Botrytis Cinerea is a plant pathogen that affect a large number of plant species like tomatoes, Lettuce, Grapes, and Strawberries among others. Sulfonamides are widely used in pharmaceutical industries as anti-cancer, anti-inflammatory and anti-viral agents. To complement our previous QSAR study, a ligand-based design and ADME/T study were carried out on these sulfonamides compounds for their fungicidal activity toward "Botrytis Cinerea". With the help of AutoDock Vina version 4.0 in Pyrex software, the docking analysis was performed after optimization of the compounds at DFT/B3LYP/6-31G* quantum mechanical method using Spartan 14 softwar. Using the model generated in the previous QSAR work, the descriptors of the chosen model were considered in modifying the most promising compound '9' in which twelve (12) derivatives were designed and found to have better activity than the template (compound 9). With compound 9j having the highest activity that turns out to be about 14 and 15 times more potent than the commercial fungicides "procymidone and chlorothalonil". Furthermore, ADME/T properties of the designed compounds were calculated using the SwissADME online tool in which all the compounds were found to have good pharmacokinetic profile. Moreover, a molecular docking study on selected compounds of the dataset (compound 8, 13, 14, 19, 20, 21, 22 and 29) revealed that compound '20' turned out to have the highest docking score of -8.5 kJ/mol. This compound has a strong affinity with the macromolecular target point (PDB ID: 3wh1) producing H-bond and hydrophobic interaction at the target point of amino acid residue. The molecular docking analysis gave an insight on the structure-based design of the new compounds with better activity against B. cinerea.

Background Colorectal cancer is common to both sexes; third in terms of morbidity and second in terms of mortality, accounting for 10% and 9.2% of cancer cases in men and women globally. Although drugs such as bevacizumab, Camptosar, and cetuximab are being used to manage colorectal cancer, the efficacy of the drugs has been reported to vary from patient to patient. These drugs have also been reported to have varying degrees of side effects; thus, the need for novel drug therapies with better efficacy and lesser side effects. In silico drugs design methods provide a faster and cost-effect method for lead identification and optimization. The aim of this study, therefore, was to design novel imidazol-5-ones via in silico design methods. Results A QSAR model was built using the genetic function algorithm method to model the cytotoxicity of the compounds against the HCT116 colorectal cancer cell line. The built model had statistical parameters; R2 = 0.7397, R2adj = 0.6712, Q2cv = 0.5547, and R2ext. = 0.7202 and revealed the cytotoxic activity of the compounds to be dependent on the molecular descriptors nS, GATS5s, VR1_Dze, ETA_dBetaP, and L3i. These molecular descriptors were poorly correlated (VIF < 4.0) and made unique contributions to the built model. The model was used to design a novel set of derivatives via the ligand-based drug design approach. Compounds e, h, j, and l showed significantly better cytotoxicity (IC50 < 5.0 μM) compared to the template. The interaction of the compounds with the CDK2 enzyme (PDB ID: 6GUE) was investigated via molecular docking study. The compounds were potent inhibitors of the enzyme having binding affinity of range −10.8 to −11.0 kcal/mol and primarily formed hydrogen bond interaction with lysine, aspartic acid, leucine, and histidine amino acid residues of the enzyme. Conclusion The QSAR model built was stable, robust, and had a good predicting ability. Thus, predictions made by the model were reliably employed in further in silico studies. The compounds designed were more active than the template and showed better inhibition of the CDK2 enzyme compared to the standard drugs sorafenib and kenpaullone.

An insilico study was carried out on a series of thirty-five (35) sulfonyl-containing compounds for their antifungal activities against Botrytis Cinerea fungi using QSAR techniques. Using Spartan 14 molecular modelling software to draw the molecular structure of the compounds, the DFT/B3LYP/6-31G* quantum method of the software was used in optimizing the drawn compounds. The optimized compounds of the dataset were then underbring into PaDEL-Descriptor software for their molecular descriptors calculation. The calculated PaDel-descriptors were then subjected to data-Pretreatment and later splitted into 70% training set and 30% test set. The model was generated using the training set and the test set for the validation of the model built. Using Genetic Function Algorithm (GFA) the model was developed. Four models were developed in which model 1 was chosen as the optimum model with good statistical parameters; R 2 = 0.954, R 2 adj =0.941, cross validation R 2 / Q 2 cv = 0.888 and R 2 pred = 0.839. The model proposed was found to be stable, robust and showed a good internal and external validation. Other statistical analysis such as mean effect, variance inflation factor (VIF), Williams plot among others were also carried out for the applicability domain of the model. k e y w o r d s QSAR 2-substituted Sulfonamides Fungicides

Background: Aphis craccivora has many plant hosts, though it seemingly forechoice to groups of bean family. Other plants it hosts are families of Solanaceae, Rosaceae, Malvaceae, Chenopodiaceae, Caryophyllaceae, Ranunculaceae, Cucurbitaceae, Brassicaceae, and Asteraceae. Result: A computational study was carried out on a series of twenty compounds of novel 4-(N,N-diarylmethylamines) furan-2(5H)-one derivatives against Aphis craccivora insect. Optimization of the compounds was performed with the aid of Spartan 14 software using DFT/B3LYP/6-31G** quantum mechanical method. Using PaDel descriptor software to calculate the descriptors, Generic Function Approximation (GFA) was employed to generate the model. Model 1 found to be the optimal out of four models generated which has the following statistical parameters; R 2 = 0.871489, R 2 adj = 0.83644, cross-validated R 2 = 0.790821, and external R 2 = 0.550768. Molecular docking study occurred between the compounds and the complex crystal structure of the acetylcholine (protein AChBP) (PDB CODE 2zju) in which compound 13 was identified to have the highest binding energy of − 8.4 kcalmol −1. Statistical analyses, such as variance inflation factor, mean effect, and the applicability domain, were conducted on the model. This compound has a strong affinity with the macromolecular target point of the A. craccivora (2zju) producing Hbond and as well the hydrophobic interaction at the target point of amino acid residue. Molecular docking gave an insight into the structure-based design of the new compounds with better activity against A. craccivora in which three compounds A, B, and C were designed and discovered to be of high quality and have greater binding affinity compared to the one obtained from the literature. Conclusion: The QSAR model was generated by the employment of Genetic Function Approximation (GFA). The model was found to be robust and possessed a good statistical parameter. Furthermore, a molecular docking study was performed to get an idea for structure-based design in which three (3) compounds A, B, and C were designed and were found to be more active than the template (compound 13, i.e., the one with highest docking score). QSAR model was developed to give an insight into the ligand/template-based design of computer-aided drug design.

Quantitative structure-activity relationships (QSAR) modelling on 30 N-Arylidenequinoline-3-carbohydrazides analogs was performed using Multi-Linear Regression (MLR) analysis adopting Genetic Function Algorithm (GFA) method. Semi empirical method using PM6 basis set was used for complete geometry optimization of the data set. The best model was chosen based on its statistical fit due to it good internal and external validations. From the Williams plot, it can be inferred that the reported model can make prediction of new compounds that are not within the data set. The molecular docking study showed that, the most active chemical in the data set was better than the standard β-glucuronidase inhibitor both in terms of binding scores and the amino acid residues that interacted with the drug and β-glucuronidase enzyme. The Pharmacokinetic studies indicated that none of the chemicals violated any of the condition set by the Lipinski′s Rule of five which confirm the bioavailability of these chemicals. The results these findings give room for designing novel β-glucuronidase inhibitors that are highly effective.                                                Resumen. Se llevó a cabo la técnica de QSAR en 30 analogos de N-arilidenequinolina-3-carbohidrazidas mediante el analisis de regresesión lineal múltiple (MLS) adopatando el método del algoritmo de función genética (GFA). Para la optimización completa de la geometría del conjunto de datos se utilizó un método semiémpirico del conjunto de bases PM6. El mejor modelo fue elegido basado en función de su ajuste estadístico debido a su validación interna y externa. A partir de la gráfica de Williams, se puede inferir que el modelo reportado puede predecir nuevos compuestos que no se encuentran en el conjunto de datos. Este estudio de acomplamiento molecular mostró que, el químico más activo del conjunto de datos fue mejor que el inhibidor estándar β-glucuronidasa, tanto en términos de unión y en términos de  interacción de los residuos con el fármaco y la enzima β-glucuronidasa. Los estudios farmacocinéticos que indicaron que ninguno de los fármacos incumple ninguna de las condiciones establecidas por la regla de cinco de Lipinski, en donde se confirma la biodisponibilidad de estos químicos. Los resultados de los hallazgos computacionales permiten diseñar nuevos inhibidores de la β-glucuronidasa que son altamente efectivos.

Background: Pyrazole-furan and pyrazole-pyrrole moiety are among the molecular structures that were found to have an extensive range of applications in the field of medicine and agrochemical due to their wide spectrum of biological activities. These include antimicrobial activity, anti-glaucoma activity, ocular hypertension activity, and antifungal activity. Results: An in silico study was carried out on 37 compounds of pyrazole-furan and pyrazole-pyrrole carboxamide derivatives against Sclerotinia sclerotiorum. Using Spartan 14 software, optimization of the compounds was performed at the DFT/B3LYP/6-31G* quantum mechanical method. PaDEL descriptor software was used to calculate the molecular descriptors, and a Generic Function Approximation (GFA) was employed to generate the model. Out of four models generated, model 1 was found to be the optimal and has the following statistical parameters; R 2 = 0.83485, R 2 adj = 0.793563, cross-validated R 2 = 0.74037, and external R 2 = 0.58479. Molecular docking study was carried out between the antifungal compounds, and the binding site of S. sclerotiorum (PDB CODE 2X2S) in which compound 7 was identified to have the highest binding energy of − 7.5kcal/mol. This compound "7" has a strong affinity with the macromolecular target point of the S. sclerotiorum (2x2s), producing H-bond and as well as the hydrophobic interaction at target point of the amino acid residue. Considering compound 7 as our scaffold, four (4) more potent compounds (7a, 7b, 7c, and 7d) were designed using optimization method of structure-based designed which have the following docking score, − 7.7, − 7.8, − 7.7, and − 7.7kcal/mol. Conclusion: Statistical analyses including variance inflation factor (VIF), mean effect (ME), and applicability domain were conducted on the model. Considering an interpretation of the descriptors given in the discussion, the QSAR model provided an idea of ligand-based design while the molecular docking gave an insight on structure-based design of the new compounds with better activity against S. sclerotiorum in which four (4) compounds 7a, 7b, 7c, and 7d were designed and discovered to be of high quality and have greater binding affinity compared to the one obtained from the literature (compound 7). BackgroundSclerotinia sclerotiorum otherwise called cottony rot, blossom blight, stem rot, crown rot, or watery soft rot, is a fungal pathogen that results in a plant disease known as "white mold" under favorable conditions. This pathogen produces black resting structures (called sclerotia) on the affected plant. It can be found in different parts of the world with an extensive range of hosts [4]. S. sclerotiorum causes great losses when onset on a favorable environmental and extensive care or control measures should drastically be taken [23]. Herbaceous, succulent plants (particularly flowers) and vegetables are the common hosts.Pyrazole-furan and pyrazole-pyrrole moiety are among the molecular structures found to have an extensive wide range of applications in the field of medic...

Background: The 1,3,4-thiadiazoles are among the structural moieties that were found to be of utmost importance in the fields of pharmacy and agrochemicals because of their widespread biological activity that includes antitumor, antibacterial, anti-inflammatory, antihypertensive, anti-tuberculosis, anticonvulsant, and antimicrobial, among others. Results: QSAR and molecular docking studies were carried out on thirty-two (32) derivatives of 2,5-disubstituted-1,3, 4-thiadiazoles for their antifungal activities toward Phytophthora infestans. Using the "graphical user interface" of Spartan14 software, the structure of the compounds of the dataset is drawn and then optimized at DFT/B3LYP/6-31G* quantum mechanical method of the software. Molecular descriptors of the optimized compounds were calculated and later on divided into the training set and test sets (at a ratio of 3:1). The training set was used for model generation and the test set was for external validation of the generated model. Four models were generated by the employment of genetic function approximation (GFA) in which the optimal model (4) turned out to have the following statistical parameters: R 2 = 0.798318, R 2 adj = 0.750864, cross-validation R 2 (Q 2 cv) = 0.662654, and external validation R 2 pred = 0.624008. On the molecular docking study of thiadiazole compounds with the target protein of Phytophthora infestans effector site (PDB ID: 2NAR), compound 13 shows the highest binding affinity with − 9.3 kcal/mol docking score and composes hydrophobic as well as H-bond interactions with the target protein (2NAR). Conclusion: The result of the QSAR study signifies the stability and robustness of the built model by considering the validation parameters and this gave an idea of template/ligand-based design while the molecular docking study revealed the binding interaction between the ligand and the protein site which gave an insight toward an "optimization method" of the structure-based design for the discovery of more potent compounds with better activity against Phytophthora infestans using the approach of computer-aided drug design (CADD) in plant pathology.