3D Pharmacophore Model Research Articles

Identification of Novel Bromodomain-Containing Protein 4 (BRD4) Binders through 3D Pharmacophore-Based Repositioning Screening Campaign.

A 3D structure-based pharmacophore model built for bromodomain-containing protein 4 (BRD4) is reported here, specifically developed for investigating and identifying the key structural features of the (+)-JQ1 known inhibitor within the BRD4 binding site. Using this pharmacophore model, 273 synthesized and purchased compounds previously considered for other targets but yielding poor results were screened in a drug repositioning campaign. Subsequently, only six compounds showed potential as BRD4 binders and were subjected to further biophysical and biochemical assays. Compounds 2, 5, and 6 showed high affinity for BRD4, with IC50 values of 0.60 ± 0.25 µM, 3.46 ± 1.22 µM, and 4.66 ± 0.52 µM, respectively. Additionally, these compounds were tested against two other bromodomains, BRD3 and BRD9, and two of them showed high selectivity for BRD4. The reported 3D structure-based pharmacophore model proves to be a straightforward and useful tool for selecting novel BRD4 ligands.

SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability.

Artificial intelligence (AI) has emerged as a pivotal force in enhancing productivity across various sectors, with its impact being profoundly felt within the pharmaceutical and biotechnology domains. Despite AI's rapid adoption, its integration into scientific research faces resistance due to myriad challenges: the opaqueness of AI models, the intricate nature of their implementation, and the issue of data scarcity. In response to these impediments, we introduce SmartCADD, an innovative, open-source virtual screening platform that combines deep learning, computer-aided drug design (CADD), and quantum mechanics methodologies within a user-friendly Python framework. SmartCADD is engineered to streamline the construction of comprehensive virtual screening workflows that incorporate a variety of formerly independent techniques─spanning ADMET property predictions, de novo 2D and 3D pharmacophore modeling, molecular docking, to the integration of explainable AI mechanisms. This manuscript highlights the foundational principles, key functionalities, and the unique integrative approach of SmartCADD. Furthermore, we demonstrate its efficacy through a case study focused on the identification of promising lead compounds for HIV inhibition. By democratizing access to advanced AI and quantum mechanics tools, SmartCADD stands as a catalyst for progress in pharmaceutical research and development, heralding a new era of innovation and efficiency.

3D-QSAR, Pharmacophore Modeling, ADMET, and DFT Studies of Halogenated Conjugated Dienones as Potent MAO-B Inhibitors.

It has been reported that the extension of conjugation in chalcone scaffolds considerably enhanced the potency, selectivity, reversibility, and competitive mode of MAO-B inhibition. In this study, using the experimental results of IC50 values of fifteen halogenated conjugated dienone derivatives (MK1-MK15) against MAO-B, we developed a 3DQSAR model. Further, we created a 3D pharmacophore model in active compounds in the series. The built model selected three variables (G2U, RDF115m, RDF155m) among the 653 AlvaDesc molecular descriptors, with a r2 value of 0.87 and a Q2 cv for cross-validation equal to 0.82. The three variables were mostly associated with the direction of symmetry and the likelihood of discovering massive atoms at great distances. The evaluated molecules exhibited a good correlation between experimental and predicted data, indicating that the IC50 value of the structure MK2 was related to the interatomic distances of 15.5 Å between bromine and chloro substituents. Furthermore, the molecules in the series with the highest activity were those with enhanced second component symmetry directional index from the 3D representation, which included the structures MK5 and MK6. Additionally, a pharmacophore hypothesis was developed and validated using the decoy Schrodinger dataset, with an ROC score of 0.87 and an HHRR 1 fitness score that ranged from 2.783 to 3.00. The MK series exhibited a significant blood-brain barrier (BBB) permeability, according to exploratory analyses and in silico projections, and almost all analogues were expected to have strong BBB permeability. Further DFT research revealed that electrostatics were important in the interactions with MAO-B.

Discovery of Novel, Selective, and Nonbasic Agonists for the Kappa-Opioid Receptor Determined by Salvinorin A-Based Virtual Screening.

Modulating the kappa-opioid receptor (KOR) is a promising strategy for treating various human diseases. KOR agonists show potential for treating pain, pruritus, and epilepsy, while KOR antagonists show potential for treating depression, anxiety, and addiction. The diterpenoid Salvinorin A (SalA), a secondary metabolite of Salvia divinorum, is a potent and selective KOR agonist. Unlike typical opioids, SalA lacks a basic nitrogen, which encouraged us to search for nonbasic KOR ligands. Through structure-based virtual screening using 3D pharmacophore models based on the binding mode of SalA, we identified novel, nonbasic, potent, and selective KOR agonists. In vitro studies confirmed two virtual hits, SalA-VS-07 and SalA-VS-08, as highly selective for the KOR and showing G protein-biased KOR agonist activity. Both KOR ligands share a novel spiro-moiety and a nonbasic scaffold. Our findings provide novel starting points for developing therapeutics aimed at treating pain and other conditions in which KOR is a central player.

Discovery of Multi-functional Lead Compounds Originating from Traditional Chinese Medicine for Developing Anti-depressive Agents via Virtual Screening

Background: The increasing prevalence of depression has become a global health issue. Currently approved anti-depressive including 5-hydroxytryptamine (5-HT), dopamine (DA), norepinephrine (NE), triple reuptake inhibitors (TRIs) and glutamate N-methyl-D-aspartate (NMDA) receptor antagonists have limited effects because of their insufficient efficacy and/or slow onset of action. Developing multifunctional antidepressants that can modulate 5-HT, DA, NE, and NMDA simultaneously can potentially overcome the current drug defects. Objective: This study aimed to explore leads for the development of multi-functional anti-depressive agents that simultaneous triple reuptake inhibitory and NMDA-GluN2B receptor antagonistic activities. Methods: Potential leads were screened virtually from the TCMSP database based on the 3DPharmacophore model of TRIs followed by the molecular docking into NMDA-GluN2B receptor, BBB score, and the in silico toxicity evaluation. The biological activities of discovered leads on 5-HT, NE, and DA reuptake and their effect on the NMDA-GluN2B receptor were evaluated via radio-labeled neurotransmitters and competition radio-ligand binding experiment with [3H] ifenprodil, respectively. Lastly, the antidepressant effect of these potential leads was determined in vivo through the forced swim test in mice. Results: Two compounds were attained as potential leads after the aforementioned experiments. Further in vitro biological evaluation identified Hit-2 as a promising lead that exerted favorable triple 5- HT/DA/NE reuptake inhibitory activity (66.98% inhibition rate at 10 μM against hNET, 73.01% inhibition rate at 1 μM against hDAT and 86.27% inhibition rate at 1 μM against hSERT), as well as potent NMDA-GluN2B receptor antagonistic activity (Ki=115.73 ± 3.54 nM). The antidepressant activity of Hit- 2 was confirmed through in vivo experiments Conclusion: Hit-2 not only simultaneously inhibited the reuptake of 5-HT, DA, and NE, and acted as an NMDA-GluN2B receptor antagonist in vitro but also showed in vivo antidepressant activity. These findings may serve as a structural basis for the further development of multi-functional anti-depressive agents.

Computer-aided rational design and synthesis of new potential antihypertensive agents among 1,2,3-triazole-containing nifedipine analogs

1,2,3-Triazole-containing Nifedipine analogues offer the opportunity to increase biostability, bioavailability, efficacy and binding selectivity to target receptors. Here, we applied a computer-aided rational design for identifying new Nifedipine analogues containing a 1,2,3-triazole moiety. First, a new chemical library of 796 derivatives combining the DHP fragment and 1,2,3-triazole moiety was generated. Second, to reduce the library size, the library was pre-filtered using two 3D-pharmacophore models with different complexity, which allowed us to gradually reduce the chemical space, ending up with 26 hit candidates. Molecular docking calculations against the rCav1.1 receptor allowed the identification of eight derivatives 5a-h, characterized by the binding affinity towards the rCav1.1 receptor of the same level as approved Nifedipine-like drugs. Next, our molecular docking results were used to guide and optimize the retrosynthetic approaches for new analogues of Nifedipine as promising antihypertensive agents. So, a retrosynthetic approach for Nifedipine analogues with a 1,2,3-triazole ring in position 4 was proposed. Finally, eight analogues 5a-h determined by molecular docking calculations were synthesized using the suggested retrosynthetic approach. The aim of this study is to identify new Nifedipine analogues using a computer-aided drug design and a retrosynthetic approach. Materials and Methods. The organic synthesis of new Nifedipine analogues containing a 1,2,3-triazole moiety. Computer-aided drug design of new DHP derivatives using pharmacophore screening and molecular docking calculations. Results. Molecular docking of new Nifedipine analogues made it possible to estimate the binding affinity of new Nifedipine derivatives to the rCav1.1 receptor. Pharmacophore screening of a chemical library of analogues, consisting of 796 derivatives, allowed gradually reducing the chemical space and obtaining 26 candidates with high affinity to the rCav1.1 receptor. Using the method of molecular docking, eight hits 5a-h were identified, and the synthesis of the recommended compounds was proposed and performed. Conclusions. The results of molecular docking showed that Nifedipine analogues are characterized by binding affinity to the rCav1.1 receptor at the same level as approved Nifedipine-like drugs. Pharmacophore screening and molecular docking calculations indicate key features of the ligand-receptor interaction that can guide and optimize the synthesis of new Nifedipine analogues as promising new antihypertensive agents. A retrosynthetic approach was proposed, and the recommended compounds were synthesized

Identification of Druggable Binding Sites and Small Molecules as Modulators of TMC1.

Our ability to hear and maintain balance relies on the proper functioning of inner ear sensory hair cells, which translate mechanical stimuli into electrical signals via mechano-electrical transducer (MET) channels, composed of TMC1/2 proteins. However, the therapeutic use of ototoxic drugs, such as aminoglycosides and cisplatin, which can enter hair cells through MET channels, often leads to profound auditory and vestibular dysfunction. Despite extensive research on otoprotective compounds targeting MET channels, our understanding of how small molecule modulators interact with these channels remains limited, hampering the discovery of novel compounds. Here, we propose a structure-based screening approach, integrating 3D-pharmacophore modeling, molecular simulations, and experimental validation. Our pipeline successfully identified several novel compounds and FDA-approved drugs that reduced dye uptake in cultured cochlear explants, indicating MET modulation activity. Molecular docking and free-energy estimations for binding allowed us to identify three potential drug binding sites within the channel pore, phospholipids, and key amino acids involved in modulator interactions. We also identified shared ligand-binding features between TMC and structurally related TMEM16 protein families, providing novel insights into their distinct inhibition, while potentially guiding the rational design of MET-channel-specific modulators. Our pipeline offers a broad application to discover small molecule modulators for a wide spectrum of mechanosensitive ion channels.

Molecular Dynamic Simulation and 3d-pharmacophore Modeling of Alpha Mangostin and Its Derivatives against Estrogen Alpha Receptor

Background: Human estrogen receptor alpha (ERα), which is known to play a role in mediating cell proliferation, metastasis, and resistance to apoptosis, is one of the targets of breast cancer therapies. Alpha mangostin (AM) is an active xanthone compound from Garcinia mangostana L. which has activity as an ERα inhibitor. Objectives: This research aims to predict the pharmacokinetic and toxicity, and to study the molecular interactions of AM derivatives with the ERα using computer-aided simulation approaches through molecular docking, molecular dynamic, and pharmacophore screening to develop novel anti-breast cancer agents. Methods: Marvinsketch and Chimera programs were used to design and optimize the structure of AM and its derivatives. For screening the pharmacokinetic and toxicity profiles, the PreADMET web was used. The AutoDockTools 1.5.6 and LigandScout 4.4.3 Advanced software were used to conduct the molecular docking simulation and pharmacophore screening, respectively, while the molecular dynamic simulation was performed using AMBER 16. The results were visualized by Biovia Discovery Studio. Results: Molecular docking using Autodock showed that FAT10 derivate has lower binding free energy (ΔG) (-12.04 kcal/mol) than AM (-8.45 kcal/mol) when docking to ERα and both performed the same hydrogen bond with Thr347. These support the results of the MMPBSA calculation on dynamic simulation which shows FAT10 (-58.4767 kcal/mol) has lower ΔG than AM (-42.7041 kcal/mol) and 4-OHT (- 49.0821 kcal/mol). The pharmacophore screening results also showed that FAT10 fitted the pharmacophore with a fit score of 47.08. Conclusion: From the results, it can be suggested that FAT10 has promising activity as ERα antagonist. Further in vitro and in vivo experiments should be carried out to support these in silico studies.

DMXP: A De Novo Small-Molecule 3D Structure Predictor with Graph Attention Networks.

Generating the three-dimensional (3D) structure of small molecules is crucial in both structure- and ligand-based drug design. Structure-based drug design needs bioactive conformations of compounds for lead identification and optimization. Ligand-based drug design techniques, such as 3D shape similarity search, 3D pharmacophore model, 3D-QSAR, etc., all require high-quality small-molecule ligand conformations to obtain reliable results. Although predicting a small molecular bioactive conformer requires information from the receptor, a crystal structure of the molecule is a proper approximation to its bioactive conformer in a specific receptor because the binding pose of a small molecule in its receptor's binding pockets should be energetically close to the crystal structures. This study presents a de novo small molecular structure predictor (dMXP) with graph attention networks based on crystal data derived from the Cambridge Structural Database (CSD) combined with molecular electrostatic information calculated by density-functional theory (DFT). Two featuring strategies (topological and atomic partial change features) were employed to explore the relation between these features and the 3D crystal structure of a small molecule. These features were then assembled to construct the holistic 3D crystal structure of a molecule. Molecular graphs were encoded using a graph attention mechanism to deal with the issues of the inconsistencies of local substructures contributing to the entire molecular structure. The root-mean-square deviation (RMSDs) of approximately 80% dMXP predicted structures and the native binding poses within receptors are less than 2.0 Å.

Ensemble-Based Virtual Screening Led to the Discovery of Novel Lead Molecules as Potential NMBAs.

Neuromuscular blocking agents (NMBAs) are routinely used during anesthesia to relax skeletal muscle. Nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels; NMBAs can induce muscle paralysis by preventing the neurotransmitter acetylcholine (ACh) from binding to nAChRs situated on the postsynaptic membranes. Despite widespread efforts, it is still a great challenge to find new NMBAs since the introduction of cisatracurium in 1995. In this work, an effective ensemble-based virtual screening method, including molecular property filters, 3D pharmacophore model, and molecular docking, was applied to discover potential NMBAs from the ZINC15 database. The results showed that screened hit compounds had better docking scores than the reference compound d-tubocurarine. In order to further investigate the binding modes between the hit compounds and nAChRs at simulated physiological conditions, the molecular dynamics simulation was performed. Deep analysis of the simulation results revealed that ZINC257459695 can stably bind to nAChRs' active sites and interact with the key residue Asp165. The binding free energies were also calculated for the obtained hits using the MM/GBSA method. In silico ADMET calculations were performed to assess the pharmacokinetic properties of hit compounds in the human body. Overall, the identified ZINC257459695 may be a promising lead compound for developing new NMBAs as an adjunct to general anesthesia, necessitating further investigations.

Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model: an in silico investigation of apelin agonists.

Introduction: 3D pharmacophore models describe the ligand's chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design. Methods: Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for 6,944 compounds of APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening. Results: This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by Receiver Operating Characteristic (AUC score of 0.994 ± 0.007), enrichment factor of (EF1% of 50.07 ± 0.211), Güner-Henry score of 0.956 ± 0.015, and F-measure of 0.911 ± 0.031. Discussion: Although one of the high-scoring models achieved statistically superior results in each dataset (AUC of 0.82; an EF1% of 19.466; GH of 0.131 and F1-score of 0.071), the ensemble learning method including voting and stacking method balanced the shortcomings of each model and passed with close performance measures.

The discovery of a novel IκB kinase β inhibitor based on pharmacophore modeling, virtual screening and biological evaluation.

Background: IκB kinase β (IKKβ) plays a pivotal role in the NF-κB signaling pathway and is considered a promising therapeutic target for various diseases. Materials & methods: The authors developed and validated a 3D pharmacophore model of IKKβ inhibitors via the HypoGen algorithm in Discovery Studio 2019, then performed virtual screening, molecular docking and kinase assays to identify hit compounds from the ChemDiv database. The compound with the highest inhibitory activity was further evaluated in adjuvant-induced arthritis rat models. Results: Among the four hit compounds, Hit 4 had the highest IKKβ inhibitory activity (IC50=30.4±3.8), and it could significantly ameliorate joint inflammation and damage in vivo. Conclusion: The identified compound, Hit 4, can be optimized as a therapeutic agent for inflammatory diseases.

Discovery of dual kinase inhibitors targeting VEGFR2 and FAK: structure-based pharmacophore modeling, virtual screening, and molecular docking studies

VEGFR2 and FAK signaling pathways are interconnected and have synergistic effects on tumor angiogenesis, growth, and metastasis. Thus, instead of the conventional targeting of each of these proteins individually with a specific inhibitor, the present work aimed to discover novel dual inhibitors targeting both VEGFR2 and FAK exploiting their association. To this end, receptor-based pharmacophore modeling technique was opted to generate 3D pharmacophore models for VEGFR2 and FAK type II kinase inhibitors. The generated pharmacophore models were validated by assessing their ability to discriminate between active and decoy compounds in a pre-compiled test set of VEGFR2 and FAK active compounds and decoys. ZINCPharmer web tool was then used to screen the ZINC database purchasable subset using the validated pharmacophore models retrieving 42,616 hits for VEGFR2 and 28,475 hits for FAK. Subsequently, they were filtered using various filters leaving 13,023 and 6,832 survived compounds for VEGFR2 and FAK, respectively, with 124 common compounds. Based on molecular docking simulations, thirteen compounds were found to satisfy all necessary interactions with VEGFR2 and FAK kinase domains. Thus, they are predicted to have a possible dual VEGFR2/FAK inhibitory activity. Finally, SwissADME web tool showed that compound ZINC09875266 is not only promising in terms of binding pattern to our target kinases, but also in terms of pharmacokinetic properties.

Clustering of atoms relative to vector space in the Z-matrix coordinate system and 'graphical fingerprint' analysis of 3D pharmacophore structure.

The behavior of a molecule within its environment is governed by chemical fields present in 3D space. However, beyond local descriptors in 3D, the conformations a molecule assumes, and the resulting clusters also play a role in influencing structure-activity models. This study focuses on the clustering of atoms according to the vector space of four atoms aligned in the Z-Matrix Reference system for molecular similarity. Using 3D-QSAR analysis, it was aimed to determine the pharmacophore groups as interaction points in the binding region of the β2-adrenoceptor target of fenoterol stereoisomers. Different types of local reactive descriptors of ligands have been used to elucidate points of interaction with the target. Activity values for ligand-receptor interaction energy were determined using the Levenberg-Marquardt algorithm. Using the Molecular Comparative Electron Topology method, the 3D pharmacophore model (3D-PhaM) was obtained after aligning and superimposing the molecules and was further validated by the molecular docking method. Best guesses were calculated with a non-output validation (LOO-CV) method. Finally, the data were calculated using the 'graphic fingerprint' technique. Based on the eLKlopman (Electrostatic LUMO Klopman) descriptor, the Q2 value of this derivative set was calculated as 0.981 and the R2ext value is calculated as 0.998.

Lead generation of UPPS inhibitors targeting MRSA: Using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking, and molecular dynamic simulations

Undecaprenyl Pyrophosphate Synthase (UPPS) is a vital target enzyme in the early stages of bacterial cell wall biosynthesis. UPPS inhibitors have antibacterial activity against resistant strains such as MRSA and VRE. In this study, we used several consecutive computer-based protocols to identify novel UPPS inhibitors. The 3D QSAR pharmacophore model generation (HypoGen algorithm) protocol was used to generate a valid predictive pharmacophore model using a set of UPPS inhibitors with known reported activity. The developed model consists of four pharmacophoric features: one hydrogen bond acceptor, two hydrophobic, and one aromatic ring. It had a correlation coefficient of 0.86 and a null cost difference of 191.39, reflecting its high predictive power. Hypo1 was proven to be statistically significant using Fischer’s randomization at a 95% confidence level. The validated pharmacophore model was used for the virtual screening of several databases. The resulting hits were filtered using SMART and Lipinski filters. The hits were docked into the binding site of the UPPS protein, affording 70 hits with higher docking affinities than the reference compound (6TC, − 21.17 kcal/mol). The top five hits were selected through extensive docking analysis and visual inspection based on docking affinities, fit values, and key residue interactions with the UPPS receptor. Moreover, molecular dynamic simulations of the top hits were performed to confirm the stability of the protein–ligand complexes, yielding five promising novel UPPS inhibitors.Graphical

Identification of novel inhibitors against VP40 protein of Marburg virus by integrating molecular modeling and dynamics approaches

Marburg virus (MV) is a highly etiological agent of haemorrhagic fever in humans and has spread across the world. Its outbreaks caused a 23–90% human death rate. However, there are currently no authorized preventive or curative measures yet. VP40 is the MV matrix protein, which builds protein shell underneath the viral envelope and confers hallmark filamentous. VP40 alone is able to induce assembly and budding of filamentous virus-like particles (VLPs), which resemble authentic virions. As a result, this research is credited with clarifying the function of VP40 and leading to the discovery of new therapeutic targets effective in combating MV disease (MVD). Virtual screening, molecular docking and molecular dynamics (MD) simulation were used to find the putative active chemicals based on a 3D pharmacophore model of the protein’s active site cavity. Initially, andrographidine-C, a potent inhibitor was selected for the development of the pharmacophore model. Later, a library of 30,000 compounds along with the andrographidine-C was docked against VP40 protein. Three best hits including avanafil, diuvaretin and macrourone were subjected to further MD simulation analysis, as these compounds had better binding affinities as compared to andrographidine-C. Furthermore, throughout the 100 ns simulations, the back bone of VP40 protein in presence of avanafil, diuvaretin and macrourone remained stable which was further validated by MM-PBSA analysis. Additionally, all of these compounds depict maximum drug-like properties. The predicted drugs based on the ligand, avanafil, diuvaretin and macrourone could be exploited and developed as an alternative or complementary therapy for the treatment of MVD. Communicated by Ramaswamy H. Sarma

Virtual screening approach for the discovery of selective 5α-reductase type II inhibitors for benign prostatic hyperplasia treatment.

Background: 5α-Reductase type II (5αR2) inhibition is a promising strategy for benign prostatic hyperplasia treatment. A computational approach including virtual screening, ligand-based 3Dpharmacophore modeling, 2D quantitative structure-activity relationship and molecular docking simulations were adopted to develop novel inhibitors. Results: Hits were first filtered via the validated pharmacophore and 2D quantitative structure-activity relationship models. Docking on the recently determined cocrystallized structure of 5αR2 showed three promising hits. Visual inspection results were compared with finasteride ligand and dihydrotestosterone as reference, to explain the role of binding to Glu57 and Tyr91 for 5αR2 selective inhibition. Conclusion: Alignment between Hit 2 and finasteride in the binding pocket showed similar binding modes. The biological activity prediction showed antitumor and androgen targeting activity of the new hits.

Design, Synthesis and 3D-Pharmacophore Modeling of some Novel Compounds and their Herbicidal Activity

Design, Synthesis and 3D-Pharmacophore Modeling of some Novel Compounds and their Herbicidal Activity

Identification of Anti-Influenza A Compounds Inhibiting the Viral Non-Structural Protein 1 (NS1) Using a Type I Interferon-Driven Screening Strategy.

There is an urgent need to identify efficient antiviral compounds to combat existing and emerging RNA virus infections, particularly those related to seasonal and pandemic influenza outbreaks. While inhibitors of the influenza viral integral membrane proton channel protein (M2), neuraminidase (NA), and cap-dependent endonuclease are available, circulating influenza viruses acquire resistance over time. Thus, the need for the development of additional anti-influenza drugs with novel mechanisms of action exists. In the present study, a cell-based screening assay and a small molecule library were used to screen for activities that antagonized influenza A non-structural protein 1 (NS1), a highly conserved, multifunctional accessory protein that inhibits the type I interferon response against influenza. Two potential anti-influenza agents, compounds 157 and 164, were identified with anti-NS1 activity, resulting in the reduction of A/PR/8/34(H1N1) influenza A virus replication and the restoration of IFN-β expression in human lung epithelial A549 cells. A 3D pharmacophore modeling study of the active compounds provided a glimpse of the structural motifs that may contribute to anti-influenza virus activity. This screening approach is amenable to a broader analysis of small molecule compounds to inhibit other viral targets.

A structural approach to investigate halogen substituted MAO-B inhibitors using QSAR modeling, molecular dynamics, and conceptual DFT analysis

Halogenated inhibitors showed robust, reversible, and selective monoamine oxidase-B (MAO-B) inhibitory efficacy in candidates that were derived from them. Our team has previously synthesized and assessed a panel of halogenated chalcones and coumarin for the study on MAO-B inhibition. The aim of this study was to build GA-MLR based QSAR models and predictive 3D Pharmacophore models, as well as to investigate the relationship between halogenated derivatives and MAO-B inhibitory activity. The robust statistical significance in the parameter (R2 = 0.78 and Q2 = 0.69) was demonstrated. Best Hypo1 contains one hydrophobic and two aromatic rings. The lead molecule for quantum mechanics was performed, and it was revealed that it would bind to proteins and provide stability. To determine the stability of the ligand-enzyme complex, a thorough molecular dynamics analysis of the lead compounds was accomplished.