ArticleActa pharmaceutica Sinica. B2019
Predicting complexation performance between cyclodextrins and guest molecules by integrated machine learning and molecular modeling techniques.
Article in Acta pharmaceutica Sinica. B, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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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
31 citing papers in PubMed.
- Toward an AI Era: Application of Artificial Intelligence in Inclusion Complex Screening.Pharmaceutics · 2026Review
- Artificial intelligence for drug delivery: Yesterday, today and tomorrow.Acta pharmaceutica Sinica. B · 2026Review
- Structurally Modified Cyclodextrins in Topical Nanotechnology: Advancing Barrier-Penetrating Drug Delivery Strategies for Psoriasis Management.AAPS PharmSciTech · 2026Review
- Association of multi-site microbial features with malignancy risk in pulmonary ground-glass nodules and identification of predictive biomarkers: a prospective multicenter cohort study.Journal of translational medicine · 2025Article
- Formation and stabilization mechanism of heterocyclic aroma compounds / β-CD/CS microcapsules.Food chemistry: X · 2025Article
- Use of Computational Intelligence in Customizing Drug Release from 3D-Printed Products: A Comprehensive Review.Pharmaceutics · 2025Review
- Design and application of human-computer interaction visual communication platform for Guandong culture by integrating RF and light GBM algorithm.Scientific reports · 2025Article
- Computer-driven formulation development of Ginsenoside Rh2 ternary solid dispersion.Drug delivery and translational research · 2025Article
- Artificial intelligence-driven rational design of ionizable lipids for mRNA delivery.Nature communications · 2024Article
- A Review of Machine Learning and QSAR/QSPR Predictions for Complexes of Organic Molecules with Cyclodextrins.Molecules (Basel, Switzerland) · 2024Review
- Gut microbiome features and metabolites in non-alcoholic fatty liver disease among community-dwelling middle-aged and older adults.BMC medicine · 2024Article
- Recent advancements toward the incremsent of drug solubility using environmentally-friendly supercritical COFrontiers in medicine · 2024Review
- FormulationAI: a novel web-based platform for drug formulation design driven by artificial intelligence.Briefings in bioinformatics · 2023Article
- Deep Learning for Identifying Promising Drug Candidates in Drug-Phospholipid Complexes.Molecules (Basel, Switzerland) · 2023Article
- A Review on Cyclodextrins/Estrogens Inclusion Complexes.International journal of molecular sciences · 2023Review
- Prediction of ADMET Properties of Anti-Breast Cancer Compounds Using Three Machine Learning Algorithms.Molecules (Basel, Switzerland) · 2023Article
- Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?Nano today · 2022Article
- Article
- Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm.Acta pharmaceutica Sinica. B · 2022Article
- Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms.Journal of cheminformatics · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most pharmaceutical formulation developments are complex and ideal formulations are generally obtained after extensive experimentation. Machine learning is increasingly advancing many aspects in modern society and has achieved significant success in multiple subjects. Current research demonstrated that machine learning can be adopted to build up high-accurate predictive models in drugs/cyclodextrins (CDs) systems. Molecular descriptors of compounds and experimental conditions were employed as inputs, while complexation free energy as outputs. Results showed that the light gradient boosting machine provided significantly improved predictive performance over random forest and deep learning. The mean absolute error was 1.38 kJ/mol and squared correlation coefficient was 0.86. The evaluation of relative importance of molecular descriptors further demonstrated the key factors affecting molecular interactions in drugs/CD systems. In the specific ketoprofen-CD systems, machine learning model showed better predictive performance than molecular modeling calculation, while molecular simulation could provide structural, dynamic and energetic information. The integration of machine learning and molecular simulation could produce synergistic effect for interpreting and predicting pharmaceutical formulations. In conclusion, the developed predictive models were able to quickly and accurately predict the solubilizing capacity of CD systems. Current research has taken an important step toward the application of machine learning in pharmaceutical formulation design.
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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.