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.
Organic chemistry; Pharmaceutical chemistry; Theoretical chemistry; Ligand-based design; ADME/T; Molecular docking; Anti-fungal; Botrytis cinerea
PIP4K2A is a type II lipid kinase that catalyzed the rate-limiting step of the conversion of phosphatidylinositol-5-phosphate (PI5P) into phosphatidylinositol 4,5-bisphosphate (PI4,5P2). PIP4K2A has ...been intricately linked to the inhibition of various types of tumors
via
reactive oxygen species-mediated apoptosis, making it an important therapeutic target. In the quest of finding biologically active substances with efficient PIP4K2A inhibitory activity, machine learning algorithms were used to investigate the quantitative relationship between structures and inhibitory activities of 1,7-naphthyridine analogues. Three machine learning algorithms (MLR, ANN, and SVM) were used to develop QSAR models that can effectively predict the PIP4K2A inhibitory activity of a library of 1,7-naphthyridine analogues. The cascaded feature selection method was performed by sequential application of GFA and MP5 algorithms to identify a molecular descriptor subset that can best describe the PIP4K2A inhibitory activity of 1,7-naphthyridine analogues. PIP4K2A inhibitory activities predicted by the ML models were strongly correlated with the experimental values. The QSAR Modelling indicates that the best-performing ML model was SVM with the RBF kernel function. The SVM model performed very well in predicting PIP4K2A inhibitory activity of the 1,7-naphthyridine analogues with RTR and QEX values of 0.9845 and 0.8793 respectively. To further gain more structural insight into the origin of PIP4K2A inhibitory activity of 1,7-naphthyridine analogues, molecular docking studies were performed. The results indicate that five compounds; 15, 25, 13, 09, and 28 were found to have a high binding affinity with the receptor molecules. Hydrogen bonding, pi-pi interaction, and pi-cation interactions were found to modulate the binding interaction of the inhibitors. Although the SVM gives essentially a black-box model which cannot be readily interpreted, using SVM in tandem with MLR and ANN provides a unique perspective in building robust QSAR predictive models. The superior predictive performance of the ML models and the explanatory power of MLR models were combined to provide a unique insight into the structure-activity relationship of 1,7-naphthyridine inhibitors. This is relevant in that it provides information that can be invaluable as guidelines for the design of novel PIP4K2A inhibitors.
PIP4K2A is a type II lipid kinase that catalyzed the rate-limiting step of the conversion of phosphatidylinositol-5-phosphate (PI5P) into phosphatidylinositol 4,5-bisphosphate (PI4,5P2).
Chemometrics study that relates biological activity to physicochemical descriptors of a molecule and the prediction of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties ...in advance are important steps in drugs discovery. In this study, a chemometrics approach was employed on some molecules (inhibitors) of norepinephrine transporter to assess their inhibitory potencies, interactions with the receptor and predict their ADMET/pharmacokinetic properties for identification of novel antipsychotic drugs. The molecules were optimized by using density functional theory at the basis set of B
3
LYP/6-31G*. The genetic function algorithm technique was used to generate a statistically significant model with a good correlation coefficient
R
2
Train
= 0.952 Cross-validated coefficient
Q
2
cv
= 0.870, and adjusted squared correlation coefficient
R
2
adj
= 0.898. The molecular docking simulation using a neurotransmitter transporter receptor (PDB Code 2A65) revealed that three inhibitors (molecule No 38, 44 and 12) exhibited the highest binding affinity of − 10.3, − 9.9 and − 9.3 kcal/mol, respectively, were observed to inhibit the target by forming strong hydrogen bonds with hydrophobic interactions. The physicochemical and ADMET/pharmacokinetic properties result showed that these three molecules are orally bioavailable, high gastrointestinal absorption, good permeability and non-inhibitors of CYP3A4 and CYP2D6 except for molecule No 38. Also, Molecules No 38 and 44 proved to be non-substrate of P-glycoprotein and nontoxicity to a human ether-a-go-go-related gene with predicted hERG toxicity endpoints (pIC
50
< 6) and low ADMET_Risk (< 7.0). The results of this study would provide physicochemical and pharmacokinetics properties needed to identify potent antipsychotic drugs and other relevant information in drug discovery.
The paper describes the molecular docking study of the inhibition of human topoisomerase I (Top1), which is the molecular target of a diverse set of anticancer compounds such as glycinate, ...camptothecin, and its analogues. The reaction mechanisms involving their interaction with a transient Top1–DNA covalent complex inhibits the resealing of a single‐strand nick created by the enzyme to relieve superhelical tension in duplex DNA; this was confirmed using ICM‐Pro Molsoft program. Our research findings on this reaction indicate that its planner nature, the presence of some fragments on the lactone E‐ring, and the Pi–Pi interactions of the camptothecin drugs with DNA were directly responsible for its stable ternary complex with Top1. The molecular docking result of our study demonstrates that morpholinodoxorubicin (−32.835 kcal/mol), 9‐amino‐20‐RS‐camptothecin (−28.792 kcal), and camptothecin lysinate HCl (−28.224 kcal) best inhibit Top1 when compared with other National Service Center (NSC) compounds within our dataset. These compounds were further utilized in designing new potent antitumor compounds by attaching potent fragments to the lactone ring of the compounds. Most of these compounds were reported to be more active than the parent structure, some of which includes CLD‐12, CLD‐7, and CD‐9 with a binding affinity of −40.307, −36.743, and − 36.072 kcal/mol, respectively.
Camptothecin is chemical compound responsible for inhibting the topoisomerase enzyme in cancer cells. The interaction between the Topoisomerase 1 and camptothecin analouges were studied using a molecular docking program. The information retrived from this interaction was then applied to the structure‐based design of more potent camptothecin.
kinase is an essential therapeutic target in melanoma and other types of tumors. Because of its resistance to known inhibitors and the adverse effects of some identified inhibitors, investigation of ...new potent inhibitors is necessary.
In the present work, in silico strategies such as molecular docking simulation, pharmacokinetic evaluation, and density functional theory (DFT) computations were used to identify potential
inhibitors from a set of 72 anticancer compounds in the PubChem database.
Five top-ranked molecules (12, 15, 30, 31, and 35) with excellent docking scores (MolDock score ≥90 kcal mol
, Rerank score ≥60 kcal mol
) were selected. Several potential binding interactions were discovered between the molecules and
. The formation of H-bonds and hydrophobic interactions with essential residues of
suggested the high stability of these complexes. The selected compounds had excellent pharmacological properties according to the drug likeness rules (bioavailability) and pharmacokinetic properties. Similarly, the energy for the frontier molecular orbitals, such as the HOMO, LUMO, energy gap, and other reactivity parameters, was computed with DFT. The frontier molecular orbital surfaces and electrostatic potentials were investigated to demonstrate the charge-density distributions potentially associated with anticancer activity.
The identified compounds were found to be potent hit compounds for
inhibition with superior pharmacokinetic properties; therefore, they may be promising cancer drug candidates.
The anti-proliferative activities of Novel series of 2-(4-fluorophenyl) imidazol-5-ones against MCF-7 breast cancer cell line were explored via in-slico studies which includes Quantitative ...structure–activity relationship QSAR, molecular docking studies, designing new compounds, and analyzing the pharmacokinetics properties of the designed compounds. From the QSAR analysis, model number one emerged the best as seen from the arithmetic assessments of (R
2
) = 0.6981, (R
2
adj
) = 0.6433, (Q
2
) = 0.5460 and (R
2
pred
) of 0.5357. Model number one was used in designing new derivative compounds, with higher effectiveness against estrogen positive breast cancer (MCF-7 cell line). The Molecular docking studies between the derivatives and Polo-like kinases (Plk1) receptor proved that the derivatives of 2-(4-fluorophenyl) imidazol-5-ones bind tightly to the receptor, thou ligand 24 and 27 had the highest binding affinities of −8.8 and − 9.1 kcal/mol, which was found to be higher than Doxorubicin with a docking score of −8.0 kcal/mol. These new derivatives of 2-(4-fluorophenyl) imidazol-5-ones shall be excellent inhibitors against (plk1). The pharmacokinetics analysis performed on the new structures revealed that all the structures passed the test and also the Lipinski rule of five, and they could further proceed to pre-clinical tests. They both revealed a revolution in medicine for developing novel anti-breast cancer drugs against MCF-7 cell line.
Hydroxylated polychlorinated biphenyls (OH-PCBs), a series of toxic chemical compounds produced via biotic and abiotic transformation of polychlorinated biphenyls (PCBs), are known to cause endocrine ...disruption by interacting inappropriately with human nuclear receptors. Due to occurrence of high numbers of inactive OH-PCB congeners recorded in many experimental toxicity studies, it is pertinent to develop rapid and inexpensive QSAR models that can reliably predict the activities of OH-PCB congeners prior to experimental testing. Using a combination of genetic function approximation and multiple linear regression methods, a local QSAR model, consisting of six 2D descriptors (MATS1s, VE3_DzZ, VE1_Dzp, SpMin8_Bhv, SpMax5_Bhi, topoRadius) and two 3D descriptors (RDF95u, RDF45m), was developed from a training set of 44 OH-PCBs. Statistical parameters for fitting (
R
2
= 0.8902,
R
adj
2
= 0.8651,
s
= 0.2840), cross-validation (
Q
LOO
2
= 0.8201,
RMSE
CV
= 0.3242), and Y-randomization (
cR
p
2
= 0.8019) obtained for the developed QSAR model indicate that the model is reliable, robust, and provides good fit to the data in the training set. The results of external validation carried out on 20 OH-PCBs in the test set also indicate that the developed QSAR model possessed good external predictivity and can be used to predict the agonistic activities of untested OH-PCB congeners to constitutive androstane receptor.
An in-silico study was performed to investigate the anti-diabetic activities of 27 Oxadiazoles derivatives. The anti-diabetic compounds were optimized using Density Functional Theory (DFT) method ...utilizing B3LYP version with 6-31G∗ basis set. Genetic Function Algorithm (GFA) was used to build four models. Model 1 was chosen as the best model, assessed and found to be statistically significant with LOF = 0.030552, R2 = 0.9681, R2adj = 0.9567, Q2CV = 0.9364 and R2pred = 0.6969. The results of the molecular docking studies revealed that ligand 10, 13 and 15 have the highest docking scores of −9.9 kcal/mol among the co-ligands. This study has shown that the docking scores generated were in good agreement with the work reported by other researchers. The results of this study give room for designing new anti-diabetic compounds with better inhibitory activity against α-glucosidase, an enzyme that catalyzes the hydrolysis of carbohydrate to produce excess glucose.
Background
This research provides a comprehensive analysis of QSAR modeling performed on 25 aryl sulfonamide derivatives to predict their effective concentration (EC
50
) against H5N1 influenza A ...virus by using some numerical information derived from structural and chemical features (descriptors) of the compounds to generate a statistically significant model. Subsequently, the molecular docking simulations were done so as to determine the binding modes of some potent ligands in the dataset with the M2 proton channel protein of the H5N1 influenza A virus as the target.
Results
In building the QSAR model, the genetic algorithm task was employed in the variable selection of the descriptors which are used to form the multi-linear regression equation. The model with descriptors, RDF100m, nO, and RDF45p, showed satisfactory internal and external validation parameters (
R
2
train
= 0.72963,
R
2
adjusted
= 0.67169,
Q
2
cv
= 0.598,
R
pred
2
=
0.67295,
R
2
test
= 0.6860) which passed the model criteria of acceptability. Docking simulation results of the more potent compounds (ligands 2, 3, and 8) revealed the formation of hydrophobic and hydrogen bonds with the binding pockets of M2 protein of influenza A virus.
Conclusion
The results in this study can help to advance the research in designing (in silico design) and synthesis of more potent aryl sulfonamides derivatives against H5N1 influenza virus.
PDF
