Aug 2026· Asian Journal of Chemical Sciences· 0 citations
Abstract
This study examined the relationship between the water/octanol partition coefficient of eighteen alkylphenols and molecular descriptors derived from quantum-chemical calculations using a quantitative structure-property relationship (QSPR) approach. The experimental database was divided into a training set of fourteen compounds and a test set of four compounds. Three descriptors, the electronic energy (ET), the energy of the Lowest Unoccupied Molecular Orbital (ELUMO) and the energy gap (ΔEgap) were used to develop a multiple linear regression model. The model showed a strong association between the experimental lipophilicity values and the selected descriptors, with R = 0.9850, R² = 0.9703, a standard deviation of 0.0853, and F = 108.9532. Internal validation using leave-one-out cross-validation and property randomisation, together with external validation using the test set and the Tropsha criteria, was applied to assess model stability and predictive performance. The applicability domain was evaluated using a Williams plot. Within the studied dataset and the defined applicability domain, the model showed close agreement between experimental and predicted water/octanol partition coefficients. The reported validation results indicate that the selected quantum-chemical descriptors can be used to model lipophilicity within this alkylphenol series. Predictions for additional alkylphenols should, however, remain restricted to compounds that fall within the model’s defined applicability domain.
In our previous work, we established a predictive QSPR (Quantitative Structure-Property Relationship) model for the water/octanol partition coefficient, based on electronic energy (ET), LUMO (Lowest Unoccupied Molecular Orbital) energy, and the energy gap (ΔEgap). This model has been shown to be suitable for predicting properties of new alkylphenols falling within its applicability domain, with a 5% risk level. The present study, which builds upon that earlier work, aims to expand the database for this family of compounds by designing new alkylphenols that fit within the established model's applicability domain and by predicting their water/octanol partition coefficients. In this context, we designed a series of forty-two (42) new alkylphenols. All these molecules were optimized at the B3LYP/6-311G(d,p) level of theory, and vibrational frequencies were calculated at the same level. Water/octanol partition coefficients calculated for these molecules using the model indicate that they are all lipophilic. The formation reaction for these new compounds proceeds spontaneously, accompanied by heat release and a decrease in disorder.
Unknown authors· International Research Journ...· 0 citations
For the first time, a comprehensive and predictive MLR-based QSPR model has been developed for this target and the predictive capability of the MLR-QSPR model was acceptable for training set.
Ali Ebrahimpoor Gorji, V. Alopaeus· Journal of Computer-Aided Mo...· 0 citations
The ionisation properties of the imines depend on the electronic distribution around the nitrogen of the imine and the presence of electron-donating, electron-withdrawing and hydrogen-bonding groups in the vicinity. We examined the correlations between semi-empirical AM1 molecular descriptors and experimental pKa values for a coded series of 14 imine compounds. Geometry optimisations and descriptor calculations were performed in ChemOffice using the AM1 method. The variables extracted included Mulliken atomic charges, selected bond lengths and bond angles, highest occupied and lowest unoccupied molecular orbital energies (HOMO and LUMO), chemical potential (μ), chemical hardness (η), electrophilicity index (ω), steric energy (S.E.), and van der Waals energy. The calculated variables were correlated with the experimental pKa values by bivariate statistical analysis in SPSS. The best correlation reported was for the combined N8-charge and hydroxyl-group model (R2 = 0.969), followed by stretching plus hydroxyl-group descriptors (R2 = 0.966), and the lowest reported value was for van der Waals energy plus N3 charge (R2 = 0.769). These results suggest that the local electronic environment around N8 and the presence or geometry of an O-H group are the most influential descriptors in the current dataset. However, before publication, the compound identities, experimental pKa values, software version, and full statistical output must be provided, and the R/R² notation reported must be checked. The results thus support AM1 descriptors as preliminary screening variables rather than a fully validated predictive model.
Mutlaq Saud Khalaf· Journal of Medical Science,...· 0 citations
Abstract One of the most frequently used methods in computational drug design is quantitative structure-activity relationship (QSAR). The main purpose of QSAR modeling is to estimate the relationship between chemical structures and biological activity in a group of molecules. In this method, molecules that have the greatest impact and the least side effects can be identified and extracted among huge numbers of molecular compounds. The molecular descriptors play a crucial role in QSAR design, and contain the physical, chemical, and geometric information. This information is called a feature that acts as an input to the QSAR model. Today, with the design of various applications to calculate molecular descriptors, the information obtained for each chemical structure is rising day by day which could lead to serious problems such as redundancy and over fitting. To solve this problem, researchers have used various techniques such as feature selection to improve the results of the model. The important point is that if the features are not properly selected, the QSAR model will fail. Up to now, different algorithms have been proposed to select the descriptors, which there are two main categories, supervised and unsupervised. The main purpose of this paper is to review feature selection methods in QSAR studies.
Fahimeh Motamedi, S. Zareian, S. Sardari et al.· Journal of Nonlinear, Comple...· 0 citations
INTRODUCTION
The study aimed to develop a QSAR model for a series of 1,3,4- oxadiazole derivatives to identify molecular determinants influencing anticancer activity and provide a predictive framework for rational drug design.
METHODS
A QSAR model was constructed using the Simulated Orthogonal Projections to Latent Structures (SO-PLS) approach. Five molecular field descriptors, steric (gauss_s), electrostatic (gauss_e), hydrophobic (gauss_h), hydrogen-bond acceptor (gauss_a), and hydrogen-bond donor (gauss_d), were employed. The model was developed on a training set of 36 compounds and evaluated using Partial Least Squares regression with up to five latent factors. Y-randomization was applied to assess model robustness.
RESULTS
The model demonstrated an excellent fit (R² = 0.931) and acceptable predictive ability (R²-CV = 0.478), with Y-randomization confirming robustness (R²-scrambled = 0.679). Field contribution analysis revealed steric (29.52%) and hydrophobic (24.92%) effects as the primary determinants of biological activity, followed by hydrogen-bond donor (18.81%) and acceptor (18.02%) contributions, while electrostatic interactions (8.73%) were less influential. Regression coefficient analysis identified molecular regions where targeted substitutions could enhance activity.
DISCUSSION
The findings highlight the critical role of steric and hydrophobic interactions in modulating the anticancer activity of oxadiazole derivatives. Strategic molecular modifications guided by field contributions and regression analysis can improve potency, supporting rational design of novel oxadiazole-based anticancer agents.
CONCLUSION
This QSAR model provides a robust predictive tool for designing 1,3, 4-oxadiazole derivatives with enhanced anticancer activity.
V. K, S. R· Current Drug Discovery Techn...· 0 citations
This study examines seventy‐one 1,4‐naphthoquinone scaffold‐bearing compounds with known half‐maximal inhibitory concentration (IC
50
) against
Mycobacterium tuberculosis
(Mtb) using QSAR, docking, and molecular dynamics (MD) simulations. The molecular descriptors were computed using PaDEL and ChemDes to create multiple linear regression (MLR) based predictive 2D QSAR models through QSARINS v2.2.4. The statistically suitable five‐descriptor QSAR model demonstrated a correlation coefficient (
R
2
0.7136) and a cross‐validated
R
2
(
Q
2
LOO
0.6599). The model exhibited lower values for root mean squared error (RMSE
tr
0.2298) and mean absolute error (MAE
tr
0.1738), along with a higher concordance correlation coefficient (CCC 0.8329), indicating strong fitness and predictive accuracy. In silico screening of all compounds for physicochemical and medicinal chemistry parameters, followed by docking against five key Tb pathogenesis proteins using Cresset Flare 10.0.1, identified 24 leading candidates. A 200 ns MD simulation revealed good protein‐ligand complex stability of two compounds,
52
and
70
, which was further supported by MM/GBSA calculation. SAR analysis demonstrates that introducing chlorine into the quinone scaffold, in combination with highly lipophilic aryl substituents such as trifluoromethyl, significantly enhances binding affinity. Considering suitable druggability parameters, we suggest compound
70
for further research to confirm its potential as an effective anti‐TB drug.
Pallavi Singh, H. Upadhyay, Somya Maurya· ChemistrySelect· 0 citations
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