In silico molecular investigations of pyridine N-Oxide compounds as potential inhibitors of SARS-CoV-2: 3D QSAR, molecular docking modeling, and ADMET screening

Computational investigation of imidazo[2,1-b]oxazole derivatives as potential mutant BRAF kinase inhibitors: 3D-QSAR, molecular docking, molecular dynamics simulation, and ADMETox studies

BRAF inhibitors are known to be an effective therapeutic target for treating melanoma and other types of cancer. Using 3D-QSAR, molecular docking, and MD simulations, this study evaluated various imidazo[2,1-b]oxazole derivatives that function as mutant BRAF kinase inhibitors. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were used to create the 3D-QSAR models. CoMSIA/SEHA model has solid predictive power across several models (Q2 = 0.578; R2 = 0.828; R2pred = 0.74) and is the best model according to the numerous field models generated. The created model's predictive power was evaluated through external validation using a test set. CoMSIA/SEHA contour maps collect information that can be used to identify critical regions with solid anticancer activity. We developed four inhibitors with high predicted activity due to these observations. ADMET prediction was used to assess the toxicity of the proposed imidazo[2,1-b]oxazole compounds. The predictive molecules (T1-T4) demonstrated good ADMET properties, excluding the toxic active compounds 11r from the database. Molecular docking was also used to determine the patterns and modes of interactions between imidazo[2,1-b]oxazole ligands and receptors, which revealed that the proposed imidazo[2,1-b]oxazole scaffold was stable in the receptor's active site (PDB code: 4G9C). The suggested compounds (T1-T4) were subjected to molecular dynamics simulations lasting 100 ns to determine their binding free energies. The results showed that T2 had a more favorable binding free energy (-149.552 kJ/mol) than T1 (-112.556 kJ/mol), T3 (-115.503 kJ/mol), and T4 (-102.553 kJ/mol). The results suggest that the imidazo[2,1-b]oxazole compounds investigated in this study have potential as inhibitors of BRAF kinase and could be further developed as anticancer drugs. Highlights22 imidazo[2,1-b]oxazole compounds were subjected to research on three-dimensional quantitative conformational relationships.Using contour maps from 3D-QSAR models as a guide was used to figure out the areas and strategies for structural optimization.Combined molecular docking, molecular dynamics simulations, and binding free energy calculations to verify the inhibitor activity of the proposed 22 imidazo[2,1-b]oxazole compounds.Four potential B-RAF Kinase inhibitors were discovered, providing theoretical clues for developing a highly anticancer agent.Communicated by Ramaswamy H. Sarma.

Computer-Aided Strategy on 5-(Substituted benzylidene) Thiazolidine-2,4-Diones to Develop New and Potent PTP1B Inhibitors: QSAR Modeling, Molecular Docking, Molecular Dynamics, PASS Predictions, and DFT Investigations

A set of 5-(substituted benzylidene) thiazolidine-2,4-dione derivatives was explored to study the main structural requirement for the design of protein tyrosine phosphatase 1B (PTP1B) inhibitors. Utilizing multiple linear regression (MLR) analysis, we constructed a robust quantitative structure-activity relationship (QSAR) model to predict inhibitory activity, resulting in a noteworthy correlation coefficient (R2) of 0.942. Rigorous cross-validation using the leave-one-out (LOO) technique and statistical parameter calculations affirmed the model's reliability, with the QSAR analysis revealing 10 distinct structural patterns influencing PTP1B inhibitory activity. Compound 7e(ref) emerged as the optimal scaffold for drug design. Seven new PTP1B inhibitors were designed based on the QSAR model, followed by molecular docking studies to predict interactions and identify structural features. Pharmacokinetics properties were assessed through drug-likeness and ADMET studies. After that density functional theory (DFT) was conducted to assess the stability and reactivity of potential diabetes mellitus drug candidates. The subsequent dynamic simulation phase provided additional insights into stability and interactions dynamics of the top-ranked compound 11c. This comprehensive approach enhances our understanding of potential drug candidates for treating diabetes mellitus.

Recent advances in chemometric modelling of inhibitors against SARS-CoV-2

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused great harm to all countries worldwide. This disease can be prevented by vaccination and managed using various treatment methods, including injections, oral medications, or aerosol therapies. However, the selection of suitable compounds for the research and development of anti-SARS-CoV-2 drugs is a daunting task because of the vast databases of available compounds. The traditional process of drug research and development is time-consuming, labour-intensive, and costly. The application of chemometrics can significantly expedite drug R&D. This is particularly necessary and important for drug development against pandemic public emergency diseases, such as COVID-19. Through various chemometric techniques, such as quantitative structure-activity relationship (QSAR) modelling, molecular docking, and molecular dynamics (MD) simulations, compounds with inhibitory activity against SARS-CoV-2 can be quickly screened, allowing researchers to focus on the few prioritised candidates. In addition, the ADMET properties of the screened candidate compounds should be further explored to promote the successful discovery of anti-SARS-CoV-2 drugs. In this case, considerable time and economic costs can be saved while minimising the need for extensive animal experiments, in line with the 3R principles. This paper focuses on recent advances in chemometric modelling studies of COVID-19-related inhibitors, highlights current limitations, and outlines potential future directions for development.

Exploring Citrus sinensis Phytochemicals as Potential Inhibitors for Breast Cancer Genes BRCA1 and BRCA2 Using Pharmacophore Modeling, Molecular Docking, MD Simulations, and DFT Analysis

BACKGROUND

Structure-activity relationship (SAR) is considered to be an effective in silico approach when discovering potential antagonists for breast cancer due to gene mutation. Major challenges are faced by conventional SAR in predicting novel antagonists due to the discovery of diverse antagonistic compounds. Methodologyand Results: In predicting breast cancer antagonists, a multistep screening of phytochemicals isolated from the seeds of the Citrus sinensis plant was applied using feasible complementary methodologies. A three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed through the Flare project, in which conformational analysis, pharmacophore generation, and compound alignment were done. Ten hit compounds were obtained through the development of the 3D-QSAR model. For exploring the mechanism of action of active compounds against cocrystal inhibitors, molecular docking analysis was done through Molegro software (MVD) to identify lead compounds. Three new proteins, namely, 1T15, 3EU7, and 1T29, displayed the best Moldock scores. The quality of the docking study was assessed by a molecular dynamics simulation. Based on binding affinities to the receptor in the docking studies, three lead compounds (stigmasterol P8, epoxybergamottin P28, and nobiletin P29) were obtained, and they passed through absorption, distribution, metabolism, and excretion (ADME) studies via the SwissADME online service, which proved that P28 and P29 were the most active allosteric inhibitors with the lowest toxicity level against breast cancer. Then, density functional theory (DFT) studies were performed to measure the active compound's reactivity, hardness, and softness with the help of Gaussian 09 software.

CONCLUSIONS

This multistep screening of phytochemicals revealed high-reliability antagonists of breast cancer by 3D-QSAR using flare, docking analysis, and DFT studies. The present study helps in providing a proper guideline for the development of novel inhibitors of BRCA1 and BRCA2.

Recent Advances in Nitrogen-Containing Heterocyclic Scaffolds as Antiviral Agents

This study aims to provide a thorough analysis of nitrogen-containing heterocycles, focusing on their therapeutic implications for the development of targeted and effective antiviral drugs. To better understand how nitrogen-containing heterocycles can be used to create antiviral drugs, this review adopts a systematic literature review strategy to compile and analyze pertinent research studies. It combines information from various fields to understand better the compounds' mode of action and their therapeutic potential. This review paper summarizes data from multiple sources to highlight the promising potential of heterocycles containing nitrogen as promising possibilities for future antiviral treatments. The capacity to engage selectively and modulate critical pathways bodes well for their use in developing new viral therapies. In conclusion, nitrogen-containing heterocycles are shown to be of utmost importance in the field of medicinal chemistry, as emphasized by the review paper. It emphasizes the central importance of chemical insights and pharmacological potential in developing novel and effective antiviral medicines by bringing them together.

Synthesis, molecular modeling Insights, and anticancer assessment of novel polyfunctionalized Pyridine congeners

The present study describes synthesizing a novel series of polyfunctionalized pyridine congeners 1-18 and assessed for cytotoxic efficacies versus HCT-116, MCF-7, and HepG-2 among one non-cancerous BJ-1 human normal cell. Most compounds were precisely potent anticancer candidate drugs. The molecular impact of the most active compounds 9, 10, 11, 13, 15, and 17 was evaluated after MCF-7 treatment. The gene expression of pro- and ant-apoptosis markers P53, Bax, Caspase-3 and Bcl-2 as well as VEGFR-2 and HER2 were determined. Compounds 13 and 15 induced upregulation of pro-apoptosis of P53, Bax, Caspase-3 and downregulation of anti-apoptosis Bcl-2 gene. However, compound 15 showed higher effect compared to 13 and respective control. Moreover, a slight reduction in HER2 gene expression was detected due to compound 15 treatment, while VEGFR-2 gene was upregulated. In agreement, the immunoblotting analysis showed higher accumulation of P53, Bax, Caspase-3 proteins and of decrease the Bcl-2 protein levels. Furthermore, docking studies united with molecular dynamic simulation exposed compounds 13 and 15 fitting in the middle of the active site at the interface linking the ATP binding site and the allosteric hydrophobic binding pocket. Finally, we performed Petra/Osiris/ Molinspiration (POM) analysis for the newly synthesized compounds. The evaluation of primary in silico parameters revealed significant differences among individual polyfunctionalized pyridine compounds, highlighting the most promising candidates. These preliminary results may help in coordinating and initiating other research projects focused on polyfunctionalized pyridine compounds, especially those with predicted bioactivity, low toxicity, optimal ADME parameters, and promising perspectives.

Chemical Profiling and Biological Properties of Essential Oils of Lavandula stoechas L. Collected from Three Moroccan Sites: In Vitro and In Silico Investigations

The aim of this study was the determination of the chemical compounds of Lavandula stoechas essential oil from Aknol (LSEO_A), Khenifra (LSEO_K), and Beni Mellal (LSEO_B), and the in vitro investigation of their antibacterial, anticandidal, and antioxidant effects, and in silico anti-SARS-CoV-2 activity. The chemical profile of LSEO was determined using GC-MS-MS analysis, the results of which showed a qualitative and quantitative variation in the chemical composition of volatile compounds including L-fenchone, cubebol, camphor, bornyl acetate, and τ-muurolol; indicating that the biosynthesis of essential oils of Lavandula stoechas (LSEO) varied depending on the site of growth. The antioxidant activity was evaluated using the ABTS and FRAP methods, our results showed that this tested oil is endowed with an ABTS inhibitory effect and an important reducing power which varies between 4.82 ± 1.52 and 15.73 ± 3.26 mg EAA/g extract. The results of antibacterial activity of LSEO_A, LSEO_K and LSEO_B, tested against Gram-positive and Gram-negative bacteria, revealed that B. subtilis (20.66 ± 1.15-25 ± 4.35 mm), P. mirabilis (18.66 ± 1.15-18.66 ± 1.15 mm), and P. aeruginosa (13.33 ± 1.15-19 ± 1.00 mm) are the most susceptible strains to LSEO_A, LSEO_K and LSEO_B of which LSEO_B exhibits bactericidal effect against P. mirabilis. furthermore The LSEO exhibited varying degrees of anticandidal activity with an inhibition zones of 25.33 ± 0.5, 22.66 ± 2.51, and 19 ± 1 mm for LSEO_K, LSEO_B, and LSEO_A, respectively. Additionally, the in silico molecular docking process, performed using Chimera Vina and Surflex-Dock programs, indicated that LSEO could inhibit SARS-CoV-2. These important biological properties of LSEO qualify this plant as an interesting source of natural bioactive compounds with medicinal actions.

Synthesis, Molecular Docking, and Dynamic Simulation Targeting Main Protease (Mpro) of New, Thiazole Clubbed Pyridine Scaffolds as Potential COVID-19 Inhibitors

Many biological activities of pyridine and thiazole derivatives have been reported, including antiviral activity and, more recently, as COVID-19 inhibitors. Thus, in this paper, we designed, synthesized, and characterized a novel series of N-aminothiazole-hydrazineethyl-pyridines, beginning with a N'-(1-(pyridine-3-yl)ethylidene)hydrazinecarbothiohydrazide derivative and various hydrazonoyl chlorides and phenacyl bromides. Their Schiff bases were prepared from the condensation of N-aminothiazole derivatives with 4-methoxybenzaldehyde. FTIR, MS, NMR, and elemental studies were used to identify new products. The binding energy for non-bonding interactions between the ligand (studied compounds) and receptor was determined using molecular docking against the SARS-CoV-2 main protease (PDB code: 6LU7). Finally, the best docked pose with highest binding energy (8a = -8.6 kcal/mol) was selected for further molecular dynamics (MD) simulation studies to verify the outcomes and comprehend the thermodynamic properties of the binding. Through additional in vitro and in vivo research on the newly synthesized chemicals, it is envisaged that the achieved results will represent a significant advancement in the fight against COVID-19.

[The prediction of SARS-CoV-2 main protease inhibition with filtering by position of ligand]

The paper analyzes a set of equations that adequately predict the IC50 value for SARS-CoV-2 main protease inhibitors. The training set was obtained using filtering by criteria independent of prediction of target value. It included 76 compounds, and the test set included nine compounds. We used the values of energy contributions obtained in the calculation of the change of the free energy of complex by MMGBSA method and a number of characteristics of the physical and chemical properties of the inhibitors as independent variables. It is sufficient to use only seven independent variables without loss of prediction quality (Q² = 0.79; R²prediction = 0.89). The maximum error in this case does not exceed 0.92 lg(IC50) units with a full range of observed values from 1.26 to 4.95.

In silico study of natural antioxidants

Antioxidants are the body's defense system against the damage of reactive oxygen species, which are usually produced in the body through various physiological processes. There are various sources of these antioxidants such as endogenous antioxidants in the body and exogenous food sources. This chapter provides important information on methods used to investigate antioxidant activity and sources of plant antioxidants. Over the past two decades, numerous studies have demonstrated the importance of in silico research in the development of novel natural and synthesized antioxidants. In silico methods such as quantitative structure-activity relationships (QSAR), pharmacophore, docking, and virtual screenings are play critical roles in designing effective antioxidants that may be synthesized and tested later. This chapter introduces the available in silico approaches for different classes of antioxidants. Many successful applications of in silico methods in the development and design of novel antioxidants are thoroughly discussed. The QSAR, pharmacophore, molecular docking techniques, and virtual screenings process summarized here would help readers to find out the proper mechanism for the interaction between the free radicals and antioxidant compounds. Furthermore, this chapter focuses on introducing new QSAR models in combination with other in silico methods to predict antioxidants activity and design more active antioxidants. In silico studies are essential to explore largely unknown plant tissue, food sources for antioxidant synthesis, as well as saving time and money in such studies.

Deciphering the Potential of Pre and Pro-Vitamin D of Mushrooms against Mpro and PLpro Proteases of COVID-19: An In Silico Approach

Vitamin D’s role in combating the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the virus causing COVID-19, has been established in unveiling viable inhibitors of COVID-19. The current study investigated the role of pre and pro-vitamin D bioactives from edible mushrooms against Mpro and PLpro proteases of SARS-CoV-2 by computational experiments. The bioactives of mushrooms, specifically ergosterol (provitamin D₂), 7-dehydrocholesterol (provitamin-D₃), 22,23-dihydroergocalciferol (provitamin-D₄), cholecalciferol (vitamin-D₃), and ergocalciferol (vitamin D₂) were screened against Mpro and PLpro. Molecular docking analyses of the generated bioactive protease complexes unravelled the differential docking energies, which ranged from −7.5 kcal/mol to −4.5 kcal/mol. Ergosterol exhibited the lowest binding energy (−7.5 kcal/mol) against Mpro and PLpro (−5.9 kcal/mol). The Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) and MD simulation analyses indicated that the generated complexes were stable, thus affirming the putative binding of the bioactives to viral proteases. Considering the pivotal role of vitamin D bioactives, their direct interactions against SARS-CoV-2 proteases highlight the promising role of bioactives present in mushrooms as potent nutraceuticals against COVID-19.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Pyridine Compounds with Antimicrobial and Antiviral Activities

In the context of the new life-threatening COVID-19 pandemic caused by the SARS-CoV-2 virus, finding new antiviral and antimicrobial compounds is a priority in current research. Pyridine is a privileged nucleus among heterocycles; its compounds have been noted for their therapeutic properties, such as antimicrobial, antiviral, antitumor, analgesic, anticonvulsant, anti-inflammatory, antioxidant, anti-Alzheimer’s, anti-ulcer or antidiabetic. It is known that a pyridine compound, which also contains a heterocycle, has improved therapeutic properties. The singular presence of the pyridine nucleus, or its one together with one or more heterocycles, as well as a simple hydrocarbon linker, or grafted with organic groups, gives the key molecule a certain geometry, which determines an interaction with a specific protein, and defines the antimicrobial and antiviral selectivity for the target molecule. Moreover, an important role of pyridine in medicinal chemistry is to improve water solubility due to its poor basicity. In this article, we aim to review the methods of synthesis of pyridine compounds, their antimicrobial and antiviral activities, the correlation of pharmaceutical properties with various groups present in molecules as well as the binding mode from Molecular Docking Studies.

In silico design of RORγ inverse agonists based on 3D-QSAR and molecular docking

Computationally designing novel RORγ inverse agonists with higher activity using a systematic modeling study.

Applications of density functional theory in COVID-19 drug modeling

The rapidly evolving Coronavirus 2019 (COVID-19) pandemic has led to millions of deaths around the world, highlighting the pressing need to develop effective antiviral pharmaceuticals. Recent efforts with computer-aided rational drug discovery have allowed detailed examination of drug–macromolecule interactions primarily by molecular mechanics (MM) techniques. Less widely applied in COVID-19 drug modeling is density functional theory (DFT), a quantum mechanics (QM) method that enables electronic structure calculations and elucidations of reaction mechanisms. Here, we review recent advances in applying DFT in molecular modeling studies of COVID-19 pharmaceuticals. We start by providing an overview of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) drugs and targets, followed by a brief introduction to DFT. We then provide a discussion of different approaches by which DFT has been applied. Finally, we discuss essential factors to consider when incorporating DFT in future drug modeling research.

Strengths and Weaknesses of Docking Simulations in the SARS-CoV-2 Era: the Main Protease (Mpro) Case Study

The scientific community is working against the clock to arrive at therapeutic interventions to treat patients with COVID-19. Among the strategies for drug discovery, virtual screening approaches have the capacity to search potential hits within millions of chemical structures in days, with the appropriate computing infrastructure. In this article, we first analyzed the published research targeting the inhibition of the main protease (Mpro), one of the most studied targets of SARS-CoV-2, by docking-based methods. An alarming finding was the lack of an adequate validation of the docking protocols (i.e., pose prediction and virtual screening accuracy) before applying them in virtual screening campaigns. The performance of the docking protocols was tested at some level in 57.7% of the 168 investigations analyzed. However, we found only three examples of a complete retrospective analysis of the scoring functions to quantify the virtual screening accuracy of the methods. Moreover, only two publications reported some experimental evaluation of the proposed hits until preparing this manuscript. All of these findings led us to carry out a retrospective performance validation of three different docking protocols, through the analysis of their pose prediction and screening accuracy. Surprisingly, we found that even though all tested docking protocols have a good pose prediction, their screening accuracy is quite limited as they fail to correctly rank a test set of compounds. These results highlight the importance of conducting an adequate validation of the docking protocols before carrying out virtual screening campaigns, and to experimentally confirm the predictions made by the models before drawing bold conclusions. Finally, successful structure-based drug discovery investigations published during the redaction of this manuscript allow us to propose the inclusion of target flexibility and consensus scoring as alternatives to improve the accuracy of the methods.

An efficient and regioselective biocatalytic synthesis of aromatic N-oxides by using a soluble di-iron monooxygenase PmlABCDEF produced in the Pseudomonas species

Here, we present an improved whole-cell biocatalysis system for the synthesis of heteroaromatic N-oxides based on the production of a soluble di-iron monooxygenase PmlABCDEF in Pseudomonas sp. MIL9 and Pseudomonas putida KT2440. The presented biocatalysis system performs under environmentally benign conditions, features a straightforward and inexpensive procedure and possesses a high substrate conversion and product yield. The capacity of gram-scale production was reached in the simple shake-flask cultivation. The template substrates (pyridine, pyrazine, 2-aminopyrimidine) have been converted into pyridine-1-oxide, pyrazine-1-oxide and 2-aminopyrimidine-1-oxide in product titres of 18.0, 19.1 and 18.3 g l-1 , respectively. To our knowledge, this is the highest reported productivity of aromatic N-oxides using biocatalysis methods. Moreover, comparing to the chemical method of aromatic N-oxides synthesis based on meta-chloroperoxybenzoic acid, the developed approach is applicable for a regioselective oxidation that is an additional advantageous option in the preparation of the anticipated N-oxides.

Mechanisms of Proteolytic Enzymes and Their Inhibition in QM/MM Studies

Experimental evidence for enzymatic mechanisms is often scarce, and in many cases inadvertently biased by the employed methods. Thus, apparently contradictory model mechanisms can result in decade long discussions about the correct interpretation of data and the true theory behind it. However, often such opposing views turn out to be special cases of a more comprehensive and superior concept. Molecular dynamics (MD) and the more advanced molecular mechanical and quantum mechanical approach (QM/MM) provide a relatively consistent framework to treat enzymatic mechanisms, in particular, the activity of proteolytic enzymes. In line with this, computational chemistry based on experimental structures came up with studies on all major protease classes in recent years; examples of aspartic, metallo-, cysteine, serine, and threonine protease mechanisms are well founded on corresponding standards. In addition, experimental evidence from enzyme kinetics, structural research, and various other methods supports the described calculated mechanisms. One step beyond is the application of this information to the design of new and powerful inhibitors of disease-related enzymes, such as the HIV protease. In this overview, a few examples demonstrate the high potential of the QM/MM approach for sophisticated pharmaceutical compound design and supporting functions in the analysis of biomolecular structures.

Exploring QSPR models for predicting PUF-air partition coefficients of organic compounds with linear and nonlinear approaches

Partition coefficients are important parameters for measuring the concentration of chemicals by passive sampling devices. Considering the wide application of the polyurethane foam (PUF) in passive air sampling, an attempt for developing several quantitative structure-property relationship (QSPR) models was made in this work, to predict PUF-air partition coefficients (K_PUF-air) using linear (multiple linear regression, MLR) and non-linear (artificial neural network, ANN and support vector machine, SVM) methods by machine learning. All of the developed models were performed on a dataset of 170 compounds comprising 9 distinct classes. A series of statistical parameters and validation results showed that models had good prediction ability, robustness and goodness-of-fit. Furthermore, the underlying mechanisms of molecular descriptors emphasized that ionization potential, molecular bond, hydrophilicity, size of molecule and valence electron number had dominating influence on the adsorption process of chemicals. Overall, the obtained models were all established on the extensive applicability domains, and thus can be used as effective tools to predict the K_PUF-air of new organic compounds or those have not been synthesized yet which, in turn, could help researchers better understand the mechanistic basis of adsorption behavior of PUF.

Novel and versatile artificial intelligence algorithms for investigating possible GHSR1α and DRD1 agonists for Alzheimer's disease

Hippocampal lesions are recognized as the earliest pathological changes in Alzheimer's disease (AD). Recent researches have shown that the co-activation of growth hormone secretagogue receptor 1α (GHSR1α) and dopamine receptor D1 (DRD1) could recover the function of hippocampal synaptic and cognition. We combined traditional virtual screening technology with artificial intelligence models to screen multi-target agonists for target proteins from TCM database and a novel boost Generalized Regression Neural Network (GRNN) model was proposed in this article to improve the poor adjustability of GRNN. R-square was chosen to evaluate the accuracy of these artificial intelligent models. For the GHSR1α agonist dataset, Adaptive Boosting (AdaBoost), Linear Ridge Regression (LRR), Support Vector Machine (SVM), and boost GRNN achieved good results; the R-square of the test set of these models reached 0.900, 0.813, 0.708, and 0.802, respectively. For the DRD1 agonist dataset, Gradient Boosting (GB), Random Forest (RF), SVM, and boost GRNN achieved good results; the R-square of the test set of these models reached 0.839, 0.781, 0.763, and 0.815, respectively. According to these values of R-square, it is obvious that boost GRNN and SVM have better adaptability for different data sets and boost GRNN is more accurate than SVM. To evaluate the reliability of screening results, molecular dynamics (MD) simulation experiments were performed to make sure that candidates were docked well in the protein binding site. By analyzing the results of these artificial intelligent models and MD experiments, we suggest that 2007_17103 and 2007_13380 are the possible dual-target drugs for Alzheimer's disease (AD).

Electronic properties investigation of human dihydrofolate reductase complexes with ligands

Despite the fact that there are already drugs for cancer, they still show strong toxicity to the human organism. That is why it is necessary to establish the factors affecting activity in order to develop new, more effective drugs aimed at tumor cells, minimizing harm to healthy cells. The present research is based on electronic properties calculation of the complexes using AlteQ approach. In the focus of this study are complexes of human dihydrofolate reductase (hDHFR) with a series of known inhibitors bound in the active site. Further, a statistical analysis was performed to establish the relationships between a myriad electronic characteristics and IC₅₀. The change in total volume and the change of own electrons number of hydrogen atoms in their atomic basins are identified as the descriptors correlating the most with the hDHFR inhibition potency. Additionally, two lipophilic parts of protein (Thr56, Ser59, Ile60 and Ile7, Val8, Ala9) were found, which act as a key factor in decreasing bioactivity. The depth analysis of intermolecular interactions showed that the interactions between water molecules and ligand play a crucial role in hDHFR inhibition. Furthermore, the molecular dynamics simulations were used for deeper understanding of the structural inhibition, each for 50 ns time scale in explicit water conditions. Thus, the AlteQ approach made it possible to determine the factors influencing the activity and evaluate them not only qualitatively, but also quantitatively.Communicated by Ramaswamy H. Sarma.