Derive QSAR Models Research Articles

Novel Arylsulfonylhydrazones as Breast Anticancer Agents Discovered by Quantitative Structure-Activity Relationships.

Breast cancer (BC) is the second leading cause of cancer death in women, with more than 600,000 deaths annually. Despite the progress that has been made in early diagnosis and treatment of this disease, there is still a significant need for more effective drugs with fewer side effects. In the present study, we derive QSAR models with good predictive ability based on data from the literature and reveal the relationships between the chemical structures of a set of arylsulfonylhydrazones and their anticancer activity on human ER+ breast adenocarcinoma and triple-negative breast (TNBC) adenocarcinoma. Applying the derived knowledge, we design nine novel arylsulfonylhydrazones and screen them in silico for drug likeness. All nine molecules show suitable drug and lead properties. They are synthesized and tested in vitro for anticancer activity on MCF-7 and MDA-MB-231 cell lines. Most of the compounds are more active than predicted and show stronger activity on MCF-7 than on MDA-MB-231. Four of the compounds (1a, 1b, 1c, and 1e) show IC50 values below 1 μM on MCF-7 and one (1e) on MDA-MB-231. The presence of an indole ring bearing 5-Cl, 5-OCH3, or 1-COCH3 has the most pronounced positive effect on the cytotoxic activity of the arylsulfonylhydrazones designed in the present study.

Coumarin-based combined computational study to design novel drugs against Candida albicans.

Candida species cause the most prevalent fungal illness, candidiasis. Candida albicans is known to cause bloodstream infections. This species is a commensal bacterium, but it can cause hospital-acquired diseases, particularly in COVID-19 patients with impaired immune systems. Candida infections have increased in patients with acute respiratory distress syndrome. Coumarins are both naturally occurring and synthetically produced. In this study, the biological activity of 40 coumarin derivatives was used to create a three-dimensional quantitative structure activity relationship (3D-QSAR) model. The training and test minimum inhibitory concentration values of C. albicans active compounds were split, and a regression model based on statistical data was established. This model served as a foundation for the creation of coumarin derivative QSARs. This is a unique way to create new therapeutic compounds for various ailments. We constructed novel structural coumarin derivatives using the derived QSAR model, and the models were confirmed using molecular docking and molecular dynamics simulation.

Quantitative structure-activity relationship study on the CDK2 inhibitory activity of 6-substituted 2-arylaminopurines

QSAR study has been carried out on the CDK2 inhibitory activity of 6-substituted 2-arylaminopurines in 0D- to 2D-Dragon descriptors. The derived QSAR models have revealed that the reciprocal hyper-detour index (descriptor Rww) and path/walk 5 Randic shape index (descriptor PW5) played a pivotal role in rationalization of CDK2 inhibition activity of titled compounds. Molecular weight (MW), mean atomic volume scaled on Carbon atom (Mv) and atomic properties such as mass and atomic Sanderson electronegativity in terms of atomic properties weighted descriptors MATS1m, MATS3e, MATS4e, GATS3e and GATS8e, certain atom centred fragments such as H attached to C0(sp3) no X attached to next C (descriptor H-046),R--CH--X (descriptor C-027) and R--CX--X (descriptor C-029) are also predominant to explain CDK2 inhibition actions of 6-substituted 2-arylaminopurines. PLS analysis has also corroborated the dominance of CP-MLR identified descriptors. Applicability domain analysis revealed that the suggested model matches the high quality parameters with good fitting power and the capability of assessing external data and all of the compounds was within the applicability domain of the proposed model and were evaluated correctly.

Hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) Simulation: A Tool for Structure-Based Drug Design and Discovery.

Quantum Mechanics (QM) is the physics-based theory that explains the physical properties of nature at the level of atoms and sub-atoms. Molecular mechanics (MM) construct molecular systems through the use of classical mechanics. So, when combined, hybrid quantum mechanics and molecular mechanics (QM/MM) can act as computer-based methods that can be used to calculate the structure and property data of molecular structures. Hybrid QM/MM combines the strengths of QM with accuracy and MM with speed. QM/MM simulation can also be applied for the study of chemical processes in solutions, as well as in the proteins, and has a great scope in structure-based drug design (SBDD) and discovery. Hybrid QM/MM can also be applied to HTS to derive QSAR models. Due to the availability of many protein crystal structures, it has a great role in computational chemistry, especially in structure- and fragment-based drug design. Fused QM/MM simulations have been developed as a widespread method to explore chemical reactions in condensed phases. In QM/MM simulations, the quantum chemistry theory is used to treat the space in which the chemical reactions occur; however, the rest is defined through the molecular mechanics force field (MMFF). In this review, we have extensively reviewed recent literature pertaining to the use and applications of hybrid QM/MM simulations for ligand and structure-based computational methods for the design and discovery of therapeutic agents.

Quantitative structure-activity relationship study on the MMP-13 inhibitory activity of fused pyrimidine derivatives possessing a 1,2,4-Triazol-3-yl group as a ZBG

QSAR study has been carried out on the MMP-13 inhibitory activity of fused pyrimidine derivatives possessing a1,2,4-triazol-3-yl group as a ZBG in 0D- to 2D-Dragon descriptors. The derived QSAR models have revealed that the number of Sulfur atoms (descriptor nS), Balaban mean square distance index (descriptor MSD), molecular electrotopological variation (descriptor DELS), structural information content index of neighborhood symmetry of 2nd and 3rd order (descriptors SIC2 and SIC3), average valence connectivity index chi-4 (descriptor X4Av) in addition to 1st order Galvez topological charge index (descriptor JGI1) and global topological charge index (descriptor JGT) played a pivotal role in rationalization of MMP-13 inhibition activity of titled compounds. Atomic properties such as mass and volume in terms of atomic properties weighted descriptors MATS5m and MATS3v, and certain atom centred fragments such as CH2RX (descriptor C-006), X--CX--X (descriptor C-044), H attached to heteroatom (descriptor H-050) and H attached to C0(sp3) with 1X attached to next C (descriptor H-052) are also predominant to explain MMP-13 inhibition actions of fused pyrimidines. PLS analysis has also corroborated the dominance of CP-MLR identified descriptors. Applicability domain analysis revealed that the suggested model matches the high-quality parameters with good fitting power and the capability of assessing external data and all of the compounds was within the applicability domain of the proposed model and were evaluated correctly.

Conformational analysis and QSAR modeling of 14-membered macrolide analogues against mycobacterium tuberculosis

Electronic structures, effect of the substitution and structure physical-chemistry properties relationship for macrolide derivatives, have been studied by PM3 and ab initio methods. In the present work, the calculated values, namely net charges, bond lengths, MESP, dipole moments, electron-affinities, heats of formation, then, we treated the structural, physical and chemical relationships for a series of macrolide derivatives with inhibition activity against Mycobacterium tuberculosis. QSAR studies were done for these macrolide derivatives using a combination of various physicochemical descriptors. A multiple linear regression procedure was used to design the relationships between molecular descriptor and the activity of macrolide derivatives. Results validate the derived QSAR model.

A Derived QSAR Model for Predicting Some Compounds as Potent Antagonist against Mycobacterium tuberculosis: A Theoretical Approach.

Development of more potent antituberculosis agents is as a result of emergence of multidrug resistant strains of M. tuberculosis. Novel compounds are usually synthesized by trial approach with a lot of errors, which is time consuming and expensive. QSAR is a theoretical approach, which has the potential to reduce the aforementioned problem in discovering new potent drugs against M. tuberculosis. This approach was employed to develop multivariate QSAR model to correlate the chemical structures of the 2,4-disubstituted quinoline analogues with their observed activities using a theoretical approach. In order to build the robust QSAR model, Genetic Function Approximation (GFA) was employed as a tool for selecting the best descriptors that could efficiently predict the activities of the inhibitory agents. The developed model was influenced by molecular descriptors: AATS5e, VR1_Dzs, SpMin7_Bhe, TDB9e, and RDF110s. The internal validation test for the derived model was found to have correlation coefficient (R2) of 0.9265, adjusted correlation coefficient (R2 adj) value of 0.9045, and leave-one-out cross-validation coefficient (Q_cv∧2) value of 0.8512, while the external validation test was found to have (R2 test) of 0.8034 and Y-randomization coefficient (cR_p∧2) of 0.6633. The proposed QSAR model provides a valuable approach for modification of the lead compound and design and synthesis of more potent antitubercular agents.

Synthesis, molecular properties and comparative docking and QSAR of new 2-(7-hydroxy-2-oxo-2H-chromen-4-yl)acetic acid derivatives as possible anticancer agents.

Novel coumarin amino acid derivatives were synthesized. The structure of synthesized compounds has established on basis of different spectral data. The optimization geometry, frontier molecular orbitals (FMOs), thermodynamic parameters and chemical reactivity, were discussed using DFT\B3LYP by 6-311G* basis set, to identify a clear view for inter and intramolecular interaction of tested compounds. The molecular electrostatic potential (MEP) has plotted to investigate a recognition manner of synthesized compounds upon COX-2 receptor. All tested compounds showed a promising (NLOs) nonlinear optical properties. Bond dissociation energy (BDE) has studied to investigate a potency of these molecules against autoxidation mechanism Polynomial molecular docking logarithms have performed into the COX-2 active site for tested compounds. The docking protocol that has low RMSD has selected for discussion the binding affinity. The compounds with a high docking score 3,4,6-8,10 and 11 were selected for additional study against ADMET insilico, which showed that these compounds are a good oral bioavailability without observed carcinogenesis affect. The compounds (3,4,6-8,10 and 11) which that passed through docking and ADMET profile have examined their potency against (MCF-7) breast cancer cell in vitro. The compound 7 showed a highest potency against MCF-7 with IC50 value 0.39 μM. The QSAR model has created to discover the structural necessity inhibition of MCF-7. The derived QSAR model has a statistically significant with a good predictive power.

2D AND 3D-QSAR ANALYSIS OF AMINO (3-((3, 5-DIFLUORO-4-METHYL-6-PHENOXYPYRIDINE-2-YL) OXY) PHENYL) METHANIMINIUM DERIVATIVES AS FACTOR XA INHIBITOR

Objective: The main objective of the present study was to evolve a novel pharmacophore of methaniminium derivatives as factor Xa inhibitors by developing best 2D and 3D QSAR models.
 The models were developed for amino (3-((3, 5-difluoro-4-methyl-6-phenoxypyridine-2-yl) oxy) phenyl) methaniminium derivatives as factor Xa inhibitors.
 Methods: With the help of Marvin application, 2D structures of thirty compounds of methaniminium derivatives were drawn and consequently converted to 3D structures. 2D QSAR using multiple linear regression (MLR) analysis and PLS regression method was performed with the help of molecular design suite VLife MDS 4.3.3. 3D QSAR analysis was carried out using k-Nearest Neighbour Molecular Field Analysis (k-NN-MFA).
 Results: The most significant 2D models of methaniminium derivatives calculated squared correlation coefficient value 0.8002 using multiple linear regression (MLR) analysis. Partial Least Square (PLS) regression method was also employed. The results of both the methods were compared. In 2D QSAR model, T_C_O_5, T_2_O_2, s log p, T_2_O_1 and T_2_O_6 descriptors were found significant.
 The best 3D QSAR model with k-Nearest Neighbour Molecular Field Analysis have predicted q2 value 0.8790, q2_se value 0.0794, pred r2 value 0.9340 and pred_r2 se value 0.0540. The stepwise regression method was employed for anticipating the inhibitory activity of this class of compound. The 3D model demonstrated that hydrophobic, electrostatic and steric descriptors exhibit a crucial role in determining the inhibitory activity of this class of compounds.
 Conclusion: The developed 2D and 3D QSAR models have shown good r2 and q2 values of 0.8002 and 0.8790 respectively. There is high agreement in inhibitory properties of experimental and predicted values, which suggests that derived QSAR models have good predicting properties.
 The contour plots of 3D QSAR (k-NN-MFA) method furnish additional information on the relationship between the structure of the compound and their inhibitory activities which can be employed to construct newer potent factor Xa inhibitors.

3D-QSAR and docking studies on ursolic acid derivatives for anticancer activity based on bladder cell line T24 targeting NF-kB pathway inhibition

Bladder cancer is the common reason for mortality worldwide, and its increasing rate announces as a significant area of research in drug designing. The side effects and toxicity of existing drugs and the consequence of gradual cancer cell resistance against the available therapy make the treatment poor. Globally, there is a continuous high demand to develop new, more potent, and easily affordable drugs against cancer. The current research article illustrates the application of developed three-dimensional quantitative structure-activity relationship (3D-QSAR) based on human bladder cancer cell line T24 in vitro anticancer activity. The derived QSAR model has been used for prediction of natural compounds and analogs with 80% similarity of the most active compound of the dataset. The developed model describes the structure-activity relationship for terpenes and their derivatives at the molecular level. The developed comparative molecular field analysis (CoMFA) model shows a satisfactory cross-validation correlation coefficient (q2) of 0.54 and a regression correlation coefficient (r2) of 0.86. In order to evaluate the compliance with electronic pharmacokinetic parameters, Lipinski’s rule of five filter, absorption, distribution, metabolism, and excretion (ADME) and toxicity of predicted compounds have been calculated. Furthermore, molecular-docking study has been performed to prioritize these predicted compounds based on their docking score and binding pocket similarity through the identified potential anticancer targets. Finally, two compounds T9 and B42 have been identified as the best hit because these two fall within the standard limits of all filters and show a good binding affinity. Conclusively, all satisfactory results strongly suggest that the derived 3D-QSAR model and obtained candidate’s binding structures are reasonable in the prediction of a new antagonist’s activity. The strategy adopted in the present research is expected to be of immense importance and a great support in the identification and optimization of lead in the early and advance drug discovery.

QSAR and Pharmacophore Mapping Studies on Benzothiazinimines to Relate their Structural Features with anti-HIV Activity.

The development of severe drug resistance caused by the extensive use of anti-HIV agents has resulted in a greatly extensive reduction in these drugs efficacy. To identify the important pharmacophoric features and correlate 3D chemical structure of benzothiazinimines with their anti-HIV potential using 2D, 3D-QSAR and pharmacophore modeling studies. QSAR and pharmacophore mapping studies have been used to relate structural features. 2D QSAR and 3D QSAR studies were performed using partial least square and k-nearest neighbor methodology, coupled with various feature selection methods, viz. stepwise, genetic algorithm, and simulated annealing, to derive QSAR models which were further validated for statistical significance. The physicochemical descriptor XAHydrophilicArea and SsOHE-index, and alignmentindependent descriptor T_C_Cl_6 showed significant correlation with the anti-HIV activity of benzothiazinimines in 2D QSAR. 3D QSAR results showed the significant effect of electrostatic and steric field descriptors in the anti-HIV potential of benzothiazinimines. The generated pharmacophore hypothesis demonstrated the importance of aromaticity and hydrogen bond acceptors. The significant models obtained in this study suggested that these techniques could be used as a guidance for designing new benzothiazinimines with enhanced anti-HIV potential.

Virtual screening, Docking, ADMET and System Pharmacology studies on Garcinia caged Xanthone derivatives for Anticancer activity

Caged xanthones are bioactive compounds mainly derived from the Garcinia genus. In this study, a structure-activity relationship (SAR) of caged xanthones and their derivatives for anticancer activity against different cancer cell lines such as A549, HepG2 and U251 were developed through quantitative (Q)-SAR modeling approach. The regression coefficient (r2), internal cross-validation regression coefficient (q2) and external cross-validation regression coefficient (pred_r2) of derived QSAR models were 0.87, 0.81 and 0.82, for A549, whereas, 0.87, 0.84 and 0.90, for HepG2, and 0.86, 0.83 and 0.83, for U251 respectively. These models were used to design and screened the potential caged xanthone derivatives. Further, the compounds were filtered through the rule of five, ADMET-risk and synthetic accessibility. Filtered compounds were then docked to identify the possible target binding pocket, to obtain a set of aligned ligand poses and to prioritize the predicted active compounds. The scrutinized compounds, as well as their metabolites, were evaluated for different pharmacokinetics parameters such as absorption, distribution, metabolism, excretion, and toxicity. Finally, the top hit compound 1G was analyzed by system pharmacology approaches such as gene ontology, metabolic networks, process networks, drug target network, signaling pathway maps as well as identification of off-target proteins that may cause adverse reactions.

Imidazole derivatives as angiotensin II AT1 receptor blockers: Benchmarks, drug-like calculations and quantitative structure-activity relationships modeling

We performed benchmark studies on the molecular geometry, electron properties and vibrational analysis of imidazole using semi-empirical, density functional theory and post Hartree-Fock methods. These studies validated the use of AM1 for the treatment of larger systems. Then, we treated the structural, physical and chemical relationships for a series of imidazole derivatives acting as angiotensin II AT1 receptor blockers using AM1. QSAR studies were done for these imidazole derivatives using a combination of various physicochemical descriptors. A multiple linear regression procedure was used to design the relationships between molecular descriptor and the activity of imidazole derivatives. Results validate the derived QSAR model.

PREDICTING POTENT ANTIHYPERTENSIVE COMPOUNDS OF PYRIDINE DERIVATIVES BY MOLECULAR MODELING APPROACH

A quantitative structure activity relationship study was performed on a series of pyridine substituted compounds as angiotensin II receptors for establishing quantitative relationship between biological activity and their physicochemical structural properties. Partial least square (PLS) methodology coupled with stepwise forward backward method were applied to derive QSAR models, which were further validated for statistical significance and predictive ability by internal and external validation. A 2D QSAR model was generated with correlation coefficient (r2) of 0.8874, significant cross-validated correlation coefficient (q2) of 0.7498, r2 for external test set (pred_r2) 0.7869. A Zscore_r2 value of 3.9853, Zscore_q2 value of 2.8642 and Zscore_ pred_r2 value of 1.0583 statistically validates the significance of the obtained QSAR model. Molecular field analysis (MFA) was used to construct the best 3D-QSAR model showing good correlative and predictive capabilities in terms of q2= 0.7264 and pred_r2= 0.6988. The quantitative structure activity relationship study provides important structural insights in the designing of potent antihypertensive molecules.

Quantitative Structure Activity Relationships for Carboxamides and Related Compounds Active on Aedes aegypti Adult Females

Aedes aegypti mosquitoes are important vectors in the transmission of severe diseases responsible for million deaths per year. Intensive use of insecticides results in environmental damages and induced resistance in mosquitoes. Search for new molecules devoid of detrimental side effects is therefore an urgent need. In this context, we derived QSAR models for evaluating the acute toxicity of 74 carboxamides and related chemicals to females of Ae. aegypti. These models based on PaDEL, 2D topological descriptors or CODESSA, 2D/3D geometrical and quantum variables, involved multilinear regression (MLR), and various machine learning methods namely support vector machine (SVM), projection pursuit regression (PPR) and artificial neural network (ANN). We considered first the full dataset, and then, a more homogeneous, reduced set of 50 compounds with non-conjugated carbonyl. In all cases, for data fitting and leave-one-out cross-validation, satisfactory results were attained. Good performance was also obtained for extended validation sets. Generally speaking, the modeling methods were broadly equivalent. PaDEL 2D descriptors worked better than 2D/3D CODESSA descriptors. A hybrid model combining the two descriptor sets gave improved results. Setting such QSAR models, linking activity to structural features of examined chemicals, will be of interest for prioritizing experimental tests on new candidates, and evaluate their toxicity and potential synergist effects.

Discovery of molecular mechanism of a clinical herbal formula upregulating serum HDL-c levels in treatment of metabolic syndrome by in vivo and computational studies

Decreased HDL cholesterol (HDL-c) is considered as an independent risk factor of cardiovascular disease in metabolic syndrome (Mets). Wendan decoction (WDD), a famous clinical traditional Chinese medicine formula in Mets in China, which can obviously up-regulate serum HDL-c levels in Mets. However, till now, the molecular mechanism of up-regulation still remained unclear. In this study, an integrated approach that combined serum ABCA1 in vivo assay, QSAR modeling and molecular docking was developed to explore the molecular mechanism and chemical substance basis of WDD upregulating HDL-c levels. Compared with Mets model group, serum ABCA1 and HDL-c levels intervened by two different doses of WDD for two weeks were significantly up-regulated. Then, kohonen and LDA were applied to develop QSAR models for ABCA1 up-regulators based flavonoids. The derived QSAR model produced the overall accuracy of 100%, a very powerful tool for screening ABCA1 up-regulators. The QSAR model prediction revealed 67 flavonoids in WDD were ABCA1 up-regulators. Finally, they were subjected to the molecular docking to understand their roles in up-regulating ABCA1 expression, which led to discovery of 23 ABCA1 up-regulators targeting LXR beta. Overall, QSAR modeling and docking studies well accounted for the observed in vivo activities of ABCA1 affected by WDD.

3D QSAR Studies on Some 5-(1H-Indol-5-yl)-1, 3, 4-Thiadiazol-2 Amines as Potential PIM-1 Inhibitors

Objective: Proviral integration site for moloney murine leukemia virus (PIM) kinases are among the contemporary targets for cancer chemotherapy as they play a pivotal role in inhibition of apoptosis and oncogenic signaling. Owing to their structural and functional dissimilarity to other kinases, they can be specifically targeted. The present study is an attempt to establish three dimensional quantitative structure activity relationship (3D QSAR) between some 5-(1 H -Indol-5-yl)-1,3,4-thiadiazol-2-amines for their inhibitory activity against PIM by partial least squares regression (PLSR) analysis. Method: PLSR coupled with different variable selection methods such as stepwise (forward-backward) (SW), genetic algorithm (GA) and simulated annealing (SA) was performed to derive QSAR models and these models were validated for statistical significance internally and externally and robustness. Results: Two most significant models were generated through GA with optimum values of validation parameters. Among these model 1 exhibited q 2 and pred_r 2 values of 0.7523 and 0.8714 and model 2 evinced 0.6577 and 0.7675 respectively. The steric field point in model 1 suggests the need of more bulky group at this position and the positive coefficients of electrostatic field points at positions E_999, E_1162 respectively indicate the need of electronegative groups for favorable biological activity. Model 2 suggests the requirement of a positive hydrophobic group at H_564 and less electronegative and more electronegative group at E_157 and E_630 respectively for more potent biological activity. Conclusion: More bulky and/or hydrophobic R groups apart from the electrostatic contribution may be favorable for better PIM-1 inhibition of 5-(1 H -indol-5-yl)-1,3,4-thiadiazol-2 amines. Key words: 1,3,4-Thiadiazole-2-amine, PIM, QSAR, Anticancer, PLSR.

2D QSAR studies of the inhibitory activity of a series of substituted purine derivatives against c-Src tyrosine kinase

A series of 34 substituted purine analogues derivatives were subjected to quantitative structure-activity relationship analyses as inhibitors of c-Src tyrosine kinase. Partial least squares regression was applied to derive QSAR models, which were further validated for statistical significance by internal and external validation. The best QSAR model developed had a good predictive correlation coefficient (r2) of 0.8319, a significant cross-validated correlation coefficient (q2) of 0.7550, and an r2 for the external test set (pred_r2) of 0.7983. It was developed from the PLS method with descriptors including the SsCH3E-index, H-Donor Count, T_2_Cl_3, and negative correlation with SsOHcount. The current study provides better insight into the future design of more potent c-Src tyrosine kinase inhibitors prior to synthesis.

CP-MLR Directed QSAR Rationales for the 1-aryl Sulfonyl Tryptamines as 5-HT6 Receptor Ligands

A QSAR study has been carried out to rationalize the 5-HT6 receptor binding affinities of the 1-aryl sulfonyl tryptamine derivatives using Dragon descriptors. A higher value of molecular symmetry and topology accounting Randic shape index descriptor PW4 (path/walk 4) would be favorable to improve the binding affinity. Presence of more number of bromine atoms (descriptor nBR) and presence of such structural fragment in which a hydrogen atom attached to sp3 hybridized carbon with no hetero atom rather than one hetero atom attached to next carbon atom (descriptors H-046 and H-052) will be supportive to the activity. The prevalence of atomic properties to explain the binding affinity is evident from the associations of polarizability to the path length 7 of Moran autocorrelation (MATS7p), masses to eigenvalues n.2 and 7 of Burden m atrix (BELm2 and BEHm7), Sanderson electronegativity to highest eigenvalue n.2 Burden matrix (BEHe2) and van der Waals volume to path length 8 of Geary autocorrelation (GATS8v) and charge content in terms of topological and mean topological charge indices (GGI3 and JGI2). The dominance of the information content of the descriptors, emerged in CP-MLR models, has also confirmed by the PLS analysis. The derived QSAR models and descriptors shared in these models revealed that the substituents of tryptamine moiety have sufficient scope for further modification.

Structure Activity Relationships, QSAR Modeling and Drug-Like Calculations of TP Inhibition of 1,3,4-Oxadiazoline-2-Thione Derivatives

QSAR studies have been performed on twenty-one molecules of 1,3,4-oxadiazoline-2-thiones. The compounds used are among the most thymidine phosphorylase (TP) inhibitors. A multiple linear regression (MLR) procedure was used to design the relationships between molecular descriptor and TP inhibition of the 1,3,4-oxadiazoline-2-thione derivatives. The predictivity of the model was estimated by cross-validation with the leave-one-out method. Our results suggest a QSAR model based of the following descriptors: logP, HE, Pol, MR, MV, and MW, qO1, SAG, for the TP inhibitory activity. To confirm the predictive power of the models, an external set of molecules was used. High correlation between experimental and predicted activity values was observed, indicating the validation and the good quality of the derived QSAR models.