3D-QSAR Models Research Articles

Discovery of Trisubstituted N-Phenylpyrazole Containing Diamides with Improved Insecticidal Activity.

To increase the structural diversity of insecticides and meet the needs of effective integrated insect management, the structure of chlorantraniliprole was modified based on a previously established three-dimensional quantitative structure-activity relationship (3D-QSAR) model. The pyridinyl moiety in the structure of chlorantraniliprole was replaced with a 4-fluorophenyl group. Further modifications of this 4-fluorophenyl group by introducing a halogen atom at position 2 and an electron-withdrawing group (e.g., iodine, cyano, and trifluoromethyl) at position 5 led to 34 compounds with good insecticidal efficacy against Mythimna separata, Plutella xylostella, and Spodoptera frugiperda. Among them, compound IV f against M. separata showed potency comparable to that of chlorantraniliprole. IV p against P. xylostella displayed a 4.5 times higher potency than chlorantraniliprole. In addition, IV d and chlorantraniliprole exhibited comparable potencies against S. frugiperda. Transcriptome analysis showed that the molecular target of compound IV f is the ryanodine receptor. Molecular docking was further performed to verify the mode of action and insecticidal activity against resistant P. xylostella.

Integrated Computational Approaches for Drug Design Targeting Cruzipain.

Cruzipain inhibitors are required after medications to treat Chagas disease because of the need for safer, more effective treatments. Trypanosoma cruzi is the source of cruzipain, a crucial cysteine protease that has driven interest in using computational methods to create more effective inhibitors. We employed a 3D-QSAR model, using a dataset of 36 known inhibitors, and a pharmacophore model to identify potential inhibitors for cruzipain. We also built a deep learning model using the Deep purpose library, trained on 204 active compounds, and validated it with a specific test set. During a comprehensive screening of the Drug Bank database of 8533 molecules, pharmacophore and deep learning models identified 1012 and 340 drug-like molecules, respectively. These molecules were further evaluated through molecular docking, followed by induced-fit docking. Ultimately, molecular dynamics simulation was performed for the final potent inhibitors that exhibited strong binding interactions. These results present four novel cruzipain inhibitors that can inhibit the cruzipain protein of T. cruzi.

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach.

Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

Novel 5-Sulfonyl-1,3,4-thiadiazole-Substituted Flavonoids as Potential Bactericides and Fungicides: Design, Synthesis, Three-Dimensional Quantitative Structure-Activity Relationship Studies.

Flavonoids, ubiquitous natural products, provide sources for drug discovery owing to their structural diversity, broad-spectrum pharmacological activity, and excellent environmental compatibility. To develop antibacterial and antifungal agents with novel mechanisms of action and innovative structures, a series of novel 5-sulfonyl-1,3,4-thiadiazole-substituted flavonoids were designed and synthesized, and their biological activities against seven agriculturally common phytopathogenic microorganisms were evaluated. The results of the antimicrobial bioassay showed that most of the target compounds displayed excellent inhibitory effects against Xanthomonas oryzae, Rhizoctonia solani, and Colletotrichum orbiculare. Compounds 1, 3, 7, 9, 13, and 14 exhibited remarkable antibacterial activity against X. oryzae pv. oryzae with EC50 values below 10 μg/mL, which were superior to bismerthiazol (70.89 μg/mL). Compound 2 (EC50 = 0.41 μg/mL) displayed the most effective inhibitory potency against R. solani in vivo, comparable protective effects with the positive control carbendizam. Preliminary mechanistic studies indicated that compound 2 induced disordered entanglement of hyphae, shrinkage of hyphal surfaces, extravasation of cellular contents, and vacuole swelling and rupture, which disrupted normal hyphal growth. Subsequently, compounds 35-53 with good antifungal activity were designed and synthesized based on reliable three-dimensional quantitative structure-activity relationship (3D-QSAR) models. Compound 49 showed high efficacy and superior antifungal activity against R. solani, with an EC50 value of 0.28 μg/mL and a half-maximal effective concentration of 0.46 μg/mL.

Designing of xanthine-based DPP-4 inhibitors: a structure-guided alignment dependent Multifacet 3D-QSAR modeling, and molecular dynamics simulation study

The DPP-4 enzyme degrades incretin hormones GLP-1 and GIP. DPP-4 inhibitors are found effective in the prevention of the degradation of incretins. Xanthine scaffold-bearing molecules are reported as potential DPP-4 inhibitors for treating type 2 diabetes mellitus, e.g. the marketed drug linagliptin. In this work, structure-guided alignment-dependent atom- and Gaussian field-based 3D-QSAR have been performed on a dataset of 75 molecules. The robustness and predictive ability of the developed multifacet 3D-QSAR models were validated on different statistical parameters and found to be statistically fit. The favorable and unfavorable pharmacophoric features were mapped for each multifacet 3D-QSAR model based on three alignment sets (1–3). A five-point common pharmacophore hypothesis was generated separately for each set of alignments. The molecular dynamics simulations (up to 100 ns) were performed for the potent molecule from each alignment set (Compounds 12, 40 and 57) compared to reference standard linagliptin to study the binding energy and stability of target-ligand complexes. The MM-PBSA calculations revealed that the binding free energy and stability of compounds 12 (−40.324 ± 17.876 kJ/mol), 40 (−80.543 ± 21.782 kJ/mol) and 57 (−50.202 ± 16.055 kJ/mol) were better than the reference drug linagliptin (−20.390 ± 63.200 kJ/mol). The generated contour maps from structure-guided alignment-dependent multifacet 3D-QSAR models offer information about the structure–activity relationship (SAR) and ligand-target binding energy and stability data from MD simulation may be utilized to design and develop target selective xanthine-based novel DPP-4 inhibitors. Communicated by Ramaswamy H. Sarma

In-silico design novel phenylsulfonyl furoxan and phenstatin derivatives as multi-target anti-cancer inhibitors based on 2D-QSAR, molecular docking, dynamics and ADMET approaches

ABSTRACT A series of 31 hybrid of phenylsulfonyl furoxan and phenstatin (1a-j, 2a-j, 3a-j, 4 and 5) derivatives, were computationally studied as potential anti-cancer inhibitors against four cell lines, i.e. A2780, MDA-MB-231, HCT-116 and A549. In this work, the 2D-QSAR approach combining the multiple linear regression (MLR) model, and internal and external cross-validation, showed a satisfactory quality factor: R 2 = 0.85, 0.74, 0.82 and 0.75 for the four cell lines, respectively. The binding affinity of the hybrid agents towards the four 4GL7, 6GUE, 1M17 and 4XL7 anti-tumoral targets was further evaluated using molecular docking and dynamics simulations (0–200 ns). The dynamics assessment parameters indicated the formation of satisfactorily stable complexes. In addition, all considered data sets show that the best binding affinity, including the highest docking score, hydrogen bond energy and amino acid steric interactions are well predicted for the best-selected complexes. The developed 2D-QSAR model was leveraged to design and predict the biological activity of 12 new hybrid compounds (N1–N12) based on the best in-vivo inhibitor, namely, the 3 h ligand of the formula: (4-((1-(2-((4-((3crylamidophenyl)amino)quinazolin-2-yl)thio)acetyl)piperidin-4-yl)oxy)-3-(phenylsulfonyl)-1,2,5-oxadiazole 2-oxide). Multitargeting docking scores and dynamics simulations show that they exhibit satisfactorily potent anti-tumoral inhibition abilities towards the four proteins. Our in-silico outcomes would be combined with in-vitro and in-vivo studies to provide a perspective on the validation of their anti-cancer activity. In particular, the ADMET predictions indicate that four new designed ligands have demonstrated a good drug-like profile and can be considered prospective candidates for future anti-cancer therapies.

Design, Synthesis, Antifungal Activity, and 3D-QASR of Novel Oxime Ether-Containing Coumarin Derivatives as Potential Fungicides.

Structural modification of natural products is an effective approach for improving antifungal activity and has, therefore, been used extensively in the development of new agrochemical products. In this work, a series of novel coumarin derivatives containing oxime ether structures were designed, synthesized, and evaluated for antifungal activity. Some of the designed compounds exhibited promising antifungal activities against tested fungi, and compounds 4a, 4c, 5a, and 6b had EC50 values equivalent to those of commercial fungicides. Compound 6b was the most promising candidate fungicide against Rhizoctonia solani (EC50 = 0.46 μg/mL). In vivo antifungal bioassays suggested that compounds 5a and 6b could serve as novel agricultural antifungals. Furthermore, microscopy demonstrated that compound 6b induced the sprawling growth of hyphae, distorted the outline of cell walls, and reduced mitochondrial numbers. Additionally, the effects of the substituent steric, electrostatic, hydrophobic, and hydrogen-bond fields were elucidated using an accurate and reliable three-dimensional quantitative structure-activity relationship (3D-QSAR) model. The results presented here will guide the discovery of potential novel fungicides for plant disease control in agriculture.

Risk mitigation strategy and mechanism analysis of neonicotinoid pesticides on earthworms

Neonicotinoid insecticides (NNIs) are a new class of widely used insecticides with certain risks to non-target organisms, like earthworms. The gray correlation method was used to calculate the comprehensive risk effect value of acute toxicity (LC50) and bioaccumulation (logKow) of NNIs on earthworms. A comprehensive effects three-dimensional quantitative structure–activity relationship (3D-QSAR) model was constructed, using NNIs molecular structures and the comprehensive effect value as the independent and dependent variables, respectively. One of the representatives guadipyr (GUA) was selected as the template molecule for the molecular design and modification. A total of 63 NNIs alternatives were designed with a reduced comprehensive value higher than 10%, and as high as 42%. After screening, 15 NNIs alternatives were screened with decreased acute toxicity to earthworms, bioaccumulation effects and improved functional property. The calculated primary acute risk quotient of earthworms shows that the designed NNIs alternatives have lower earthworm risks (reduction of 70.48–99.99%). Results also found that the electronic, geometric and topological parameters of NNIs are the key descriptors that affect NNIs alternatives' toxicity. The number of hydrophobic interaction amino acid residues in NNIs molecules also contributes to the acute toxicity and the bioaccumulation of NNIs alternatives on earthworms. This study aims to design and screen functionally improved and environmentally friendly NNIs alternatives that have low risk to earthworms and provide theoretical methods and new ideas for the risk control and development of pesticides represented by NNIs.

Integrated virtual screening and in vitro studies for exploring the mechanism of triterpenoids in Chebulae Fructus alleviating mesaconitine-induced cardiotoxicity via TRPV1 channel.

Background: In traditional Mongolian or Tibetan medicine in China, Chebulae Fructus (CF) is widely used to process or combine with aconitums to decrease the severe toxicity of aconitums. Researches in this area have predominantly focused on tannins, with few research on other major CF components for cardiotoxicity mitigation. The present study aimed to clarify whether triterpenoids can attenuate the cardiotoxicity caused by mesaconitine (MA) and investigate the mechanism of cardiotoxicity attenuation. Methods: Firstly, the pharmacophore model, molecular docking, and 3D-QSAR model were used to explore the mechanism of CF components in reducing the toxicity of MA mediated by the TRPV1 channel. Then three triterpenoids were selected to verify whether the triterpenoids had the effect of lowering the cardiotoxicity of MA using H9c2 cells combined with MTT, Hoechst 33258, and JC-1. Finally, Western blot, Fluo-3AM, and MTT assays combined with capsazepine were used to verify whether the triterpenoids reduced H9c2 cardiomyocyte toxicity induced by MA was related to the TRPV1 channel. Results: Seven triterpenoids in CF have the potential to activate the TRPV1 channel. And they exhibited greater affinity for TRPV1 compared to other compounds and MA. However, their activity was relatively lower than that of MA. Cell experiments revealed that MA significantly reduced H9c2 cell viability, resulting in diminished mitochondrial membrane potential and nuclear pyknosis and damage. In contrast, the triterpenoids could improve the survival rate significantly and counteract the damage of MA to the cells. We found that MA, arjungenin (AR), and maslinic acid (MSA) except corosolic acid (CRA) upregulated the expression of TRPV1 protein. MA induced a significant influx of calcium, whereas all three triterpenoids alleviated this trend. Blocking the TRPV1 channel with capsazepine only increased the cell viability that had been simultaneously treated with MA, and AR, or MSA. However, there was no significant difference in the CRA groups treated with or without capsazepine. Conclusion: The triterpenoids in CF can reduce the cardiotoxicity caused by MA. The MSA and AR function as TRPV1 agonists with comparatively reduced activity but a greater capacity to bind to TRPV1 receptors, thus antagonizing the excessive activation of TRPV1 by MA.

Conducting 2D and 3D QSAR Analyses and Molecular Docking Studies of Analogues of 2-(1-(1,3,4-thiadiazol-2-yl)piperidin-4-yl)ethan-1-ol with the Aim of Identifying Promising Drug Candidates for Targeting Glioblastoma

Background: 2-(1-(1,3,4-thiadiazol-2-yl)piperidin-4-yl) ethan-1-ol analogues represent novel glutaminase 1 inhibitors. Their exemplary antineoplastic efficacy underscores their prospective utility in glioblastoma chemotherapy. Objectives: This study aimed to elucidate 2D and 3D-QSAR models that authenticate the antineoplastic efficacy of ethan-1-ol analogues and delineate optimal structural configurations conducive to new pharmaceutical design. Methods: The Heuristic Method (HM) was employed for the development of a 2D-linear QSAR paradigm, whilst the Gene Expression Programming (GEP) algorithm was employed for a 2D-nonlinear QSAR paradigm. Concurrently, the CoMSIA methodology was deployed to scrutinize the nexus between pharmaceutical structure and potency. An ensemble of 200 nascent anti-glioma ethan-1-ol compounds was conceptualized, and their potency levels were prognosticated via chemical descriptors and molecular field delineations. Pharmaceuticals epitomizing peak potency were earmarked for molecular docking validation. Results: The empirical modeling exhibited pronounced superiority with the 3D paradigm, succeeded by the GEP nonlinear paradigm and culminated with the HM linear model. The 3D paradigm was characterized by a robust Q2 (0.533), R2 (0.921), and F-values (132.338) complemented by a minimal SEE (0.110). The molecular descriptor MNO coupled with the hydrogen bond donor field facilitated novel pharmaceutical conceptualizations, leading to the identification of the quintessential active molecule, 24J.138, lauded for its superlative antineoplastic attributes and docking proficiency. Conclusion: The orchestration of bidimensional and tridimensional paradigms, synergized by innovative amalgamation of contour maps and molecular descriptors, provides novel insights and methodologies for the synthesis of glioblastoma chemotherapeutic agents.

Molecular modeling of novel 2-aminopyridine derivatives as potential JAK2 inhibitors: a rational strategy for promising anticancer agents

Janus kinase 2(JAK2) is a potential target for anticancer drugs in the treatment of numerous myeloproliferative diseases due to its central role in the JAK/STAT signaling cascade. In this study, the binding behavior of 2 amino-pyridine derivatives as JAK2 inhibitors was investigated by using multifaceted strategies including 3D-QSAR, molecular docking, Fingerprint analysis, MD simulations, and MM-PBSA calculations. A credible COMFA (q 2 = 0.606 and r 2 = 0.919) and COMSIA (q 2 = 0.641 and r 2 = 0.992) model was developed, where the internal and external validation revealed that the obtained 3D-QSAR models could be capable of predicting bioactivities of JAK2 inhibitors. The structural criteria provided by the contour maps of model were used to computationally develop more potent 100 new JAK2 inhibitors. Docking studies were conducted on the model data set and newly developed compounds (in-house library) to demonstrate their binding mechanism and highlight the key interacting residues within JAK2 active site. The selected docked complexes underwent MD simulation (100 ns), which contributed in the further study of the binding interactions. Binding free energy analyses (MMGB/PBSA) revealed that key residues such as Glu930, Leu932 (hinge region), Asp939 (solvent accessible region), Arg980, Asn981and Asp994 (catalytic site) have a significantly facilitate ligand-protein interactions through H-bonding and van der Waals interactions. The preliminary in-silico ADMET evaluation revealed encouraging results for all the modeled and in-house library compounds. The findings of this research have the potential to offer valuable recommendations for the advancement of novel, potent, and efficacious JAK2 inhibitors. Overall, this work has successfully employed a wide range of computer-based methodologies to understand the interaction dynamics between 2-amino-pyridine derivatives and the JAK2 enzyme, which is a crucial target in myeloproliferative disorders. Communicated by Ramaswamy H. Sarma

First report on pesticide sub-chronic and chronic toxicities against dogs using QSAR and chemical read-across

ABSTRACT Excessive use of chemicals is the outcome of the industrialization of agricultural sectors which leads to disturbance of ecological balance. Various agrochemicals are widely used in agricultural fields, urban green areas, and to protect from various pest-associated diseases. Due to their long-term health and environmental hazards, chronic toxicity assessment is crucial. Since in vivo and in vitro toxicity assessments are costly, lengthy, and require a large number of animal experiments, in silico toxicity approaches are better alternatives to save time, cost, and animal experimentation. We have developed the first regression-based 2D-QSAR models using different sub-chronic and chronic toxicity data of pesticides against dogs employing 2D descriptors. From the statistical results ( n train = 53 − 62 , r 2 = 0.614 to 0.754, Q L O O 2 = 0.501 to 0.703 and Q F 1 2 = 0.531 to 0.718, Q F 2 2 = 0.523 − 0.713 ), it was concluded that the models are robust, reliable, interpretable, and predictive. Similarity-based read-across algorithm was also used to improve the predictivity ( Q F 1 2 = 0.595 − 0.813 , Q F 2 2 = 0.573 − 0.809 ) of the models. 5132 chemicals obtained from the CPDat and 1694 pesticides obtained from the PPDB database were also screened using the developed models, and their predictivity and reliability were checked. Thus, these models will be helpful for eco-toxicological data-gap filling, toxicity prediction of untested pesticides, and development of novel, safer & eco-friendly pesticides.

Synthesis, antibacterial activity, and 3D-QASR studies of matrine-indole derivatives as potential antibiotics

Matrine and indole have antibacterial, anticancer, and other biological activities, in order to develop new antibiotics to solve the problem of multi-drug resistant bacteria. In this paper, we synthesized a series of 29 novel matrine derivatives as potential drug candidates by combining indole analogs and matrine. The antibacterial activity of these compounds was evaluated through minimum inhibitory concentration (MIC) assays against five bacterial strains (S. aureus, C. albicans, P. acnes, P. aeruginosa, and E. coli). The obtained results demonstrated promising antibacterial efficacy, particularly for compounds A20 and A18, which exhibited MICs.au values of 0.021 and 0.031 mg/ml, respectively, against S. aureus. Moreover, compounds A20 and A27 displayed remarkable MICc.al values of 2.806 and 4.519 mg/ml, respectively, against C. albicans, surpassing the performance of the clinical antibiotic penicillin G sodium (0.0368 mg/ml) and fluconazole (4.849 mg/ml). These findings underscore the significant bacteriostatic activity of the matrine derivatives. Furthermore, to gain a deeper understanding 3D-QSAR modeling was employed, revealing the critical influence of steric structure, charge distribution, hydrophobic interactions, and hydrogen bonding within the molecular structure on the bacteriostatic activity of the compounds. Additionally, molecular docking simulations shed light on the interaction between compound A20 and bacterial proteins, highlighting the involvement of hydrogen bonding, hydrophobic interactions, and π-π conjugation in the formation of stable complexes that inhibit the normal functioning of the proteins. This comprehensive analysis provided valuable insights into the antibacterial mechanism of the novel matrine derivatives, offering theoretical support for their potential application as antibiotics.

Discovery of novel bromodomain-containing protein 4 (BRD4-BD1) inhibitors combined with 3d-QSAR, molecular docking and molecular dynamics in silico

Bromine-containing domain protein 4 (BRD4) plays a crucial role in regulating transcription and genome stability. Selective inhibitors of BRD4-BD1 can specifically target specific bromine domains to affect cell proliferation, apoptosis, and differentiation. In this work, 43 selective benzoazepinone BRD4-BD1 inhibitors were studied using molecular simulations and three-dimensional quantitative conformation relationships (3D-QSAR). A reliable 3D-QSAR model was established based on COMFA (Q2 = 0.532, R2 = 0.981) and COMSIA (S + E + H (Q2 = 0.536, R2 = 0.979) two different analysis methods. Through 3D-QSAR model prediction and quantum chemical analysis, 15 small molecules with stronger inhibitory activity than the template compounds were constructed, and 5 new compounds with higher predictive activity and binding affinity were screened by molecular docking and ADMET methods. According to the molecular dynamics simulation, the key residues that can interact with BRD4-BD1 protein and molecular docking results are consistent, including ASN140, MET132, GLN85, MET105, ASN135 and TYR97. From the MD trajectory, we calculated and analyzed RMSD, RMSF, free binding energy, FECM, DCCM and PCA, the loop region formed by amino acids VAL45∼PRO62 showed α-helix, β-folding and clustering towards the active center with the extension of simulation time. Further optimization of the structure of active candidate compounds A6, A11, A14, and A15 will provide the necessary theoretical basis for the synthesis and activity evaluation of novel BRD4-BD1 inhibitors. Communicated by Ramaswamy H. Sarma

Shaping the Future of Obesity Treatment: In Silico Multi-Modeling of IP6K1 Inhibitors for Obesity and Metabolic Dysfunction.

Recent research has uncovered a promising approach to addressing the growing global health concern of obesity and related disorders. The inhibition of inositol hexakisphosphate kinase 1 (IP6K1) has emerged as a potential therapeutic strategy. This study employs multiple ligand-based in silico modeling techniques to investigate the structural requirements for benzisoxazole derivatives as IP6K1 inhibitors. Firstly, we developed linear 2D Quantitative Structure-Activity Relationship (2D-QSAR) models to ensure both their mechanistic interpretability and predictive accuracy. Then, ligand-based pharmacophore modeling was performed to identify the essential features responsible for the compounds' high activity. To gain insights into the 3D requirements for enhanced potency against the IP6K1 enzyme, we employed multiple alignment techniques to set up 3D-QSAR models. Given the absence of an available X-ray crystal structure for IP6K1, a reliable homology model for the enzyme was developed and structurally validated in order to perform structure-based analyses on the selected dataset compounds. Finally, molecular dynamic simulations, using the docked poses of these compounds, provided further insights. Our findings consistently supported the mechanistic interpretations derived from both ligand-based and structure-based analyses. This study offers valuable guidance on the design of novel IP6K1 inhibitors. Importantly, our work exclusively relies on non-commercial software packages, ensuring accessibility for reproducing the reported models.

Virtual screening of acetylcholinesterase inhibitors through pharmacophore-based 3D-QSAR modeling, ADMET, molecular docking, and MD simulation studies.

The online version contains supplementary material available at 10.1007/s40203-024-00189-1.

Chelation of the Optimal Antifungal Pogostone Analogue with Copper(II) to Explore the Dual Antifungal and Antibacterial Agent.

In an ongoing effort to explore more potent antifungal pogostone (Po) analogues, we maintained the previously identified 3-acetyl-4-hydroxy-2-pyrone core motif while synthesizing a series of Po analogues with variations in the alkyl side chain. The in vitro bioassay results revealed that compound 21 was the most potent antifungal analogue with an EC50 value of 1.1 μg/mL against Sclerotinia sclerotiorum (Lib.) de Bary. Meanwhile, its Cu(II) complex 34 manifested significantly enhanced antibacterial activity against Xanthomonas campestris pv campestris (Xcc) with a minimum inhibitory concentration (MIC) value of 300 μg/mL compared with 21 (MIC = 700 μg/mL). Complex 34 exhibited a striking preventive effect against S. sclerotiorum and Xcc in rape leaves, with control efficacies of 98.8% (50 μg/mL) and 80.7% (1000 μg/mL), respectively. The 3D-QSAR models generated using Topomer comparative molecular field analysis indicated that a shorter alkyl chain (carbon atom number <8), terminal rings, or electron-deficient groups on the alkyl side chain are beneficial for antifungal potency. Further, bioassay results revealed that the component of 21 in complex 34 dominated the antifungal activity, but the introduction of Cu(II) significantly enhanced its antibacterial activity. The toxicological observations demonstrated that 21 could induce abnormal mitochondrial morphology, loss of mitochondrial membrane potential, and reactive oxygen species (ROS) accumulation in S. sclerotiorum. The enzyme assay results showed that 21 is a moderate promiscuous inhibitor of mitochondrial complexes II and III. Besides, the introduction of Cu(II) to 34 could promote the disruption of the cell membrane and intracellular proteins and the ROS level in Xcc compared with 21. In summary, these results highlight the potential of 34 as a dual antifungal and antibacterial biocide for controlling rape diseases or as a promising candidate for further optimization.

Design and Synthesis of Pleuromutilin Derivatives as Antibacterial Agents Using Quantitative Structure-Activity Relationship Model.

The quantitative structure-activity relationship (QSAR) is one of the most popular methods for the virtual screening of new drug leads and optimization. Herein, we collected a dataset of 955 MIC values of pleuromutilin derivatives to construct a 2D-QSAR model with an accuracy of 80% and a 3D-QSAR model with a non-cross-validated correlation coefficient (r2) of 0.9836 and a cross-validated correlation coefficient (q2) of 0.7986. Based on the obtained QSAR models, we designed and synthesized pleuromutilin compounds 1 and 2 with thiol-functionalized side chains. Compound 1 displayed the highest antimicrobial activity against both Staphylococcus aureus ATCC 29213 (S. aureus) and Methicillin-resistant Staphylococcus aureus (MRSA), with minimum inhibitory concentrations (MICs) < 0.0625 μg/mL. These experimental results confirmed that the 2D and 3D-QSAR models displayed a high accuracy of the prediction function for the discovery of lead compounds from pleuromutilin derivatives.

Identification of PLK1-PBD Inhibitors from the Library of Marine Natural Products: 3D QSAR Pharmacophore, ADMET, Scaffold Hopping, Molecular Docking, and Molecular Dynamics Study.

PLK1 is found to be highly expressed in various types of cancers, but the development of inhibitors for it has been slow. Most inhibitors are still in clinical stages, and many lack the necessary selectivity and anti-tumor effects. This study aimed to create new inhibitors for the PLK1-PBD by focusing on the PBD binding domain, which has the potential for greater selectivity. A 3D QSAR model was developed using a dataset of 112 compounds to evaluate 500 molecules. ADMET prediction was then used to select three molecules with strong drug-like characteristics. Scaffold hopping was employed to reconstruct 98 new compounds with improved drug-like properties and increased activity. Molecular docking was used to compare the efficient compound abbapolin, confirming the high-activity status of [(14S)-14-hydroxy-14-(pyridin-2-yl)tetradecyl]ammonium,[(14S)-15-(2-furyl)-14-hydroxypentadecyl]ammonium and [(14S)-14-hydroxy-14-phenyltetradecyl]ammonium. Molecular dynamics simulations and MMPBSA were conducted to evaluate the stability of the compounds in the presence of proteins. An in-depth analysis of [(14S)-15-(2-furyl)-14-hydroxypentadecyl]ammonium and [(14S)-14-hydroxy-14-phenyltetradecyl]ammonium identified them as potential candidates for PLK1 inhibitors.

4D-QSAR and MIA-QSAR Studies of Aminobenzimidazole Derivatives as Fourth-generation EGFR Inhibitors.

The epidermal growth factor receptor (EGFR) protein has been intensively studied as a therapeutic target for non-small cell lung cancer (NSCLC). The aminobenzimidazole derivatives as the fourth-generation EGFR inhibitors have achieved promising results and overcame EGFR mutations at C797S, del19 and T790M in NSCLC. In order to understand the quantitative structure-activity relationship (QSAR) of aminobenzimidazole derivatives as EGFRdel19 T790M C797S inhibitors, the four-dimensional QSAR (4D-QSAR) and multivariate image analysis (MIA-QSAR) have been performed on the data of 45 known aminobenzimidazole derivatives. The 4D-QSAR descriptors were acquired by calculating the association energies between probes and aligned conformational ensemble profiles (CEP), and the regression models were established by partial least squares (PLS). In order to further understand and verify the 4D-QSAR model, MIA-QSAR was constructed by using chemical structure pictures to generate descriptors and PLS regression. Furthermore, the molecular docking and averaged noncovalent interactions (aNCI) analysis were also performed to further understand the interactions between ligands and the EGFR targets, which was in good agreement with the 4D-QSAR model. The established 4D-QSAR and MIA-QSAR models have strong stability and good external prediction ability. These results will provide theoretical guidance for the research and development of aminobenzimidazole derivatives as new EGFRdel19 T790M C797S inhibitors.