ArticleMolecules (Basel, Switzerland)2022
Development of GBRT Model as a Novel and Robust Mathematical Model to Predict and Optimize the Solubility of Decitabine as an Anti-Cancer Drug.
Article in Molecules (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment.Biomedicines · 2025Review
- Modeling and validation of drug release kinetics using hybrid method for prediction of drug efficiency and novel formulations.Frontiers in chemistry · 2024Article
- Simulation and Optimization: A New Direction in Supercritical Technology Based Nanomedicine.Bioengineering (Basel, Switzerland) · 2023Review
- Computational intelligence modeling of hyoscine drug solubility and solvent density in supercritical processing: gradient boosting, extra trees, and random forest models.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
11 authors at 10 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The efficient production of solid-dosage oral formulations using eco-friendly supercritical solvents is known as a breakthrough technology towards developing cost-effective therapeutic drugs. Drug solubility is a significant parameter which must be measured before designing the process. Decitabine belongs to the antimetabolite class of chemotherapy agents applied for the treatment of patients with myelodysplastic syndrome (MDS). In recent years, the prediction of drug solubility by applying mathematical models through artificial intelligence (AI) has become known as an interesting topic due to the high cost of experimental investigations. The purpose of this study is to develop various machine-learning-based models to estimate the optimum solubility of the anti-cancer drug decitabine, to evaluate the effects of pressure and temperature on it. To make models on a small dataset in this research, we used three ensemble methods, Random Forest (RFR), Extra Tree (ETR), and Gradient Boosted Regression Trees (GBRT). Different configurations were tested, and optimal hyper-parameters were found. Then, the final models were assessed using standard metrics. RFR, ETR, and GBRT had R2 scores of 0.925, 0.999, and 0.999, respectively. Furthermore, the MAPE metric error rates were 1.423 × 10
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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.