Solvation effects on the reaction mechanism of the title reactions have been kinetically evaluated in 21 conventional solvents and 17 ionic liquids. Solvent polarity affects the catalyzed and non-catalyzed S_NAr pathways differently. The ambiphilic character of water and formamide, which act as a hydrogen bond donor/acceptor, induces nucleophilic activation at the nitrogen center of the nucleophile. The ionic liquid EMIMDCN appears to be the best solvent for the S_NAr route probably due to the high polarizability of the dicyanamide anion.

We herein report results obtained from an integrated experimental and theoretical study on aromatic nucleophilic substitution (S_NAr) reactions of a series of amines towards 1-fluoro-2,4-dinitrobenzene in water. Specific nucleophile–electrophile interactions in the title reactions have been kinetically evaluated. The whole series undergoes S_NAr reactions where the formation of the Meisenheimer complex is rate determining. Theoretical studies concerning specific interactions are discussed in detail. It is found that H-bonding effects along the intrinsic reaction coordinate profile promote the activation of both the electrophile and the nucleophile. Using these results, it is possible to establish a hierarchy of reactivity that is in agreement with the experimental data. Second order energy perturbation energy analysis highlights the strong interaction between the ortho-nitro group and the acidic hydrogen atom of the amine. The present study strongly suggests that any theoretical analysis must be performed at the activated transition state structure, because the static model developed around the reactant states hides most of the relevant specific interactions that characterize the aromatic substitution process.

Understanding the non-covalent interactions in host-guest complexes is crucial to their stability, design and applications. Here, we use density functional theory to compare the ability of β-cyclodextrin (β-CD) and heptakis(2,6-di-O-methyl)-β-cyclodextrin (DM-β-CD) to encapsulate the model guest phenol. For both macrocycles, we quantify the intramolecular interactions before and after the formation of the complex, as well as the intermolecular host-guest and host-host dimer interactions. These are individually classified as van der Waals interactions or hydrogen bonds, respectively. The results show a stronger intramolecular binding energy of β-CD, with the absolute difference being −5.53 kcal/mol relative to DM-β-CD. Consequently, the intermolecular interactions of both cyclodextrins with phenol are affected, such that the free binding energy calculated for the DM-β-CD/phenol complex (−5.23 kcal/mol) is ≈50% more negative than for the complex with β-CD (−2.62 kcal/mol). The latter is in excellent agreement with the experimental data (−2.69 kcal/mol), which validates the level of theory (B97-3c) used. Taken together, the methylation of β-CD increases the stability of the host-guest complex with the here studied guest phenol through stronger van der Waals interactions and hydrogen bonds. We attribute this to the disruption of the hydrogen bond network in the primary face of β-CD upon methylation, which influences the flexibility of the host toward the guest as well as the strength of the intermolecular interactions. Our work provides fundamental insights into the impact of different non-covalent interactions on host-guest stability, and we suggest that this theoretical framework can be adapted to other host-guest complexes to evaluate and quantify their non-covalent interactions.

Background/Objectives: Acute myeloid leukemia (AML) presents significant therapeutic challenges, particularly in cases driven by mutations in the FLT3 tyrosine kinase. This study aimed to develop a robust and user-friendly machine learning-based quantitative structure–activity relationship (QSAR) model to predict the inhibitory potency (pIC₅₀ values) of FLT3 inhibitors, addressing the limitations of previous models in dataset size, diversity, and predictive accuracy. Methods: Using a dataset which was 14 times larger than those employed in prior studies (1350 compounds with 1269 molecular descriptors), we trained a random forest regressor, chosen due to its superior predictive performance and resistance to overfitting. Rigorous internal validation via leave-one-out and 10-fold cross-validation yielded ${\mathrm{Q}}^2$ values of 0.926 and 0.922, respectively, while external validation on 270 independent compounds resulted in an R² value of 0.941 with a standard deviation of 0.237. Results: Key molecular descriptors influencing the inhibitor potency were identified, thereby improving the interpretability of structural requirements. Additionally, a user-friendly computational tool was developed to enable rapid prediction of pIC₅₀ values and facilitate ligand-based virtual screening, leading to the identification of promising FLT3 inhibitors. Conclusions: These results represent a significant advancement in the field of FLT3 inhibitor discovery, offering a reliable, practical, and efficient approach for early-stage drug development, potentially accelerating the creation of targeted therapies for AML.