Prediction Of Biological Activity Research Articles

Boosting Sinh Cosh Optimizer and arithmetic optimization algorithm for improved prediction of biological activities for indoloquinoline derivatives

Quantitative Structure Activity Relation (QSAR) models are mathematical techniques used to link structural characteristics with biological activities, thus considered a useful tool in drug discovery, hazard evaluation, and identifying potentially lethal molecules. The QSAR regulations are determined by the Organization for Economic Cooperation and Development (OECD). QSAR models are helpful in discovering new drugs and chemicals to treat severe diseases. In order to improve the QSAR model's predictive power for biological activities of naturally occurring indoloquinoline derivatives against different cancer cell lines, a modified machine learning (ML) technique is presented in this paper. The Arithmetic Optimization Algorithm (AOA) operators are used in the suggested model to enhance the performance of the Sinh Cosh Optimizer (SCHO). Moreover, this improvement functions as a feature selection method that eliminates superfluous descriptors. An actual dataset gathered from previously published research is utilized to evaluate the performance of the suggested model. Moreover, a comparison is made between the outcomes of the suggested model and other established methodologies. In terms of pIC50 values for different indoloquinoline derivatives against human MV4-11 (leukemia), human HCT116 (colon cancer), and human A549 (lung cancer) cell lines, the suggested model achieves root mean square error (RMSE) of 0.6822, 0.6787, 0.4411, and 0.4477, respectively. The biological application of indoloquinoline derivatives as possible anticancer medicines is predicted with a high degree of accuracy by the suggested model, as evidenced by these findings.

Machine Learning of Three-Dimensional Protein Structures to Predict the Functional Impacts of Genome Variation.

Research in the human genome sciences generates a substantial amount of genetic data for hundreds of thousands of individuals, which concomitantly increases the number of variants of unknown significance (VUS). Bioinformatic analyses can successfully reveal rare variants and variants with clear associations with disease-related phenotypes. These studies have had a significant impact on how clinical genetic screens are interpreted and how patients are stratified for treatment. There are few, if any, computational methods for variants comparable to biological activity predictions. To address this gap, we developed a machine learning method that uses protein three-dimensional structures from AlphaFold to predict how a variant will influence changes to a gene's downstream biological pathways. We trained state-of-the-art machine learning classifiers to predict which protein regions will most likely impact transcriptional activities of two proto-oncogenes, nuclear factor erythroid 2 (NFE2L2)-related factor 2 (NRF2) and c-Myc. We have identified classifiers that attain accuracies higher than 80%, which have allowed us to identify a set of key protein regions that lead to significant perturbations in c-Myc or NRF2 transcriptional pathway activities.

Formation of Inclusion Complex of Curcumin and Tetrahydrocurcumin Prevents Angiogenesis by Inhibiting VEGF Activity: An in-Silico Study

Curcumin and tetrahydrocurcumin (THC) are known for their anticancer properties, but limited solubility in water hinders their effectiveness against cancer. In this study, we conducted an in silico exploration of β-cyclodextrin's potential to form inclusion complexes with curcumin or THC. The aim of this study was to assess the potential of curcumin and THC inclusion complexes to inhibit vascular endothelial growth factor (VEGF) signaling pathway, a key element in carcinogenesis. The in silico analysis involved multiple stages, such as bioactive compound preparation, biological activity prediction, 3D structure retrieval of VEGF and VEGFR, protein-ligand docking, and visualization. The results of the study demonstrated that both the curcumin- and THC-inclusion complexes exhibit a lower requirement for binding free energy to interact with VEGFR compared to curcumin or THC molecules alone. When VEGFR binds with curcumin, the curcumin-inclusion complex, or the THC-inclusion complex before interacting with VEGF, there is a notable increase in the binding free energy for the VEGF-VEGFR interaction. Specifically, the presence of THC-inclusion complex demonstrates the highest binding free energy for the VEGF-VEGFR interaction. The molecular dynamic simulation study shows that when VEGFR binds with curcumin, curcumin-inclusion complex, or THC-inclusion complex, the fluctuation of amino acid residues in VEGFR decreases compared to the VEGFR protein structure before binding with these molecules. In conclusion, this study suggests that the formation of inclusion complexes holds considerable promise for enhancing the anticancer potential of curcumin and THC by augmenting their anti-angiogenic activity.

Essential Oil of Ocimum basilicum against Aedes aegypti and Culex quinquefasciatus: Larvicidal Activity of a Nanoemulsion and In Silico Study

Diseases transmitted by vectors such as Aedes aegypti and Culex quinquefasciatus (Diptera: Culicidae) have been responsible for endemics and epidemics in several countries, causing irreparable damage to human health. For this reason, vector control is one of the main strategies to prevent the contamination and dissemination of these etiological agents. In this study, the essential oil (EO) of Ocimum basilicum was obtained by hydrodistillation, and the compounds were identified by GC/MS. A nanoemulsion was obtained through a low-energy input method and evaluated by photon correlation spectroscopy. Bioassays were performed against 4th instar larvae of A. aegypti and C. quinquefasciatus. Furthermore, additional in silico studies of biological activity prediction and molecular docking for the acetylcholinesterase enzyme and juvenile hormone protein targets were performed with the 53 identified compounds of the EO. The major compounds identified in greater quantity were linalool (32.66%) and anethole (32.48%). The obtained nanoemulsion had an average size diameter between 244.6 and 280.4 nm and a polydispersity index below 0.250 during the entire storage period. The nanoemulsion was tested at concentrations of 10, 20, 30, 40, and 50 mg/L and the following activity values were observed: LC50 = 42.15 mg/L and 40.94 mg/L and LC90 = 50.35 mg/L and 48.87 mg/L for A. aegypti (24 h and 48 h); LC50 = 39.64 mg/L and 38.08 mg/L and LC90 = 52.58 mg/L and 54.26 mg/L for C. quinquefasciatus (24 h and 48 h). The in silico results showed better activity values for linalool, anethole, carvone, α-selinene, eugenol, and limonene. The α-selinene compound showed the best binding affinity with the insect acetylcholinesterase enzyme (−9.1 Kcal) in molecular docking, showing the importance of antagonist compounds in elucidating the mechanism of action for the investigated targets. Thus, the studied nanoemulsion was considered active against the tested species, becoming a potential alternative as an ecological bioinsecticide due to bioactivity and simplicity of formulation.

Exploratory Studies on Chrysin via Antioxidant, Antimicrobial, ADMET, PASS and Molecular Docking evaluations

IntroductionChrysin, a flavonoid found in Feng Jiao (Chinese name for propolis), exhibits a wide array of pharmacological activities and is recognized for the significant roles it plays in traditional Chinese medicine. The present study aimed to explore the in vitro antioxidant and antimicrobial potentials of chrysin and provide mechanistic insights via in silico experiments. MethodsThe in vitro antioxidant and antimicrobial activities of chrysin, a natural flavone found in propolis was investigated. Molecular docking interactions, the Adsorption, Distribution, Metabolism, Elimination and Toxicity (ADMET) and Prediction of Activity Spectra for Substances (PASS) were used to predict the biological activities of chrysin. ResultsChrysin exhibited a considerable 2,2-diphenyl-1-picrylhydrazyl (DPPH) radicals scavenging activity and ferric reducing antioxidant capacity. Chrysin demonstrated inhibition against S. typhi, and B. subtilis with lowest MIC value of 1.25 mg/mL which was similar to that of chloramphenicol. Drug-likeness and ADMET study of the two compounds showed their physicochemical properties as non-toxic, non-mutagenic, high permeability, good solubility and absorption characteristics. DiscussionThe research below shows that chrysin and silymarin had antioxidant, free radical scavenging, antibacterial, hepatoprotective and anticarcinogenic properties based on PASS prediction of biological activities. Molecular docking of chrysin and silymarin, a standard hepatoprotective drug, with tumour necrosis factor alpha converting enzyme (TACE) and manganese-dependent superoxide dismutase (SOD 2) showed interactions with LEU 401, VAL 402, HIS 405 amino acid residues at the active sites of TACE. Chrysin non-covalently interacted with amino acids of SOD 2 at the active site while silymarin non-covalently interacted at a site different from the active site. Silymarin demonstrated a minimum binding energy of – 9.7 Kcal/mol and – 7.0 Kcal/mol for TACE and SOD 2, respectively while chrysin showed a minimal binding energy of – 9.0 Kcal/mol and – 6.8 Kcal/mol for TACE and SOD2 respectively. ConclusionThe study concluded that chrysin exhibited antioxidant and antimicrobial activity via inhibition and regulation of TACE and SOD2 and possibly offer protection against hepatotoxicity in animal experimental models.

Nootropic Property of Punica grantum Extract as BDNF4 Stimulant for Treatment of Major Depressive Disorder

In 2008, the World Health Organization (WHO) stressed major depressive disorder (MDD) as the third most important cause of disease burden worldwide. Based on their projections, it is expected that by 2030, MDD will take the lead among the rest of the world’s health concerns. Brain derived neurotrophic factor 4 (BDNF4), a protein of neurotrophins family play pivotal role in maintaining neural plasticity, and its reduced level in the hippocampus and plasma have been reported in patients with MDD. Nootropic drugs serve as therapeutic interventions in mitigating MDD through diverse molecular mechanisms. Punica granatum (pomegranate) is acknowledged for its nutritional and medicinal properties, currently under medical scrutiny for its potential as a natural antidepressant. Various computational methodologies, including molecular docking, pharmacokinetics, ADME (absorption, distribution, metabolism and excretion) profile assessment, toxicological analysis, and prediction of biological activity, were employed to identify promising compounds among the phytochemicals present in Punica extract, focusing on their potential BDNF4-stimulating, nootropic, and antidepressant properties. The comprehensive examination of docking scores, interactions between proteins and ligands, pharmacological and toxicological attributes, along with the forecasting of biological activities, collectively underscores the potential attributes of M-Cymene, Flavylium, 2-(4-Methylphenyl)propan-2-ol, Thymol, and Pelletierine as prospective drug candidates targeting human BDNF4 for alleviating MDD.

Search for molecular descriptors and computer prediction of biological activity in a series of S-derivatives (1,2,4-triazole-3(2H)-yl)methyl)thiopyrimidines

Most pharmaceutical compounds interact with various molecular compositions in the body, leading to complex biological effects. Furthermore, during biotransformation, they may generate one or more metabolites with potent bioactive properties. Therefore, the development and careful use of new medications require thorough examination of their biological impact profiles, including considerations of human metabolic processes. In silico techniques are currently widely used to evaluate the interactions of emerging drug candidates with pharmacological conditions and predict their metabolic conversions. To investigate the bioactivity of compounds based on the structure of 1,2,4-triazole-3(2H)-thione with pyrimidine-2-thiol, we generated a combinatorial library of bioregulators using the computer programs SuperPred and SwissADME. The relevance of this research is underscored by the ongoing quest for novel biomolecular compounds that are highly effective yet low in toxicity, spanning both natural and synthetic sources. Much attention is directed towards nitrogen-containing heterocycles, particularly derivatives of 1,2,4-triazole, due to their substantial medical and biological potential. The aim of the work is to create a combinatorial library of bioregulators, which contains the structures of 1,2,4-triazole and pyrimidine, and to carry out in silico screening of heterocycle derivatives using the SuperPred web server to determine promising directions for the study of their bioactivity. Materials and methods. The subject of our research is S-derivatives of (1,2,4-triazole-3(2H)-yl)methyl)thiopyrimidines. Virtual screening of compounds was carried out using the SuperPred computer program. The ADME study was performed using the free service SwissADME, which is used to evaluate the pharmacokinetics, bioavailability, and interaction of small molecules with enzymes in medicinal chemistry. Results. Predicting the likelihood of a substance demonstrating a particular biological activity enables the selection of the most appropriate tests for studying the activity of a given chemical compound, as well as identifying which substances available to the researcher are most likely to enhance the effectiveness of the effects. This principle is grounded in the concept that “the biological activity of a substance is a function of its chemical structure”. Such predictions are generated based on the structural formula of a chemical compound and can be made during the planning stage of synthesis. Conclusions. A combinatorial library of bioregulators was created by combining the structure of 1,2,4-triazole-3(2H)-thione with pyrimidine-2-thiol. The conducted virtual screening of the 1,2,4-triazole-3(2H)-thione heterocycle with pyrimidine-2-thiol shows promise for obtaining non-toxic compounds with antineoplastic biological activity. Screening of derivatives, particularly S-derivatives of (1,2,4-triazole-3(2H)-yl)methyl)thiopyrimidines, indicates the potential for discovering biologically active substances with antineoplastic, antibacterial, analgesic, antidiabetic, antihypertensive, and other types of biological activities within this compound series.

Identification of Novel EGFR Inhibitors for the Targeted Therapy of Colorectal Cancer Using Pharmacophore Modelling, Docking, Molecular Dynamic Simulation and Biological Activity Prediction.

Colorectal cancer (CRC) is considered the second deadliest cancer in the world. One of the reasons for the occurrence of this cancer is the deregulation of the Epidermal Growth Factor Receptor (EGFR), which plays a critical role in regulating cell division, persistence, differentiation, and migration. The overexpression of the EGFR protein leads to its dysregulation and causes CRC. Hence, this work aims to identify and validate novel EGFR inhibitors for the treatment of colorectal cancer employing various computer aided techniques such as pharmacophore modeling, docking, molecular dynamic simulation and Quantitative Structure-Activity Relationship (QSAR) analysis. In this work, a shared-featured ligand-based pharmacophore model was generated using the known inhibitors of EGFR. The best model was validated and screened against ZincPharmer and Maybridge databases, and 143 hits were obtained. Pharmacokinetic and toxicological properties of these hits were studied, and the acceptable ligands were docked against EGFR. The best five protein-ligand complexes with binding energy less than -5 kcal/mol were selected. The molecular dynamic simulation studies of these complexes were conducted for 100 nanoseconds (ns), and the results were analyzed. The biological activity of this ligand was calculated using QSAR analysis. The best complex with Root Mean Square Deviation (RMSD) 3.429 Å and Radius of Gyration (RoG) 20.181 Å was selected. The Root Mean Square Fluctuations (RMSF) results were also found to be satisfactory. The biological activity of this ligand was found to be 1.38 μM. This work hereby proposes the ligand 2-((1,6-dimethyl-4-oxo-1,4-dihydropyridin-3-yl)oxy)-N- (1H-indol-4-yl)acetamide as a potential EGFR inhibitor for the treatment of colorectal cancer. The wet lab analysis must be conducted, however, to confirm this hypothesis.

A Detailed Study of Solvent-Ligand Interactions and in Silico Biological Activity Predictions on Hydroxychloroquine

In this study, the effects of solvent environment changes, which are of critical importance in drug production processes, on the geometric structure and physicochemical parameters of the Hydroxychloroquine (HQC) molecule were investigated. For this purpose, optimized molecule structures were obtained using Density Functional Theory in vacuum and solvent environments. Based on the optimized structures, the molecule's thermochemical properties, atomic charges, and chemical reactivity data were calculated in vacuum and solvent environments. Moreover, the molecule's molecular electrostatic potential map and HOMO-LUMO contour maps were drawn. Vibrational frequencies, intensities, and assignments in solvent environments were determined. The characteristics of the hydrogen bonding interactions established between solvent molecules and HQC were determined in detail. ADME, toxicity, and drug-likeness predictions of the molecule were made. The study results showed that while the structural, chemical, and physical properties of the HQC molecule were severely affected when transferred to the solvent environment, they were less affected by the changes between solvent environments. In addition, very strong h-bond interactions are established between the solvent molecules and HQC.

РАЗРАБОТКА С ИСПОЛЬЗОВАНИЕМ МОЛЕКУЛЯРНОЙ ПЕПТИДНОЙ ТРАНСПЛАНТАЦИИ ПОТЕНЦИАЛЬНОГО НЕЙРОПРОТЕКТОРНОГО БИОПЕПТИДА

The present research is focused on design of new nutritional neuroprotective biopeptide, using method of molecular peptide transplantation (MPT) based on analysis of literature sources, databases on biophysical properties and biological activity of peptides. The objects of the research are sequences of amino acid residues of peptides with proven neuroprotective properties. Prediction of biological activity of the developed biopeptide was carried out using the Peptide Ranker database (http://distilldeep.ucd.ie/PeptideRanker). Information on cyclic peptides frameworks that are resistant to proteolysis was obtained in the Cybase database (http://www.cybase.org.au), biopeptide identification was carried out using the EROP-Moscow database (http://erop.inbi.ras.ru/index.html), biophysical properties assessment of the developed biopeptide was carried out using the PepCalc peptide properties calculator (http://pepcalc.com) and APD 3 databases (https://aps.unmc.edu/home). A neuro-protective interfering peptide has been developed by preliminary preparation of a cyclotide-based framework followed by insertion of short HFRWPGP peptide into the framework by the MPT method. The developed peptide has the following sequence of amino acid residues: PCRRRCHFRWPGPCRGRCP, has high biological activity equal to 0.89. The hydrophobicity of the peptide according to Wimley-White in the whole residue is 2.91, therefore, the peptide is well soluble in water. The protein binding potential (Boman index) is high and is 4.3 kcal / mol, indicating its ability to bind to target receptors.

Investigation of the «Structure – analgesic activity» Relationship Using Molecular Docking for Cyclooxygenases 1 and 2 in the Series 5-<i>N</i>-arylaminocarbonyl-6-(get)aryl-4-methyl-1,2,3,6-tetrahydropyrimidine-2-thiones

Introduction. Molecular modeling methods are very popular in the scientific community at the present time. The value of the pharmacological action depends on the affinity of the substance to the biological target. Molecular docking makes it possible to assess the degree of affinity of the studied compound with the active center of the molecular target. The enzyme cyclooxygenase (COX) plays a key role in the cascade of synthesis of proinflammatory cytokines and, as a consequence, in pain.Aim. To identify the dependence "structure – analgesic activity" using the method of molecular docking for cyclooxygenase type 1 and type 2 in the series of 5-N-arylaminocarbonyl-6-(get)aryl-4-methyl-1,2,3,6-tetrahydropyrimidine-2-thions.Materials and methods. 19 compounds 5-N-arylaminocarbonyl-6-(get)aryl-4-methyl-1,2,3,6-tetrahydropyrimidine-2-thions are objects of research The study of the interaction of tetrahydropyridine derivatives with COX 1 and 2 was carried out by the method of molecular docking by the AutoDock 4 program using scoring functions.Results and discussion. Molecular docking of 5-N-arylaminocarbonyl-6-(get)aryl-4-methyl-1,2,3,6-tetrahydropyrimidine-2-thions (I–XIX) with COX 1 and 2 is described. Quantitative studies of the "structure – analgesic activity" in the studied series of compounds of the dependence of experimental values of analgesic activity (ААex.) on scoring functions (BeCOX1, KiCOX1, BeCOX2, KiCOX2) and physico-chemical descriptors (log Ppred., рКаpred., рКvpred.) were performed. By checking on an independent sample of 5 compounds, equation No. 3 (ААpred. 3 = 32,6215 – 4,4894 × Becox1 + 0,0066 × Kicox1 + 3,6032 × log Ppred. (R = 0,854, F = 9,01, S = 7,73, Q2LOO = 0,53) was found, with a high value of the correlation coefficient of the predicted values ААex. with experimental values (Rpred. = 0,878) and a minimum forecast error (Spred. = 6,74).Conclusion. The "structure-activity" models for predicting analgesic activity in a series of 5-N-arylaminocarbonyl-6-(get)aryl-4-methyl-1,2,3,6-tetrahydropyrimidine-2-thions are obtained. The result of the prediction of biological activity is confirmed by the values of the correlation coefficient (R) obtained when testing models on independent samples.

Non-covalent interaction, biological activity prediction, topology and molecular docking studies on adenine derivative

The schiff base 4-(((9H-purin-6-yl)imino)methyl)-2‑methoxy-6-nitrophenol (A5NV) was synthesized and characterized by FTIR, UV, 1H13C–NMR spectral analysis. The experimental FTIR, UV, 1H, 13CNMR spectra were compared with the corresponding predicted by B3LYP/cc-pVDZ calculations. The local energy decomposition analsyis (LED) confirms the A5NV-H2O complex interaction energy, which is -7.17 kcal/mol. The A5NV is a optical material, because it is showed good absorption wavelength, which is 260, 316, and 432 nm. The titled compound is biologically active compound, because calculated HOMO and LUMO energy gap is very low, which is 2.70 eV. In molecular electrostatic potential analsyis confirms the A5NV molecule electrophilic and nucleophilic attacking sites. Inter and intra molecular interadctions and hyper conjucative interactions were calculated by NBO analysis. Condensed fukui functions, local softness, and mulliken charges also calculated. The pharmacological analysis like ADMET properties were calculated. Based on the antimicrobial activity, the synthesized compound showed moderate activity. Using molecular docking study the protein-lignad interactions were investigated.

Toward a comprehensive understanding of alicyclic compounds: Bio-effects perspective and deep learning approach

The escalating use of alicyclic compounds in modern industrial production has led to a rapid increase of these substances in the environment, posing significant health hazards. Addressing this challenge necessitates a comprehensive understanding of these compounds, which can be achieved through the deep learning approach. Graph neural networks (GNN) known for its' extraordinary ability to process graph data with rich relationships, have been employed in various molecular prediction tasks. In this study, alicyclic molecules screened from PCBA, Toxcast and Tox21 are made as general bioactivity and biological targets' activity prediction datasets. GNN-based models are trained on the two datasets, while the Attentive FP and PAGTN achieve best performance individually. In addition, alicyclic carbon atoms make the greatest contribution to biological activity, which indicate that the alicycle structures have significant impact on the carbon atoms' contribution. Moreover, there are terrific number of active molecules in other public datasets, indicates that alicyclic compounds deserve more attention in POPs control. This study uncovered deeper structural-activity relationships within these compounds, offering new perspectives and methodologies for academic research in the field.

EFFECTS OF LIMA BEAN (Phaseolus lunatus) FLOUR ON COGNITIVE FUNCTION AND GROWTH RECOVERY MALNUTRITION RATS

Incorporating lima bean (Phaseolus lunatus) flour can replace and complement the content of proteins, macronutrients, and micronutrients, which may be related to curative effects for malnutrition. Therefore, an in vivo approach was undertaken to demonstrate the effect of lima bean flour on cognitive function and growth recovery in malnourished rats. Four groups of male Wistar rats were included in this study: The control group (N), MAL, MAL+25% LB group, and MAL+50% LB group. Morphometric analysis was used to assess growth recovery. Cognitive tests included Y-Maze, Novel Object Recognition Test (NORT), and Water Maze by Morris (MWM) test, and histopathological examination of the brain sections was conducted on the cerebral cortex and hippocampus by hematoxylin and eosin stains. The biological activity prediction of lima bean active compounds was analyzed by the PASS server using an in-silico study. This study found that lima bean flour increased body weight, length, tail increment, ear length, and leg length in malnourished rats at 50% lima bean concentration, similar to the control group. Lima bean flour showed neuroprotective capabilities by reversing the effects of malnutrition on the cerebral cortex and hippocampus by lowering the percentage of degenerated cells and increasing cognitive and behavioral. Eight bioactive compounds in lima bean extract have antioxidant, anti-inflammatory, and antibacterial properties. Lima bean flour, a source of complex nutrients, protects against malnutrition systemically in various organ systems, including the brain.

In silico analysis of identified molecules using LC–HR/MS of Cupressus sempervirens L. ethyl acetate fraction against three monkeypox virus targets

Monkeypox virus is a viral disease transmitted to humans through contact with infected animals, such as monkeys and rodents, or through direct contact with the bodily fluids or lesions of infected humans. The aim of this study is to evaluate in silico the inhibition effect of eight Cupressus sempervirens L. ethyl acetate fraction identified molecules using LC-MS on three monkeypox targets such as the vaccinia virus thymidylate kinase (VTK), the viral profilin-like protein (VPP), and the viral RNA polymerase (VRP). The study consist of using molecular docking with AutoDock vina based on the lowest energy value in kcal/mol, pharmacokinetics prediction with pre-ADMET v2.0 server, and prediction of biological activity with the PASS server tool. The best complexes were subjected to molecular dynamics simulation (MD) study to confirm their stability using Desmond software. The used molecules were vitamin C, vanillic acid (Pol), Flav1 (Catechin), Flav2 (Epicatechin), Flav3 (Hyperoside), Flav4 (Luteolin), Flav5 (Taxifolin), and Flav6 (Quercetin). The results show that flavonoids are potent to VTK, VPP and effectively block the VRP channel with energy values ranging from −7.0 to −9.3 kcal/mol. Further, MD simulation supports Flav1 and, Flav2 for notable stability in the VTK binding pocket through hydrogen and hydrophobic interactions. PASS results predicted various biological activities with promising VTK and VRP inhibition activities. The studied molecules could constitute a safer alternative to current drugs, which often cause adverse side effects. 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.

Application of bioinformatics methods in pesticide research and development

The use of pesticides is accompanied by a number of environmental and agrotechnological problems. Many pesticides do not degrade and stay in the soil for years and have low selectivity. Massive application of pesticides with non-selective nature of action caused a gradual increase of resistance in pests due to persistent inherited changes in their DNA. This affects the efficiency of growing agricultural plants and pollution of the environment and food. Computational biology methods, which are actively developing all over the world, can help to solve this problem. Despite the fact that in Russia bioinformatics methods are used to study plant genes of animals, metagenomes of microorganisms, there are no own databases and specialized computer applications for such research and pesticide modernization. Development of domestic similar bioinformatics tools is also an urgent task. The article highlights the problem of creating new effective and environmentally friendly pesticides. The methods of bioinformatics that can be used in the research and development of pesticides are given. The stages of creating new pesticides by bioinformatics methods (review of databases, modeling of molecules, modeling of the interaction of a pesticide with a target, prediction of biological activity) are considered. A description is given of the methods for optimizing the molecular framework of pesticides, which is a change in the carbon skeleton in order to search for new active compounds and screen out many similar compounds in the chemical space. Foreign web resources used to assess the presence of pesticidal properties in substances, such as toxicity, metabolism and physico-chemical properties, and their subsequent registration as pesticides are given.

Artificial intelligence for prediction of biological activities and generation of molecular hits using stereochemical information

In this work, we develop a method for generating targeted hit compounds by applying deep reinforcement learning and attention mechanisms to predict binding affinity against a biological target while considering stereochemical information. The novelty of this work is a deep model Predictor that can establish the relationship between chemical structures and their corresponding pIC_{50} values. We thoroughly study the effect of different molecular descriptors such as ECFP4, ECFP6, SMILES and RDKFingerprint. Also, we demonstrated the importance of attention mechanisms to capture long-range dependencies in molecular sequences. Due to the importance of stereochemical information for the binding mechanism, this information was employed both in the prediction and generation processes. To identify the most promising hits, we apply the self-adaptive multi-objective optimization strategy. Moreover, to ensure the existence of stereochemical information, we consider all the possible enumerated stereoisomers to provide the most appropriate 3D structures. We evaluated this approach against the Ubiquitin-Specific Protease 7 (USP7) by generating putative inhibitors for this target. The predictor with SMILES notations as descriptor plus bidirectional recurrent neural network using attention mechanism has the best performance. Additionally, our methodology identify the regions of the generated molecules that are important for the interaction with the receptor’s active site. Also, the obtained results demonstrate that it is possible to discover synthesizable molecules with high biological affinity for the target, containing the indication of their optimal stereochemical conformation.

Generation of Chemical Space of Compounds for Prostate Cancer Treatment: Biological Activity Prediction, Clustering, and Visualization of Chemical Space.

Designing molecules for pharmaceutical purposes has been a significant focus for several decades. The pursuit of novel drugs is an arduous and financially demanding undertaking. Nevertheless, the integration of computer-assisted frameworks presents a swift avenue for designing and screening drug-like compounds. Within the context of this research, we introduce a comprehensive approach for the design and screening of compounds tailored to the treatment of prostate cancer. To forecast the biological activity of these compounds, we employed machine learning (ML) models. Additionally, an automated process involving the deconstruction and reconstruction of molecular building blocks leads to the generation of novel compounds. Subsequently, the ML models were utilized to predict the biological activity of the designed compounds, and the t-SNE method was employed to visualize the chemical space covered by the novel compounds. A meticulous selection process identified the most promising compounds, and their potential for synthesis was assessed, offering valuable guidance to experimental chemists in their investigative endeavors. Furthermore, fingerprint and heatmap analysis were conducted to evaluate the chemical similarity among the selected compounds. This multifaceted approach, encompassing predictive modeling, compound generation, visualization, and similarity assessment, underscores our commitment to refining the process of identifying potential candidates for further exploration in prostate cancer treatment.

Characterization of Degradation Products of Bisoprolol by LC-MS and In-silico Determination of Toxicity and Biological Activity Prediction of its Degradation Product: A Comprehensive Approach during Drug Development

The stability of bisoprolol in tablet and bulk form was developed and validated using chromatographic methods. The bisoprolol was exposed to oxidizing agents like hydrogen peroxide, heat (dry and wet) and photolytic conditions, acid, alkali, and water hydrolysis. However, considerable degradation was observed in acid, alkali, oxidative, and wet heat thermal conditions. The drug’s stability under photolytic and dry heat conditions was established. The drug and its degradation products were separated on ODS C-18 with the specification of (250 mm × 4.6 mm, 5 μm) and the mobile phase containing acetonitrile and phosphate buffer (20 mM, pH 8) (60:40, v/v). According to ICH Q2 (R1) all method validation parameters are verified. Determination of the active pharmaceutical ingredient was not interfered by excipients or degradation products. In the 6–14 μg/mL range, the response was shown to be linear. One degradation product’s LC-MS m/z values and fragmentation patterns matched an impurity mentioned in the drug’s European Pharmacopoeia monograph. The remaining three were unidentified degradation products. LC-MS fragmentation experiments were used to characterize the products. The findings may lead to the suggestion of a more thorough drug degradation mechanism. The Toxtree software (Version 3.1.1) was then used to forecast the toxicity of the degradation products. Additional toxicity determination was carried out by using in-silico method.