Acetylcholinesterase (AChE) is one of the main drug targets for treating Alzheimer’s disease. This current study relies on multiple molecular modeling approaches to develop new potent inhibitors of AChE. We explored a 2D QSAR study using the statistical method of multiple linear regression based on a set of substituted 5-phenyl-1,3,4-oxadiazole and N-benzylpiperidine analogs, which were recently synthesized and proved their inhibitory activities against acetylcholinesterase (AChE). The molecular descriptors, polar surface area, dipole moment, and molecular weight are the key structural properties governing AChE inhibition activity. The MLR model was selected based on its statistical parameters: R² = 0.701, R²test = 0.76, Q²CV = 0.638, and RMSE = 0.336, demonstrating its predictive reliability. Randomization tests, VIF tests, and applicability domain tests were adopted to verify the model’s robustness. As a result, 11 new molecules were designed with higher anti-Alzheimer’s activities than the model molecule. We demonstrated their improved pharmacokinetic properties through an in silico ADMET study. A molecular docking study was conducted to explore their AChE inhibition mechanisms and binding affinities in the active site. The binding scores of compounds M1, M2, and M6 were (−12.6 kcal/mol), (−13 kcal/mol), and (−12.4 kcal/mol), respectively, which are higher than the standard inhibitor Donepezil with a binding score of (−10.8 kcal/mol). Molecular dynamics simulations over 100 ns were used to validate the molecular docking results, indicating that compounds M1 and M2 remain stable in the active site, confirming their potential as promising anti-AChE inhibitors.

Cholinesterase enzymes (BuChE and AChE) are privileged biological targets for the symptomatic treatment of Alzheimer's disease. Indeed, inhibition of cholinesterase enzymes has been proven to improve the neurotransmission mechanisms in Alzheimer's disease patients. In this investigation, we attempt to highlight new cholinesterase inhibitors from natural products. For this purpose, secondary metabolites (299 phytoconstituents) of twenty-eight Medicinal plants were virtually screened using molecular docking, pharmacokinetic and toxicological analysis. Ten phytoconstituents (L82, L86, L92, L121, L148, L187, L211, L221, L228) exhibited their high binding affinity with BuChE, and five phytoconstituents, namely L119, L147, L149, L192 and L193, exhibited their strong binding ability with AChE. Subsequently, these phytoconstituents were evaluated for their ADMET properties. As result, L221 is predicted to be highly bioavailable and readily absorbed by the human intestinal tract without significant toxicity concerns, making it suitable for oral administration. Crucially, it can penetrate the blood-brain barrier (BBB), allowing it to effectively reach the central nervous system. Molecular dynamics simulations and MM-PBSA analysis revealed that the best-screened phytoconstituent form thermodynamically favorable and stable complex with the BuChE binding site. The conducted investigations highlighted promising outcomes that can orient towards the rational development of effective Cholinesterase inhibitors.

Candida albicans and Aspergillus fumigatus are recognized as significant fungal pathogens, responsible for various human infections. The rapid emergence of drug-resistant strains among these fungi requires the identification and development of innovative antifungal therapies. We undertook a comprehensive screening of 297 naturally occurring compounds to address this challenge. Using computational docking techniques, we systematically analyzed the binding affinity of each compound to key proteins from Candida albicans (PDB ID: 1EAG) and Aspergillus fumigatus (PDB ID: 3DJE). This rigorous in silico examination aimed to unveil compounds that could potentially inhibit the activity of these fungal infections. This was followed by an ADMET analysis of the top-ranked compound, providing valuable insights into the pharmacokinetic properties and potential toxicological profiles. To further validate our findings, the molecular reactivity and stability were computed using the DFT calculation and molecular dynamics simulation, providing a deeper understanding of the stability and behavior of the top-ranking compounds in a biological environment. The outcomes of our study identified a subset of natural compounds that, based on our analysis, demonstrate notable potential as antifungal candidates. With further experimental validation, these compounds could pave the way for new therapeutic strategies against drug-resistant fungal pathogens.

Background: Skin cancer progression is driven by the dysregulation of key oncogenic signaling pathways, including EGFR, BRAF V600E, and TGF-β, which collectively promote tumor proliferation, invasion, and metastatic progression. Targeting these pathways using multitarget natural modulators represents a promising therapeutic strategy. Methods: In this study, forty-nine phytoconstituents from Cannabis sativa were evaluated using an integrated computational approach to explore their inhibitory potential against EGFR, BRAF V600E, and the TGF-β receptor. Molecular docking was performed to assess binding affinities and interaction profiles, followed by ADMET analysis to evaluate pharmacokinetic and safety properties. The top-ranked compounds were further investigated using 200 ns molecular dynamics simulations and MM-GBSA binding free energy calculations to assess the stability and strength of protein–ligand interactions. Results: Several phytoconstituents exhibited strong binding affinities toward the target proteins, formed stable interactions with key active-site residues, and demonstrated favorable pharmacokinetic profiles with acceptable safety characteristics. Molecular dynamics simulations confirmed the structural stability of the selected protein–ligand complexes, while MM-GBSA analysis supported their favorable binding energetics. Conclusions: These findings suggest that Cannabis sativa phytoconstituents may represent a promising source of multitarget modulators capable of attenuating EGFR, BRAF V600E, and TGF-β driven oncogenic signaling in skin cancer. This study provides a mechanistic framework that supports further in vitro validation and the development of cannabis-derived therapeutic candidates for targeted skin cancer management.

Mammalian target of rapamycin complex 1 (mTORC1) is an important and promising alternative biological target for the treatment of different types of cancer including breast, lung and renal cell carcinoma. This study contributed to the development of mathematical models highlighting the quantitative structure-activity relationship of a series of piperazine derivatives reported as mTORC1 inhibitors. Various molecular descriptors were calculated using Gaussian 09, Chemsketch, and ChemOffice software. The density funcional theory (DFT) method at the level B3LYP/6-31G+(d, p) was applied to determine the structural, electronic and energetic parameters associated with the studied molecules. The predictive ability of the built models, which is obtained by two methods (MLR and MNLR), showed that the built models are statistically significant. The QSAR modeling results revealed that the six molecular descriptors of lowest unoccupied molecular orbital energy (ELUMO), electrophilicity index (ω), molar refractivity (MR), aqueous solubility (Log S), topological polar surface area (PSA), and refractive index (n) significantly correlated to the biological inhibitory activity of piperazine derivatives. Using QSAR models and in silico pharmacokinetic profiles predictions, five new candidate compounds are selected as potential inhibitors against cancer.

Since its appearance in Wuhan in December 2019, finding ways to manage the COVID-19 pandemic has become the biggest challenge the world is facing. In this investigation, we used a quantitative structure-activity relationship (QSAR) study, an absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis, and computational molecular docking simulations to screen and assess the efficacy of 39 bioactive 9,10-dihydrophenanthrene analogues. The density functional theory (DFT) using the B3LYP/6-31G (d, p) level was used for the calculations of molecular descriptors, and principal component analysis (PCA) was used to eliminated redundant and non-significant descriptors. After that, statistically robust models were developed using the multiple linear regression (MLR) method. All the derived models were then subjected to thorough external and internal statistical validations, Y-randomization, and applicability domain analysis. These validations were carried out as per the Organisation for Economic Co-operation and Development (OECD) principles. The best built model was used to design new molecules that have good values of inhibitory activity against SARS-CoV-2. The pharmacokinetics properties were then determined using an ADMET analysis to weed out any that would be harmful to the human body or cause adverse effects. Through the use of computational molecular docking simulations, in silico research was conducted on the deigned compounds to forecast their SARS-CoV-2 activity and determine the stability of the evaluated ligands during their contacts with the proteins of the desired activity.