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Purine nucleoside phosphorylase (PNP) is one of the major enzymes in the purine salvage pathway. It is responsible for the elevation of deoxyguanosine, and thus considered as the potent target in T-cell lymphoma. The present study examined acyclovir, reported as a low-affinity PNP inhibitor, for the rational design of new acyclovir derivatives by incorporating halogens, hydroxyl, and bulky amino groups. The molecular actions of designed derivatives were investigated by employing density functional theory, molecular docking, and binding energy calculations. The results revealed that the newly designed compounds were highly stable and showed more affinity to PNP than the parent compound, acyclovir. The quantum mechanics and molecular docking studies suggested that modification of side chains with bulky polar groups provided better binding affinities than substitutions with halogens. The resultant derivatives have strong polar interactions like His257 and Tyr88. Furthermore, the designed derivatives were within the ideal range of ADMET (absorption, distribution, metabolism, elimination, and toxicity) analysis. Considering that, these findings recommend further validation of designed acyclovir derivatives in wet lab confirmatory analysis with the emphasis on the further improvements in the treatment of T-cell-mediated diseases.

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Background: Multi drug resistance (MDR) is known to defeat most chemotherapies as one of the main anticancer strategies. The role of overexpression or overactivation of ATP-binding cassette (ABC) transporters, especially P-glycoprotein (P-gp), in the development of chemotherapy has long been demonstrated. Salinispora is a marine actinomycete genus known for the production of novel bioactive metabolites.
Objectives: In this study, the potential of Salinispora derived metabolites as inhibitor of ATP-binding cassette (ABC) transports have been investigated using in-silico approaches.
Methods: Physicochemical, pharmacokinetic and drug likeness of the Salinispora derived metabolites have been analyzed using SwissADME server. This was accompanied by the employment of docking strategy to evaluate anti-MDR potential of the metabolites using P-gp, Breast Cancer Resistance Protein (BCRP) and Multidrug Resistance Protein 1 (MRP-1) as target proteins. 
Results: Nineteen metabolites were found to have demonstrated appropriate physicochemical, pharmacokinetic, and drug-likeness properties and were involved in the docking studies. Based on docking studies, saliniquinones, cyclomarazine, and cyanosporoside A demonstrated ABC transporters inhibitory potential.
Conclusion: Our results suggest that further in vivo and in vitro studies on anti-MDR effects of Salinispora-derived metabolites are warranted.

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Background: The use of pharmacological agents to synergistically target key enzymes associated with carbohydrate digestion (α-glucosidase) and the hypertension-related angiotensin-converting enzyme (ACE) are critical strategies for the management of type 2 diabetes (T2D) and its end-stage complications. Furthermore, aside from their blood pressure-lowering effect, ACE inhibitors (ACEIs) are important therapeutic agents for preventing diabetic complications, highlighting their synergistic renoprotective and antihypertensive effects in diabetic patients who are normotensive and hypertensive.
Objectives: We reviewed the safety and potent activity of phytochemicals discovered based on molecular docking and dynamics in recent years that could be used to treat T2D.
Methods: We surveyed recently in silico drug discovery findings on α-glucosidase and ACE retrieved from the PubMed database. Computational in silico ADMET meta-analysis was performed on 57 compounds that could potentially inhibit α-glucosidase or ACE.
Results: The review highlighted the fact that most hit compounds of α-glucosidase and ACE involving the use of molecular docking and molecular dynamics techniques are competitive and peptide inhibitors, respectively. Moreover, we found that most authors do not consider absorption distribution metabolism excretion toxicity (ADMET) studies on drug candidates, which is important in determining the safety profile of potent leads. Hence, we performed in silico ADMET meta-analysis of the reported compounds and found some inhibitors with an excellent pharmacological profile.
Conclusion: We propose that further studies be conducted on these promising leads to demonstrate their efficacy and safety in the treatment of T2D.

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Background: Plasma protein binding is a key component in drug therapy as it affects the pharmacokinetics and pharmacodynamics of drugs.
Objectives: This study aimed to predict the fraction of plasma protein binding.
Methods: A quantitative structure-activity relationship, convolutional neural network, and feed-forward neural network (QSAR-CNN-FFNN) methodology was used. CNN was used for feature selection, which is known as a difficult task in QSAR studies. The values of the descriptors acquired without the preprocessing procedures were rearranged into matrices, and features from a deep fully connected layer of a pre-trained CNN (ALEXNET) were extracted. Then, the latest features learned from the CNN layers were flattened out and passed through an FFNN to make predictions.
Results: The external accuracy of the validation set (Q2=0.945, RMSE=0.085) showed the performance of this methodology. Another extremely favorable circumstance of this method is that it does not take a lot of time (only a few minutes) compared to the QSAR-Wrapper-FFNN method (days of hard work and concentration) and it automatically gives us the characteristics that are the best representations of our input.
Conclusion: We can say that this model can be used to predict the fraction of human plasma protein binding for drugs that have not been tested to avoid chemical synthesis and reduce expansive laboratory tests.

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Background: In the spectrum of neurodegenerative conditions, Alzheimer disease (AD) stands out as the predominant contributor to dementia and behavioral alterations. Cholinesterase inhibitors (ChE-Is) represent the primary pharmaceutical category currently endorsed for addressing AD. 
Objectives: This investigation assessed the potential of selected test compounds to inhibit cholinesterase, specifically targeting acetylcholinesterase (AChE), through in silico approaches.
Methods: In this investigation, five test compounds—oxypertine, cinitapride, niaprazine, fenoverine, and clebopride—were identified and selected based on their electroshape resemblance to donepezil, utilizing the SwissSimilarity web server. Molecules with shapes similar to donepezil can fit into the AChE active site more snugly, facilitating similar interactions. AChE (PDB ID: 6U34) was sourced from the RCSB Protein Data Bank (PDB) and prepared for molecular docking with Discovery Studio 2020 software. Molecular docking was executed using PyRx, while visualization was performed with Discovery Studio 2020 software. Furthermore, the physicochemical properties (adhering to Lipinski’s rule of five), drug-likeness, and various parameters, encompassing absorption, distribution, metabolism, elimination, and toxicity (ADMET) profiles of the test compounds were scrutinized utilizing the SwissADME server. These findings were juxtaposed with those of donepezil, the standard drug.
Results: The docking scores revealed that fenoverine (-10.40 kcal/mol) exhibits greater potency against 6U34 compared to donepezil (-10.30 kcal/mol), while clebopride, cinitapride, niaprazine, and oxypertine (-9.50, -9.40, -9.20, and -9.10 kcal/mol, respectively) demonstrated lower potency against 6U34 relative to donepezil. All compounds adhered to Lipinski’s drug-likeness rules and displayed promising ADMET profiles suitable for therapeutic applications as ChE-Is.
Conclusion: Based on molecular docking and pharmacological parameters, fenoverine is a suitable alternative to donepezil. However, further studies using in vivo methods and other techniques are recommended to validate the results of this study.