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[Display omitted] • A DFT study of CUR interacting with B 40 and SiB 39 based carriers. • Interaction energy between CUR and B 40 is chemisorption. • B 40 and SiB 39 are electronically sensitive to CUR drug. • Molecular docking results illustrated that CUR/B 40 complexes have a good docking score with COX1, and TNF-alpha receptors. In this paper, density functional theory calculations have been employed to study the interaction of curcumin through β-diketone- and enol- forms with the borospherene molecule (B 40) and its silicon-doped variant (SiB 39) for application as a nanocarrier for designing a drug delivery system. The obtained results from binding energies analysis indicated that the enol form (state II: −1.32 eV) and β-diketone form (state III: −2.51 eV) of curcumin prefers a strong polar covalent B-O and Si-O bonds through carbonyl groups after the interaction with B 40 and SiB 39 fullerenes. The boron fullerene doped with silicon atom improves the adsorption of β-diketone form and has higher binding energy values and increased dipole moment. So, SiB 39 fullerene is more sensitive to the presence of β-diketone (state III: 76.84 %) and enol form is (state IV: 93.68 %) compared to the B 40 fullerene due to the alteration in energy gap. According to molecular docking analysis the β-diketone curcumin conjugated with SiB 39 (state III) and B 40 fullerenes (state I) revealed the best binding affinity with the tumor necrosis factor alpha (TNF-α) receptor (-8.9 kcal/mol) and the cyclooxygenase-1 (COX-1) receptor (-8.8 kcal/mol). The anticancer activities of β-diketone curcumin conjugated B 40 and SiB 39 fullerenes improved in comparison with curumin towards Epidermal growth factor and Human Epidermal Growth Factor Receptor 2 receptors. Molecular docking simulation analysis illustrated that the interaction of curcumin conjugated B 40 and SiB 39 fullerenes have great potential for the anticancer and anti-inflammatory activities in contrast to Curcumin. Thus, the molecular docking and density functional theory analysis confirm that curcumin decorated B 40 and SiB 39 fullerenes have significant potential to be explored further in cancer and inflammation treatments. [ABSTRACT FROM AUTHOR]

• Computational-based estimation of drug solubility in supercritical CO 2. • The used models are Random Forest (RF), KNN and Extra Tree (ET). • The ET model had the best result with a R2 score of 0.9999. In this study, we implemented and compared various non-mechanistic based models for prediction of drug solubility in supercritical solvent. The data were collected from references and the models were built considering various operational circumstances. Small data sets, like the solubility data used in this study, have always been one of the challenges for modeling in machine learning method. In this study, in order to solve the regression problem related to the solubility of drugs, which includes 32 laboratory data, we implemented and studied models that are naturally compatible with very small data like solubility data of drugs in solvents. These models included Random Forest (RF), KNN and Extra Tree (ET). After obtaining the best settings for each model, their final results were compared in terms of accuracy for predicting drug solubility. The ET model had the best result with a score of 0.9999 on the R2 criterion. Random forests with 0.978 and KNN with 0.972 also had acceptable regression results. Finally, the trained model was used to display and evaluate the effect of input parameters like pressure and temperature on drug solubility to understand the process. [ABSTRACT FROM AUTHOR]