2D-QSAR modeling, drug-likeness studies, ADMET prediction, and molecular docking for anti-lung cancer activity of 3-substituted-5-(phenylamino) indolone derivatives

Unraveling potent Glycyrrhiza glabra flavonoids as AKT1 inhibitors using network pharmacology and machine learning-assisted QSAR

Glycyrrhiza glabra (G. glabra) phytocompounds have been reported to interact with neurological targets, including those implicated in epilepsy, and may modulate epilepsy-related targets. While substantial evidence supports their potential antiepileptic effects, the underlying molecular mechanisms remain unclear. This study aims to elucidate the molecular mechanism of G. glabra phytocompounds by integrating network pharmacology and machine learning (ML)-based quantitative structure-activity relationship (QSAR) techniques. Network pharmacology analysis identified AKT1 as a key epilepsy-related target, and four ML-based 2D-QSAR models were developed using AKT1 inhibitors. The developed models underwent comprehensive validation, including internal and external validation, Y-randomization, statistical analysis, and applicability domain assessment to ensure robustness and reliability. Among them, the Multilayer Perceptron (MLP) model excelled as the most robust and demonstrated superior predictive ability with a correlation coefficient r2training = 0.95, r2test = 0.84, and cross-validation coefficient q2 = 0.72. The MLP model accurately predicted pIC50 values of phytoflavonoids, leading to the identification of 19 active molecules through the activity atlas model. ADME and drug-likeliness screening narrowed the selection to eleven promising compounds for further docking analysis. Molecular docking highlighted glabranin and 3'-hydroxy-4'-O-methylglabridin as top lead compounds with a binding energy of - 5.75 and - 5.37 kcal/mol, respectively. Additionally, 400 ns molecular dynamics simulation confirmed the structural and conformational stability of the glabranin-AKT1 complex, further reinforced by Principal Component Analysis, free energy landscapes, and Molecular Mechanics Poisson-Boltzmann/Generalized Born Surface Area. Collectively, these findings underscore the potential of G. glabra phytocompounds as promising antiepileptic candidates, paving the way for future advancements in this field.

In silico design of novel pyridazine derivatives as balanced multifunctional agents against Alzheimer's disease

Alzheimer's disease (AD) necessitates innovative therapeutic approaches that target its multifaceted pathology. This study investigates a series of 2-aminoalkyl-6-(2-hydroxyphenyl)pyridazin-3(2H)-one derivatives as potential multi-target ligands for AD, aiming to simultaneously inhibit acetylcholinesterase (AChE) and amyloid-beta (Aβ) aggregation. To assess the therapeutic potential of these compounds, we employed a comprehensive computational approach, incorporating 2D-QSAR modeling, molecular dynamics simulations, molecular docking, and ADMET property analysis. Based on these analyses, we designed 13 novel pyridazine derivatives exhibiting favorable interactions with key AD-related proteins, enhanced dynamic stability within protein binding sites, and adherence to established drug-likeness principles. Notably, these compounds demonstrated promising oral absorption (96%) and exhibited no significant toxicity in preliminary assessments. These results indicate that the novel pyridazine derivatives warrant further investigation as promising multifunctional agents for the treatment of Alzheimer's disease.

Research on the optimization model of anti-breast cancer candidate drugs based on machine learning

Breast cancer is one of the most common malignancies among women globally, with its incidence rate continuously increasing, posing a serious threat to women's health. Although current treatments, such as drugs targeting estrogen receptor alpha (ERα), have extended patient survival, issues such as drug resistance and severe side effects remain widespread. This study proposes a machine learning-based optimization model for anti-breast cancer candidate drugs, aimed at enhancing biological activity and optimizing ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties through multi-objective optimization. Initially, grey relational analysis and Spearman correlation analysis were performed on the molecular descriptors of 1,974 compounds, identifying 91 key descriptors. A Random Forest model combined with Shapley Additive Explanations (SHAP) values was then used to further select the top 20 descriptors with the greatest impact on biological activity. The constructed Quantitative Structure-Activity Relationship (QSAR) model, using algorithms such as LightGBM, Random Forest, and XGBoost, achieved an R2 value of 0.743 for biological activity prediction, demonstrating strong predictive performance. Additionally, a multi-model fusion strategy and Particle Swarm Optimization (PSO) algorithm were employed to optimize both biological activity and ADMET properties, thereby improving the prediction of Caco-2, CYP3A4, hERG, HOB, and MN properties. For example, the best model for predicting Caco-2 achieved an F1 score of 0.8905, while the model for predicting CYP3A4 reached an F1 score of 0.9733. This multi-objective optimization model provides a novel and efficient tool for drug development, offering significant improvements in both biological activity and pharmacokinetic properties, with practical implications for the optimization of future anti-breast cancer drugs.

Design and evaluation of novel triazole derivatives as potential anti-gout inhibitors: a comprehensive molecular modeling study

Introduction

Gout is the most common inflammatory arthritis, characterized by hyperuricemia, tophus formation, joint disease, and kidney stones. Uric acid, the final byproduct of purine catabolism, is eliminated via the kidneys and digestive system. Xanthine oxidase (XO) catalyzes the conversion of hypoxanthine and xanthine into uric acid, making XO inhibitors crucial for treating hyperuricemia and gout. Currently, three XO inhibitors are clinically used, showing significant efficacy. A molecular modeling study on triazole derivatives aims to identify novel XO inhibitors using 3D-QSAR, molecular docking, MD simulations, ADMET analysis, and DFT calculations. These computational approaches facilitate drug discovery while reducing research costs.

Methods

Our work focuses on a series of synthesized anti-xanthine oxidase inhibitors, aiming to develop new inhibitors. A computational study was carried out to identify the xanthine oxidase inhibitory structural features of a series of triazole inhibitors using computational method.

Results

A model based on CoMFA and CoMSIA/SEA has been built to predict new triazole derivatives.

Discussion

The optimal model established from CoMFA and CoMSIA/SEA was successfully evaluated for its predictive capability. Visualization of the contour maps of both models showed that modifying the substituents plays a key role in enhancing the biological activity of anti-gout inhibitors. Molecular docking results for complexes N°8-3NVY and N°22-3NVY showed scores of -7.22 kcal/mol and -8.36 kcal/mol, respectively, indicating substantial affinity for the enzyme. Complex N°8-3NVY forms two hydrogen bonds with SER 69 and ASN 71, three alkyl bonds with ALA 70, LEU 74, and ALA 75, and one Pi-Pi T-shaped bond with PHE 68. Complex N°22-3NVY forms three hydrogen bonds with HIS 99, ARG 29, and ILE 91, and one halogen bond with LEU 128 at 3.60 Å. A MD study revealed that the N°22-3NVY complex remained highly stable throughout the simulation. Therefore, we proposed six new molecules, their anti-gout inhibitory activities were predicted using two models, and they were evaluated for Lipinski's rule, and ADMET properties. The results show that both Pred 4 and Pred 5 have better pharmacokinetic properties than the height potent molecule in the studied series, making these two compounds valuable candidates for new anti-gout drugs. Subsequently, using DFT study to evaluate the chemical reactivity properties of these two proposed compounds, the energy gap results revealed that both molecules exhibit moderate chemical stability and reactivity.

Anticholinesterase and carbonic anhydrase inhibitory activities of natural carnosic acid derivatives: A comprehensive in vitro and in silico study

This study investigates the anticholinesterase (acetylcholinesterase [AChE] and butyrylcholinesterase [BChE]) and carbonic anhydrase (CAI and CAII) inhibitory activities of carnosic acid and its natural derivatives, including carnosol, rosmanol, 7-methoxy-rosmanol, 12-methoxy-carnosic acid, and isorosmanol. Among the tested compounds, rosmanol demonstrated exceptional potency, with IC50 values of 0.73 nM for AChE and 0.75 nM for BChE, significantly outperforming tacrine. Rosmanol also exhibited remarkable inhibition of CA I (IC50 = 0.21 nM), surpassing acetazolamide by over 450-fold, and moderate inhibition of CAII. Molecular docking and molecular mechanics generalized born surface area (MM-GBSA) studies revealed strong binding affinities for rosmanol, with docking scores of -11.757 kcal/mol (AChE) and -11.465 kcal/mol (BChE). The MM-GBSA binding free energy calculations further confirmed stable interactions for CA I (-63.24 kcal/mol) and AChE (-60.09 kcal/mol). Molecular dynamics simulations over 50 ns showed stable enzyme-ligand complexes, particularly for AChE and BChE (root mean square deviation ~1.5 Å), with key residues identified as crucial for stabilization. Other derivatives also displayed significant inhibitory activities, suggesting their potential as secondary leads. The ADMET analysis showed favorable pharmacokinetics and rosmanol emerged as a promising candidate. This comprehensive study highlights rosmanol as a multitarget therapeutic agent with potent anticholinesterase and CA inhibitory properties, offering promise for treating neurodegenerative and metabolic disorders.

An in-depth study of indolone derivatives as potential lung cancer treatment

Lung cancer is a type of cancer that begins in the lungs and is one of the leading causes of cancer-related deaths worldwide. Herein an attempt to explore the relationship between the properties of indolone derivatives and their anticancer activity was investigated, implementing in silico approaches. Four indolone derivatives with the highest anticancer potential were selected to evaluate their pharmacological properties. The ADMET analysis revealed that these compounds exhibited favourable drug-like properties, meeting nearly all the key pharmacological criteria required for potential therapeutic agents. Molecular docking studies of the most active compounds revealed strong interactions with critical amino acid residues in the PDK1 receptor's binding site, underscoring their potential as effective PDK1 inhibitors. In addition, 200 ns molecular dynamics (MD) simulations of two R and S configurations validated the stability of the ligand-receptor complexes, with minimal structural deviations observed throughout the simulation period. These comprehensive results highlight the potential of the selected indolone derivatives as viable drug candidates and provide a solid foundation for future optimization efforts aimed at developing novel PDK1 inhibitors for cancer therapy.

Biophysical insight into the interaction mechanism of 4-bromo-N-(thiazol-2-yl)benzenesulfonamide and human serum albumin using multi-spectroscopic and computational studies

4-Bromo-N-(thiazol-2-yl)benzenesulfonamide (1) is enriched with bioactive components and is highlighted for its pharmacological properties. However, its pharmacokinetic characteristics are yet to be reported. The interaction of compound 1 with carrier proteins in the bloodstream is an important factor that affects its potential therapeutic efficacy. This study aimed to elucidate the pharmacokinetic mechanisms of compound 1 in relation to human serum albumin (HSA) using multi-spectroscopic and computational techniques. Its predicted drug-like properties revealed no mutagenicity, although potential hepatotoxicity and interactions with certain cytochrome P450 enzymes were observed. Spectroscopic analyses extensively provided the interaction between HSA and 1 through a static fluorescence quenching mechanism with spontaneous hydrophobic interactions and hydrogen bonding. The binding constant of the HSA‒1 complex was relatively moderate to strong at a level of 106 M-1. Various spectroscopic techniques including ultraviolet-visible, Fourier transform infrared, and circular dichroism spectroscopies indicated that its binding induced alteration in the α-helix content of HSA. Competitive binding and molecular docking studies designated the preferential binding of 1 to sub-structural domain IIA binding site I of HSA. Molecular dynamic simulations further illustrated the formation of a stable complex between 1 and HSA, accompanied by conformational changes in the protein. Importantly, esterase capacity of the HSA‒1 complex increased compared to the free HSA. Therefore, elucidation of the HSA‒1 binding mechanism provides valuable insights into the pharmacokinetics, suggesting potential benefits for the further development of 1 as a therapeutic agent.

Green Synthesis of Zinc Oxide Nanoparticles for Tetracycline Adsorption: Experimental Insights and DFT Study

An eco-friendly approach was used to fabricate zinc oxide nanoparticles (ZnO NPs) using thyme, Thymus vulgaris L., leaf extract. The produced ZnO nanoparticles were characterized by XRD and SEM analysis. The ZnO NPs showed remarkable adsorption efficiency for tetracycline (TC) from water systems, with a maximum removal rate of 95% under optimal conditions (10 ppm, 0.10 g of ZnO NPs, pH 8.5, and 30 min at 25 °C). The adsorption kinetics followed the pseudo-2nd-order model, and the adsorption process fitted the Temkin isotherm model. The process was spontaneous, endothermic, and primarily chemisorptive. Quantum chemistry calculations, utilizing electrostatic potential maps and HOMO-LUMO gap analysis, have confirmed the stability of the TC clusters. This study suggests that green synthesis using plant extracts presents an opportunity to generate nanoparticles with properties suitable for real-world applications.

Identification of novel DNA gyrase inhibitor by combined pharmacophore modeling, QSAR analysis, molecular docking, molecular dynamics, ADMET and DFT approaches

DNA gyrase, an ATP-dependent enzyme, plays a critical role in DNA replication, transcription, and recombination in Mycobacterium tuberculosis (MTB). While fluoroquinolones are effective antibacterial agents targeting DNA gyrase, their clinical use is often limited due to side effects and the emergence of bacterial resistance. In this study, we developed a quantitative structure-activity relationship (QSAR) model to predict the anti-tubercular activity of Quinoline-Aminopiperidine derivatives targeting the DNA gyrase enzyme, using a dataset of 48 compounds obtained from the literature. The QSAR model was validated using both internal and external validation metrics. Model 4, the best predictive model, demonstrated a strong fit with an R² of 0.8393, an adjusted R² (R²adj) of 0.8010, and a lack of fit (LOF) parameter of 0.0626. The QSAR results revealed that DNA gyrase inhibition is significantly influenced by factors such as partition coefficient, molecular flexibility, hydrogen bonding potential, and the presence of fluorine atoms. Twelve quinoline-aminopiperidine derivatives were designed, and their anti-tubercular activity was predicted using QSAR model-4. These compounds were further assessed for pharmacokinetic properties, toxicity, and binding affinity to DNA gyrase. Pharmacophore modeling was also performed and validated using MOE software. The final pharmacophore model includes the features of two aromatic hydrophobic features, one hydrogen bond acceptor, and one hydrogen bond donor. The results indicated that designed compounds QA-3 and dataset compounds C-34 exhibit favorable drug-likeness properties. Molecular dynamics simulations confirmed the stable binding of compounds QA-3 and C-34 to the DNA gyrase protein, highlighting their potential as promising anti-tubercular agents. The developed QSAR Model-4 will facilitate the prediction of anti-tubercular activity in Quinoline-Aminopiperidine derivatives.

Integrated computational approaches for identification of potent pyrazole-based glycogen synthase kinase-3β (GSK-3β) inhibitors: 3D-QSAR, virtual screening, docking, MM/GBSA, EC, MD simulation studies

Glycogen synthase kinase-3β (GSK-3β) has emerged as a crucial target due to its substantial contribution in various cellular processes. Dysfunctional GSK-3β activity can lead to ion channel disturbances, sustain abnormal excitability, and contribute to the pathogenesis of epilepsy and other GSK-3β-related disorders. A set of 82 pyrazole analogs was utilized to study its structural features using a three-dimensional quantitative structure-activity relationship (3D-QSAR), virtual screening, molecular docking, and molecular dynamics. The QSAR model, validated using internal and external methods, demonstrated robustness with a high correlation coefficient r2training = 0.99, cross-validation coefficient q2 = 0.79, r2test = 0.69, and r2external = 0.74. The "Average of Actives" in the Activity Atlas model identified 17 molecules as active. Subsequent pharmacophore-based virtual screening of 17 actives yielded 70 compounds, which were selected as the prediction set to determine the potential GSK-3β inhibitors. Docking studies pinpointed compound P66 as the promising lead compound, with a docking score of - 10.555 kcal/mol. These findings were further supported by electrostatic potential (ESP), electrostatic complementarity (EC), and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) analyses. Furthermore, a 500 ns molecular dynamics (MD) simulation confirmed the structural and conformational stability of the lead complex throughout the simulation period. As a result, this study suggests that compound P66 holds the potential to be a potent lead candidate for the inhibition of GSK-3β, offering a novel therapeutic approach for GSK-3β related disorders, including epilepsy.

First report on exploration of structural features of natural compounds (NPACT database) for anti-breast cancer activity (MCF-7): QSAR-based virtual screening, molecular docking, ADMET, MD simulation, and DFT studies

Due to the high toxicity, poor efficacy and resistance associated with current anti-breast cancer drugs, there's growing interest in natural products (NPs) for their potential anti-cancer properties. Computational modelling of NPs to identify key structural features can aid in developing novel natural inhibitors. In this study, we developed statistically significant QSAR models based on NPs from the NPACT database, which have shown potential anticancer activity against the MCF-7 cancer cell lines. All the developed QSAR models were statistically robust, meeting both internal (R 2  = 0.666-0.669, R 2 adj  = 0.657-0.660, Q 2 Loo  = 0.636-0.638) and external (Q 2 F n  = 0.686-0.714, CCC ext = 0.830-0.847) validation criteria. Consequently, they were utilized to virtually screen a series of NPs from the COCONUT database in the search for novel natural inhibitors. Molecular docking studies were conducted on the identified compounds against the human HER2 protein (PDB ID: 3PP0), which is a crucial target in breast cancer. Molecular docking analysis demonstrated that compounds 4608 and 2710 achieved the highest docking scores, with CDOCKER interaction energies of -72.67 kcal/mol and - 72.63 kcal/mol respectively. Compounds 4608 and 2710 were identified as the most promising candidates upon performing triplicate 100 ns MD simulation study using the CHARMM36 force field. DFT studies was performed to evaluate their stability and reactivity as potential drug molecules. This research contributes to the development of new natural inhibitors for breast cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00266-5.

An in-silico investigation based on molecular simulations of novel and potential brain-penetrant GluN2B NMDA receptor antagonists as anti-stroke therapeutic agents

GluN2B-induced activation of NMDA receptors plays a key function in central nervous system (CNS) disorders, including Parkinson, Alzheimer, and stroke, as it is strongly involved in excitotoxicity, which makes selective NMDA receptor antagonists one of the potential therapeutic agents for the treatment of neurodegenerative diseases, especially stroke. The present study aims to examine a structural family of thirty brain-penetrating GluN2B N-methyl-D-aspartate (NMDA) receptor antagonists, using virtual computer-assisted drug design (CADD) to discover highly candidate drugs for ischemic strokes. Initially, the physicochemical and ADMET pharmacokinetic properties confirmed that C13 and C22 compounds were predicted as non-toxic inhibitors of CYP2D6 and CYP3A4 cytochromes, with human intestinal absorption (HIA) exceeding 90%, and designed to be as efficient central nervous system (CNS) agents due to the highest probability to cross the blood-brain barrier (BBB). Compared to ifenprodil, a co-crystallized ligand complexed with the transport protein encoded as 3QEL.pdb, we have noticed that C13 and C22 chemical compounds were defined by good ADME-Toxicity profiles, meeting Lipinski, Veber, Egan, Ghose, and Muegge rules. The molecular docking results indicated that C22 and C13 ligands react specifically with the amino acid residues of the NMDA receptor subunit GluN1 and GluN2B. These intermolecular interactions produced between the candidate drugs and the targeted protein in the B chain remain stable over 200 nanoseconds of molecular dynamics simulation time. In conclusion, C22 and C13 ligands are highly recommended as anti-stroke therapeutic drugs due to their safety and molecular stability towards NMDA receptors.Communicated by Ramaswamy H. Sarma.

In silico insights into the design of novel NR2B-selective NMDA receptor antagonists: QSAR modeling, ADME-toxicity predictions, molecular docking, and molecular dynamics investigations

Based on a structural family of thirty-two NR2B-selective N-Methyl-D-Aspartate receptor (NMDAR) antagonists, two phenylpiperazine derivatives labeled C37 and C39 were conceived thanks to molecular modeling techniques, as novel NMDAR inhibitors exhibiting the highest analgesic activities (of pIC50 order) against neuropathic pain, with excellent ADME-toxicity profiles, and good levels of molecular stability towards the targeted protein of NMDA receptor. Initially, the quantitative structure-activity relationships (QSARs) models were developed using multiple linear regression (MLR), partial least square regression (PLSR), multiple non-linear regression (MNLR), and artificial neural network (ANN) techniques, revealing that analgesic activity was strongly correlated with dipole moment, octanol/water partition coefficient, Oxygen mass percentage, electronegativity, and energy of the lowest unoccupied molecular orbital, whose the correlation coefficients of generated models were: 0.860, 0.758, 0.885 and 0.977, respectively. The predictive capacity of each model was evaluated by an external validation with correlation coefficients of 0.703, 0.851, 0.778, and 0.981 respectively, followed by a cross-validation technique with the leave-one-out procedure (CVLOO) with Q2cv of 0.785, more than Y-randomization test, and applicability domain (AD), in addition to Fisher's and Student's statistical tests. Thereafter, ten novel molecules were designed based on MLR QSAR model, then predicted with their ADME-Toxicity profiles and subsequently examined for their similarity to the drug candidates. Finally, two of the most active compounds (C37 and C39) were chosen for molecular docking and molecular dynamics (MD) investigations during 100 ns of MD simulation time in complex with the targeted protein of NMDA receptor (5EWJ.pdb).

QSAR, ADMET, molecular docking, and dynamics studies of 1,2,4-triazine-3(2H)-one derivatives as tubulin inhibitors for breast cancer therapy

Breast cancer remains a leading cause of cancer-related deaths among women globally, necessitating the development of more effective therapeutic agents with minimal side effects. This study explores novel 1,2,4-triazine-3(2H)-one derivatives as potential inhibitors of Tubulin, a pivotal protein in cancer cell division, highlighting a targeted approach in cancer therapy. Using an integrated computational approach, we combined quantitative structure-activity relationship (QSAR) modeling, ADMET profiling, molecular docking, and molecular dynamics simulations to evaluate and predict the efficacy and stability of these compounds. Our QSAR models, developed through rigorous statistical analysis, revealed that descriptors such as absolute electronegativity and water solubility significantly influence inhibitory activity, achieving a predictive accuracy (R2) of 0.849. Molecular docking studies identified compounds with high binding affinities, particularly Pred28, which exhibited the best docking score of - 9.6 kcal/mol. Molecular dynamics simulations conducted over 100 ns provided further insights into the stability of these interactions. Pred28 demonstrated notable stability, with the lowest root mean square deviation (RMSD) of 0.29 nm and root mean square fluctuation (RMSF) values indicative of a tightly bound conformation to Tubulin. The novelty of this work lies in its methodological rigor and the integration of multiple advanced computational techniques to pinpoint compounds with promising therapeutic potential. Our findings advance the current understanding of Tubulin inhibitors and open avenues for the synthesis and experimental validation of these compounds, aiming to offer new solutions for breast cancer treatment.

Unveiling potent inhibitors for schistosomiasis through ligand-based drug design, molecular docking, molecular dynamics simulations and pharmacokinetics predictions

Schistosomiasis is a neglected tropical disease which imposes a considerable and enduring impact on affected regions, leading to persistent morbidity, hindering child development, diminishing productivity, and imposing economic burdens. Due to the emergence of drug resistance and limited management options, there is need to develop additional effective inhibitors for schistosomiasis. In view of this, quantitative structure-activity relationship studies, molecular docking, molecular dynamics simulations, drug-likeness and pharmacokinetics predictions were applied to 39 Schistosoma mansoni Thioredoxin Glutathione Reductase (SmTGR) inhibitors. The chosen QSAR model demonstrated robust statistical parameters, including an R2 of 0.798, R2adj of 0.767, Q2cv of 0.681, LOF of 0.930, R2test of 0.776, and cR2p of 0.746, confirming its reliability. The most active derivative (compound 40) was identified as a lead candidate for the development of new potential non-covalent inhibitors through ligand-based design. Subsequently, 12 novel compounds (40a-40l) were designed with enhanced anti-schistosomiasis activity and binding affinity. Molecular docking studies revealed strong and stable interactions, including hydrogen bonding, between the designed compounds and the target receptor. Molecular dynamics simulations over 100 nanoseconds and MM-PBSA free binding energy (ΔGbind) calculations validated the stability of the two best-designed molecules. Furthermore, drug-likeness and pharmacokinetics prediction analyses affirmed the potential of these designed compounds, suggesting their promise as innovative agents for the treatment of schistosomiasis.

Exploring isoindolin-1-ones as potential CDK7 inhibitors using cheminformatic tools

In women who die from cancer, breast cancer is the most common cause of death. The development of small molecular scaffolds as specific Cyclin-dependent kinase (CDK) inhibitors is a promising strategy in the discovery of anti-breast cancer drugs. Isoindolin-1-ones are heterocyclic compounds with useful therapeutic properties. In this study, a library of 48 isoindolinones has been virtually screened by molecular docking that showed high binding affinity up to - 10.1 kcal/mol and conventional hydrogen bonding interactions with active amino acid residues of CDK7. The molecular dynamics simulation (MDS), fragment molecular orbital (FMO), density functional theory (DFT), and pharmacokinetics studies of the best two docked scored ligands 7 and 14 have been studied. Examining the ligand root mean square deviation and hydrogen bonding occupancy of the 100 ns MDS trajectory, both ligands 7 and 14 showed docked pose stability. FMO calculations displayed that LYS139 and LYS41 are majorly contributing to the binding interactions with ligands 7 and 14 in the docked poses. DFT studies of ligands 7 and 14 showed high values of global softness and low values of global hardness and chemical potential thus displaying chemically reactive soft molecules and this influences their anti-cancer activity. Our hits exhibited superior qualities to known CDK7 inhibitors, according to the comprehensive pharmacokinetic parameters that were predicted. The results indicate that isoindolin-1-one moieties are good candidates for anti-cancer action and could serve as effective CDK7 inhibitors.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00225-0.

3D computer modeling of inhibitors targeting the MCF-7 breast cancer cell line

This study focused on developing new inhibitors for the MCF-7 cell line to contribute to our understanding of breast cancer biology and various experimental techniques. 3D QSAR modeling was used to design new tetrahydrobenzo[4, 5]thieno[2, 3-d]pyrimidine derivatives with good characteristics. Two robust 3D-QSAR models were developed, and their predictive capacities were confirmed through high correlations [CoMFA (Q2 = 0.62, R 2 = 0.90) and CoMSIA (Q2 = 0.71, R 2 = 0.88)] via external validations (R2 ext = 0.90 and R2 ext = 0.91, respectively). These successful evaluations confirm the potential of the models to provide reliable predictions. Six candidate inhibitors were discovered, and two new inhibitors were developed in silico using computational methods. The ADME-Tox properties and pharmacokinetic characteristics of the new derivatives were evaluated carefully. The interactions between the new tetrahydrobenzo[4, 5]thieno[2, 3-d]pyrimidine derivatives and the protein ERα (PDB code: 4XO6) were highlighted by molecular docking. Additionally, MM/GBSA calculations and molecular dynamics simulations provided interesting information on the binding stabilities between the complexes. The pharmaceutical characteristics, interactions with protein, and stabilities of the inhibitors were examined using various methods, including molecular docking and molecular dynamics simulations over 100 ns, binding free energy calculations, and ADME-Tox predictions, and compared with the FDA-approved drug capivasertib. The findings indicate that the inhibitors exhibit significant binding affinities, robust stabilities, and desirable pharmaceutical characteristics. These newly developed compounds, which act as inhibitors to mitigate breast cancer, therefore possess considerable potential as prospective drug candidates.

Design of potential anti-cancer agents as COX-2 inhibitors, using 3D-QSAR modeling, molecular docking, oral bioavailability proprieties, and molecular dynamics simulation

Modeling the structural properties of novel morpholine-bearing 1, 5-diaryl-diazole derivatives as potent COX-2 inhibitor, two proposed models based on CoMFA and CoMSIA were evaluated by external and internal validation methods. Partial least squares analysis produced statistically significant models with Q 2 values of 0.668 and 0.652 for CoMFA and CoMSIA, respectively, and also a significant non-validated correlation coefficient R² with values of 0.882 and 0.878 for CoMFA and CoMSIA, respectively. Both models met the requirements of Golbraikh and Tropsha, which means that both models are consistent with all validation techniques. Analysis of the CoMFA and CoMSIA contribution maps and molecular docking revealed that the R1 substituent has a very significant effect on their biological activity. The most active molecules were evaluated for their thermodynamic stability by performing MD simulations for 100 ns; it was revealed that the designed macromolecular ligand complex with 3LN1 protein exhibits a high degree of structural and conformational stability. Based on these results, we predicted newly designed compounds, which have acceptable oral bioavailability properties and would have high synthetic accessibility.

QSAR modelling, molecular docking, molecular dynamic and ADMET prediction of pyrrolopyrimidine derivatives as novel Bruton's tyrosine kinase (BTK) inhibitors

In recent years, there has been a focus on developing and discovering novel Bruton's tyrosine kinase (BTK) inhibitors, as they offer an effective treatment strategy for B-cell malignancies. BTK plays a crucial role in B cell receptor (BCR)-mediated activation and proliferation by regulating downstream factors such as the NF-κB and MAP kinase pathways. To address this challenge and propose potential therapeutic options for B-cell lymphomas, researchers conducted 2D-QSAR and ADMET studies on pyrrolopyrimidine derivatives that act as inhibitors of the BCR site in cytochrome b. These studies aim to improve and identify new compounds that could serve as more potent potential BTK inhibitors, which would lead to the identification of new drug candidates in this field. In our study, we used 2D-QSAR (multiple linear regression, multiple nonlinear regression, and artificial neural networks), molecular docking, molecular dynamics, and ADMET properties to investigate the potential of 35 pyrrolopyrimidine derivatives as BTK inhibitors. A molecular docking study and molecular dynamics simulations of molecule 13 over 10 ns revealed that it establishes multiple hydrogen bonds with several residues and exhibits frequent stability throughout the simulation period. Based on the results obtained by molecular modeling, we proposed six new compounds (Pred1, Pred2, Pred3, Pred4, Pred5, and Pred6) with highly significant predicted activity by MLR models. A study based on the in silico evaluation of the predicted ADMET properties of the new candidate molecules is strongly recommended to classify these molecules as promising candidates for new anticancer agents specifically designed to target Bruton's tyrosine kinase (BTK) inhibition.

A combined in silico approaches of 2D-QSAR, molecular docking, molecular dynamics and ADMET prediction of anti-cancer inhibitor activity for actinonin derivatives

Inhibition of human mitochondrial peptide deformylase (HsPDF) plays a major role in reducing growth, proliferation, and cellular cancer survival. In this work, a series of 32 actinonin derivatives for HsPDF (PDB: 3G5K) inhibitor's anticancer activity was computationally analyzed for the first time, using an in silico study considering 2D-QSAR modeling, and molecular docking studies, and validated by molecular dynamics and ADMET properties. The results of multilinear regression (MLR) and artificial neural networks (ANN) statistical analysis reveal a good correlation between pIC50 activity and the seven (7) descriptors. The developed models were highly significant with cross-validation, the Y-randomization test and their applicability range. In addition, all considered data sets show that the AC30 compound, exhibits the best binding affinity (docking score = -212.074 kcal/mol and H-bonding energy = -15.879 kcal/mol). Furthermore, molecular dynamics simulations were performed at 500 ns, confirming the stability of the studied complexes under physiological conditions and validating the molecular docking results. Five selected actinonin derivatives (AC1, AC8, AC15, AC18 and AC30), exhibiting best docking score, were rationalized as potential leads for HsPDF inhibition, in well agreement with experimental outcomes. Furthermore, based on the in silico study, new six molecules (AC32, AC33, AC34, AC35, AC36 and AC37) were suggested as HsPDF inhibition candidates, which would be combined with in-vitro and in-vivo studies to perspective validation of their anticancer activity. Indeed, the ADMET predictions indicate that these six new ligands have demonstrated a fairly good drug-likeness profile.

Quantitative structure activity relationship (QSAR) modeling study of some novel thiazolidine 4-one derivatives as potent anti-tubercular agents

This study aims to develop a QSAR model for Antitubercular activity. The quantitative structure-activity relationship (QSAR) approach predicted the thiazolidine-4-ones derivative's Antitubercular activity. For the QSAR study, 53 molecules with Antitubercular activity on H37Rv were collected from the literature. Compound structures were drawn by ACD/Labs ChemSketch. The energy minimization of the 2D structure was done using the MM2 force field in Chem3D pro. PaDEL Descriptor software was used to construct the molecular descriptors. QSARINS software was used in this work to develop the 2D QSAR model. A series of thiazolidine 4-one with MIC data were taken from the literature to develop the QSAR model. These compounds were split into a training set (43 compounds) and a test set (10 compounds). The PaDEL software calculated 2300 descriptors for this series of thiazolidine 4-one derivatives. The best predictive Model 4, which has R2 of 0.9092, R2adj of 0.8950 and LOF parameter of 0.0289 identify a preferred fit. The QSAR study resulted in a stable, predictive, and robust model representing the original dataset. In the QSAR equation, the molecular descriptor of MLFER_S, GATSe2, Shal, and EstateVSA 6 positively correlated with Antitubercular activity. While the SpMAD_Dzs 6 is negatively correlated with Antitubercular activity. The high polarizability, Electronegativities, Surface area contributions and number of Halogen atoms in the thiazolidine 4-one derivatives will increase the Antitubercular activity.

QSAR, ADME-Tox, molecular docking and molecular dynamics simulations of novel selective glycine transporter type 1 inhibitors with memory enhancing properties

A structural class of forty glycine transporter type 1 (GlyT1) inhibitors, was examined using molecular modeling techniques. The quantitative structure-activity relationships (QSAR) technology confirmed that human GlyT1 activity is strongly and significantly affected by constitutional, geometrical, physicochemical and topological descriptors. ADME-Tox in-silico pharmacokinetics revealed that L28 and L30 ligands were predicted as non-toxic inhibitors with a good ADME profile and the highest probability to penetrate the central nervous system (CNS). Molecular docking results indicated that the predicted inhibitors block GlyT1, reacting specifically with Phe319, Phe325, Tyr123, Tyr 124, Arg52, Asp475, Ala117, Ala479, Ile116 and Ile483 amino acids of the dopamine transporter (DAT) membrane protein. These results were qualified and strengthened using molecular dynamics (MD) study, which affirmed that the established intermolecular interactions for (L28, L30-DAT protein) complexes remain perfectly stable along 50 ns of MD simulation time. Therefore, they could be strongly recommended as therapeutics in medicine to improve memory performance.

Design of novel anti-cancer drugs targeting TRKs inhibitors based 3D QSAR, molecular docking and molecular dynamics simulation

Tropomyosin receptor kinase (TRK) enzymes are responsible for different types of tumors caused by neurotrophic tyrosine receptor kinase gene fusion and have been identified as an effective target for anticancer therapy. The study of the mechanism between polo-like kinase (PLKs) and pyrazol inhibitors was performed using 3D-QSAR modeling, molecular docking, and MD simulations in order to design high-activity inhibitors. The HQSAR (Q2 = 0.793, R2 = 0.917, R2ext = 0.961), CoMFA (Q2 = 0.582, R2 = 0.722, R2ext = 0.951), CoMSIA/SE (Q2 = 0.603, R2 = 0.801, R2ext = 0.849), and Topomer CoMFA (Q2 = 0.726, R2 = 0.992, R2ext = 0.717) showed good reliability and predictability. All models have been successfully tested by external validation, so all five established models are reliable. The analysis of the different contour maps of different models gives structural information to improve the inhibitory function. Molecular docking results show that the amino acids Met 592, GLU 590, LEU 657, VAL 524, and PHE 589 are the active sites of the tropomyosin receptor TRKs. The results obtained by MD showed that compound 19i could form a more stable complex protein (PDB id: 5KVT). Based on these results, we developed new compounds and their expected inhibitory activities. The results of physicochemical and ADME-Tox properties showed that the four proposed molecules are orally bioavailable, and they are not toxic in the Ames test. Thus, these results would provide modeling information that could help experimental researchers find TRK type I inhibitors more efficiently.Communicated by Ramaswamy H. Sarma.

3D-QSAR Studies, Molecular Docking, Molecular Dynamic Simulation, and ADMET Proprieties of Novel Pteridinone Derivatives as PLK1 Inhibitors for the Treatment of Prostate Cancer

Overexpression of polo-like kinase 1 (PLK1) has been found in many different types of cancers. With its essential role in cell proliferation, PLK1 has been determined to be a broad-spectrum anti-cancer target. In this study, 3D-QSAR, molecular docking, and molecular dynamics (MD) simulations were applied on a series of novel pteridinone derivatives as PLK1 inhibitors to discover anti-cancer drug candidates. In this work, three models—CoMFA (Q² = 0.67, R² = 0.992), CoMSIA/SHE (Q² = 0.69, R² = 0.974), and CoMSIA/SEAH (Q² = 0.66, R² = 0.975)—of pteridinone derivatives were established. The three models that were established gave Rpred2 = 0.683, Rpred 2= 0.758, and Rpred 2= 0.767, respectively. Thus, the predictive abilities of the three proposed models were successfully evaluated. The relations between the different champs and activities were well-demonstrated by the contour chart of the CoMFA and CoMSIA/SEAH models. The results of molecular docking indicated that residues R136, R57, Y133, L69, L82, and Y139 were the active sites of the PLK1 protein (PDB code: 2RKU), in which the more active ligands can inhibit the enzyme of PLK1. The results of the molecular dynamic MD simulation diagram were obtained to reinforce the previous molecular docking results, which showed that both inhibitors remained stable in the active sites of the PLK1 protein (PDB code: 2RKU) for 50 ns. Finally, a check of the ADME-Tox properties of the two most active molecules showed that molecular N° 28 could represent a good drug candidate for the therapy of prostate cancer diseases.