polymers.Macromolecular rapid communications · 2026Review
- Explainable Machine Learning for Efficient Cocrystal Prediction: Leveraging Morgan Fingerprints to Decode Local Structure-Cocrystal Formation Relationships.The journal of physical chemistry. A · 2026Article
- Crystal engineering for poorly water-soluble drugs: From design to applications.Acta pharmaceutica Sinica. B · 2026Review
- Data-Driven Discovery of Polar Organic Cocrystals: Integration of Machine Learning and Automated Screening.Journal of the American Chemical Society · 2025Article
- Hydrogen bond strength for riparins (I-III) cocrystals with carboxylic acid group coformers: ESP, QTAIM, and NBO analytical screening.Journal of molecular modeling · 2025Article
- A Combined Experimental and Modeling Workflow to Tune Surface Properties of Organic Materials via Cocrystallization.Chemistry of materials : a publication of the American Chemical Society · 2025Article
- The Structural Regulation and Properties of Energetic Materials: A Review.Nanomaterials (Basel, Switzerland) · 2025Review
- Deep Supramolecular Language Processing for Co-Crystal Prediction.Angewandte Chemie (International ed. in English) · 2025Article
- High-throughput encapsulated nanodroplet screening for accelerated co-crystal discovery.Chemical science · 2025Article
- Integrative residue-intuitive machine learning and MD Approach to Unveil Allosteric Site and Mechanism for β2AR.Nature communications · 2024Article
- Cocrystal Prediction of Nifedipine Based on the Graph Neural Network and Molecular Electrostatic Potential Surface.AAPS PharmSciTech · 2024Article
- Graph Neural Networks with Multi-features for Predicting Cocrystals using APIs and Coformers Interactions.Current medicinal chemistry · 2024Article
- Graph Comparison of Molecular Crystals in Band Gap Prediction Using Neural Networks.ACS omega · 2023Article
- Recent Advances in Co-Former Screening and Formation Prediction of Multicomponent Solid Forms of Low Molecular Weight Drugs.Pharmaceutics · 2023Review
- Exploring the Supramolecular Interactions and Thermal Stability of Dapsone:Bipyridine Cocrystals by Combining Computational Chemistry with Experimentation.Crystal growth & design · 2023Article
- Virtual Screening, Structural Analysis, and Formation Thermodynamics of Carbamazepine Cocrystals.Pharmaceutics · 2023Article
- Prediction and Construction of Energetic Materials Based on Machine Learning Methods.Molecules (Basel, Switzerland) · 2022Review
- Cocrystal Prediction of Bexarotene by Graph Convolution Network and Bioavailability Improvement.Pharmaceutics · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cocrystal engineering have been widely applied in pharmaceutical, chemistry and material fields. However, how to effectively choose coformer has been a challenging task on experiments. Here we develop a graph neural network (GNN) based deep learning framework to quickly predict formation of the cocrystal. In order to capture main driving force to crystallization from 6819 positive and 1052 negative samples reported by experiments, a feasible GNN framework is explored to integrate important prior knowledge into end-to-end learning on the molecular graph. The model is strongly validated against seven competitive models and three challenging independent test sets involving pharmaceutical cocrystals, π-π cocrystals and energetic cocrystals, exhibiting superior performance with accuracy higher than 96%, confirming its robustness and generalization. Furthermore, one new energetic cocrystal predicted is successfully synthesized, showcasing high potential of the model in practice. All the data and source codes are available at https://github.com/Saoge123/ccgnet for aiding cocrystal community.
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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.