Drug Design Techniques Research Articles

Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression

Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Furthermore, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis, we identified two potent drugs (Tadalafil and Finasteride) that can efficiently inhibit AKT1 and MDM2 proteins. We propose that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma.Graphical abstract

Unraveling the mechanistic details of rho-associated coiled-coil kinase1 (ROCK1) inhibition: Implications from pharmacophore mapping and free energy analysis

Rho-associated coiled-coil kinases (ROCKs) are essential for various cellular processes and play a crucial role in neurological and inflammatory disorders. The two ROCK isoforms (ROCK1 and ROCK2) are involved in the pathogenesis of multiple diseases. Increased levels of ROCK1 are observed in patients with Alzheimer's disease (AD), making it a promising drug target. This work presents the mechanism of inhibitor binding at the ROCK1 active site using a combination of ligand and structure-based drug design techniques. Ligand-based pharmacophore studies were performed on previously reported ROCK1 inhibitors to unravel the 3D steric and electronic features essential for ROCK1 inhibition. Molecular docking and molecular dynamics-based free energy analysis studies were performed to recognize the amino acids assisting in inhibitor binding by stabilizing the identified pharmacophore features. The crucial 3D pharmacophore features of ROCK1 inhibitors included hydrophobic (HYD), hydrogen-bond acceptor (HBA), and hydrogen-bond donor (HBD) characteristics. These features were oriented next to the complementary amino acids in the ROCK1 active site, as evident from the detailed interaction analysis. The free energy calculations provided a quantifiable estimate of the favorable energetic contribution of Gly83, Val90, Lys105, Asp160, Leu205, and Asp216 to the total binding energy. These amino acids significantly contributed to the ROCK1-inhibitor complexes' overall binding free energy, indicating their importance in inhibitor binding.

Integrated fragment-based drug design and virtual screening techniques for exploring the antidiabetic potential of thiazolidine-2,4-diones: Design, synthesis and in vivo studies

Diabetes mellitus is a metabolic disorder characterized by elevated blood sugar levels and related complications. This study focuses on harnessing and integrating fragment-based drug design and virtual screening techniques to explore the antidiabetic potential of newly synthesized thiazolidine-2,4-dione derivatives. The research involves the design of novel variations of thiazolidine-2,4-dione compounds by Fragment-Based Drug Design. The screening process involves pharmacophore based virtual screening through docking algorithms, and the identification of newly twelve top-scoring compounds. The molecular docking analysis revealed that compounds SP4e, SP4f showed highest docking scores of −9.082 and −10.345. The binding free energies of the compounds SP4e, SP4f and pioglitazone was found to be −19.9, −16.1 and −13 respectively, calculated using the Prime MM/GBSA approach. The molecular dynamic study validates the docking results. Furthermore, In the Swiss albino mice model, both SP4e and SP4f exhibited significant hypoglycaemic effects, comparable to the reference drug pioglitazone. Furthermore, these compounds demonstrated favorable effects on the lipid profile, reducing total cholesterol, triglycerides, and LDL levels while increasing HDL levels. In mice tissue, the disease control group showed PPAR-γ expression of 4.200 ± 0.24, while compound SP4f displayed higher activation at 7.84 ± 0.431 compared to compound SP4e with an activation of 7.68 ± 0.65. In zebrafish model, SP4e and SP4f showed significant reductions in blood glucose levels and lipid peroxidation, along with increased glutathione levels and catalase activity. These findings highlighted the potential of SP4e and SP4f as antidiabetic agents, warranting further exploration for therapeutic applications. The in vitro study was performed in HEK-2 cell line, the pioglitazone group demonstrated PPAR-γ expression of EC50 = 575.2, while compound SP4f exhibited enhanced activation at EC50 = 739.0 in contrast to compound SP4e activation of EC50 = 826.7.

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery.

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

Design of Novel Imidazopyrazine Derivative against Breast Cancer via Targeted NPY1R Antagonist.

Breast cancer is the most frequent malignancy in women with more than one in ten new cancer diagnoses each year. Synthetic products are a key source for the identification of new anticancer medicines and drug leads. Imidazopyrazine is a highly favored skeleton for the design of new anticancer drugs. In silico designed derivatives were screened using computer aided drug design techniques and validated using MTT assay. A template-based methodology was used in the current work to create novel Imidazopyrazine derivatives, targeting the NPY1R protein. Molecular docking, Diffusion docking, MD simulation, MM-GBSA and meta-dynamics techniques were followed. MTT assay was performed to validate the activity of principal compound. A docking score of -6.660 and MMGBSA value of -108.008 (+/-) 9.14 kcal/mol was obtained from the investigations conducted. In addition, molecular dynamics simulation was carried out for 500 ns, yielding a stable RMSD and value of 5.6 Å, thus providing insights on the stability of the protein conformation on interaction with the principal compound. Furthermore, the in vivo validation studies conducted via MTT assay showed an IC50 value of 73.45 (+/-) 0.45 μg /mL. The research has produced encouraging findings and can be applied as a model for precise enumerations in the future. It also encourages the study of novel synthetic compounds with potential anti-cancer properties.

Application of Ligand- and Structure-Based Prediction Models for the Design of Alkylhydrazide-Based HDAC3 Inhibitors as Novel Anti-Cancer Compounds.

Histone deacetylases (HDAC) represent promising epigenetic targets for several diseases including different cancer types. The HDAC inhibitors approved to date are pan-HDAC inhibitors and most show a poor selectivity profile, side effects, and in particular hydroxamic-acid-based inhibitors lack good pharmacokinetic profiles. Therefore, the development of isoform-selective non-hydroxamic acid HDAC inhibitors is a highly regarded field in medicinal chemistry. In this study, we analyzed different ligand-based and structure-based drug design techniques to predict the binding mode and inhibitory activity of recently developed alkylhydrazide HDAC inhibitors. Alkylhydrazides have recently attracted more attention as they have shown promising effects in various cancer cell lines. In this work, pharmacophore models and atom-based quantitative structure-activity relationship (QSAR) models were generated and evaluated. The binding mode of the studied compounds was determined using molecular docking as well as molecular dynamics simulations and compared with known crystal structures. Calculated free energies of binding were also considered to generate QSAR models. The created models show a good explanation of in vitro data and were used to develop novel HDAC3 inhibitors.

Identification of Potent hDHODH Inhibitors for Lung Cancer via Virtual Screening of a Rationally Designed Small Combinatorial Library.

Cancer is characterized by altered cellular metabolism, and metabolic enzymes are considered as a promising target for anticancer therapy. Pyrimidine metabolism dysregulation is associated with various types of cancer, particularly lung cancer, which is one of the leading causes of cancer-related mortality worldwide. Recent studies have shown that small-cell lung cancer cells are particularly reliant on the pyrimidine biosynthesis pathway and are sensitive to its disruption. DHODH, the rate-limiting enzyme of the de novo pyrimidine production pathway, is essential in the production of RNA and DNA and is overexpressed in malignancies such as AML, skin cancer, breast cancer, and lung cancer, thereby highlighting DHODH as a viable target for developing drugs to combat lung cancer. Herein, rational drug design and computational techniques were used to discover novel DHODH inhibitors. A small combinatorial library was generated, and the top hits were synthesized and tested for anticancer activity against three lung cancer cell lines. Among the tested compounds, compound 5c possessed a stronger cytotoxicity (TC50 of 11 μM) compared to the standard FDA-approved drug (Regorafenib, TC50 of 13 μM) on the A549 cell line. Furthermore, compound 5c demonstrated potent inhibitory activity against hDHODH at a nanomolar level of 421 nM. DFT, molecular docking, molecular dynamic simulations, and free energy calculations were also carried out to understand the inhibitory mechanisms of the synthesized scaffolds. These in silico studies identified key mechanisms and structural features that will be crucial for future studies.

Potential anti-obesity/inflammatory flavonoid-derived biomolecules against Obesity to prevent WAT differentiation by targeting a DNA-binding protein inhibitor, ID-1.

A concoction of unhealthy eating, inactivity, and the adverse effects of specific drugs brings on obesity. The primary cause of Obesity is the storage of too much energy and triglycerides in adipocytes, particularly white adipose tissue (WAT). In addition to modifying one's lifestyle, anti-obesity medicines are increasingly used as adjuvant therapy. Flavonoids are the major class of compounds having significant biological impacts and health-improving properties. To find novel flavonoid compounds that fight obesity using computational drug design techniques. This work targets 1DI protein to predict new flavonoid compounds that fight obesity. The study uses computational approaches to anticipate potential anti-obesity/inflammatory flavonoid compounds against obesity to prevent WAT differentiation by targeting ID-1, a DNA-binding protein inhibitor. Our study led to the identification of the protein target inhibitor lead CID: 5280443, which was found to be a potent inhibitor of the receptor. According to the findings of this study, this bio-active molecule may be used as a lead for the development of drugs that preferentially fight obesity without interfering with the functions of the human proteasome. The scientific community will benefit from these discoveries, which could aid in the creation of new medications that treat obesity more successfully.

Design, synthesis, molecular docking and biological evaluation of novel pyrazole derivatives bearing quinoxalinone moiety as multi-targeted anticancer agents

Based on a multitarget-directed drug design technique, a series of new quinoxalinone-based pyrazole derivatives (4a-h) were designed and synthesized. The potency of newly synthesized molecules to inhibit the antiproliferation of the human cancer cell lines MCF-7 (breast), HCT-116 (colon), and A549 (lung) was examined. The most effective compounds against the examined cancer cell lines were 4e, 4f, 4 g, and 4 h. Among these, compounds 4e and 4 h had a strong anticancer activity that was equivalent to sorafenib. The capacity of the potent compounds (4e, 4f, 4 g, and 4 h) to inhibit the in vitro activity of the thymidylate synthase (TS) enzyme, BRaf, and EGFR kinases was also tested. With IC50 values for the TS enzyme, BRaf kinase, and EGFR kinase ranging from 1.16 to 2.97 μM, 1.28 to 3.69 μM, and 1.93 to 4.28 μM, respectively, all the investigated compounds showed a noticeable inhibitory action. Among the synthesized hybrids, compound 4 h showed IC50 value of 2.04, 2.69 and 1.93 μM against MCF-7, HCT-116, and A549 cell line, respectively, and 1.16, 1.28 and 1.93 nM against TS, BRaf and EGFR kinase enzyme, respectively. All of the synthesized hybrids adhered to Lipinski's guidelines, which suggested that they would have favorable oral drug-like qualities. To determine the probable interaction between the potent compounds and the TS active site, molecular docking study was conducted.

The Structural Combination of SIL and MODAG Scaffolds Fails to Enhance Binding to α-Synuclein but Reveals Promising Affinity to Amyloid β.

A technique to image α-synuclein (αSYN) fibrils in vivo is an unmet scientific and clinical need that would represent a transformative tool in the understanding, diagnosis, and treatment of various neurodegenerative diseases. Several classes of compounds have shown promising results as potential PET tracers, but no candidate has yet exhibited the affinity and selectivity required to reach clinical application. We hypothesized that the application of the rational drug design technique of molecular hybridization to two promising lead scaffolds could enhance the binding to αSYN up to the fulfillment of those requirements. By combining the structures of SIL and MODAG tracers, we developed a library of diarylpyrazoles (DAPs). In vitro evaluation through competition assays against [3H]SIL26 and [3H]MODAG-001 showed the novel hybrid scaffold to have preferential binding affinity for amyloid β (Aβ) over αSYN fibrils. A ring-opening modification on the phenothiazine building block to produce analogs with increased three-dimensional flexibility did not result in an improved αSYN binding but a complete loss of competition, as well as a significant reduction in Aβ affinity. The combination of the phenothiazine and the 3,5-diphenylpyrazole scaffolds into DAP hybrids did not generate an enhanced αSYN PET tracer lead compound. Instead, these efforts identified a scaffold for promising Aβ ligands that may be relevant to the treatment and monitoring of Alzheimer's disease (AD).

Editorial: Hot trends in computer-aided drug design techniques

Editorial: Hot trends in computer-aided drug design techniques

HIV-1/HBV Coinfection Accurate Multitarget Prediction Using a Graph Neural Network-Based Ensemble Predicting Model.

HIV and HBV infection are both serious public health challenges. There are more than approximately 4 million patients coinfected with HIV and HBV worldwide, and approximately 5% to 15% of those infected with HIV are coinfected with HBV. Disease progression is more rapid in patients with coinfection, which significantly increases the likelihood of patients progressing from chronic hepatitis to cirrhosis, end-stage liver disease, and hepatocellular carcinoma. HIV treatment is complicated by drug interactions, antiretroviral (ARV) hepatotoxicity, and HBV-related immune reconditioning and inflammatory syndromes. Drug development is a highly costly and time-consuming procedure with traditional experimental methods. With the development of computer-aided drug design techniques, both machine learning and deep learning have been successfully used to facilitate rapid innovations in the virtual screening of candidate drugs. In this study, we proposed a graph neural network-based molecular feature extraction model by integrating one optimal supervised learner to replace the output layer of the GNN to accurately predict the potential multitargets of HIV-1/HBV coinfections. The experimental results strongly suggested that DMPNN + GBDT may greatly improve the accuracy of binary-target predictions and efficiently identify the potential multiple targets of HIV-1 and HBV simultaneously.

Identification and Introduction of Possible Inhibitors of Plasmodium Falciparum Lactate Dehydrogenase Enzyme using Computational Techniques of Drug Design and Virtual Screening based on Macromolecules

Introduction: Human malaria is an infectious-blood disease that is caused by the Plasmodium genus. Anopheles mosquitoes transmit malaria by biting and are well-known as the only biological carriers of this disease. The aim of this research was the identification and introduction of possible inhibitors of Plasmodium falciparum lactate dehydrogenase enzyme using computational techniques of drug design and virtual screening based on macromolecule.
 Methods: In this analytical-descriptive study, 8733 compounds were initially collected from the PubChem database. In the second step, different filtrations were performed on the library compounds. The selected compounds showed good drug-like properties and pharmacokinetics. Finally, molecular docking simulations were carried out to investigate their binding mode and interactions in the enzyme's active site.
 Results: The results of the present study showed that the bonds involved in the binding of the compounds with the enzyme were hydrophobic interactions and hydrogen bonds, and the π-π interaction was in the lower priority. Among the studied compounds, the best docking results were related to the compounds with identification codes CID_23603310, CID_23603337, CID_11912187 and CID_11912184 and free binding energy of -29.10, -9.06, -9.04 and -9.00 kcal/mol, respectively. In general, lipophilic parts and hydrogen bonds increased the affinity and inhibited the enzyme.
 Conclusion: Based on the results, all the compounds showed suitable connections in the active site of the enzyme and can be proposed as potential effective inhibitors of Plasmodium falciparum lactate dehydrogenase enzyme.

In-silico Validation of Pyrazolone Derivatives as the Potent Scaff old for Modulating Protein Abnormalities Associated with Parkinson’s Disease

Aim: We explored pyrazolone derivatives for anti-Parkinson’s activity using in-silico tools to identify novel lead hits against Parkinson’s disease. Background: Parkinson’s disease is the most predominant neuronal degenerative disorder, caused by protein aggregation and dopamine imbalance. The available therapeutic agents on prolonged exposure lead to severe adverse eff ects and provide only symptomatic relief. Therefore, it is a need for novel drug molecules that would modulate the disease condition. Objectives: To identify novel hit molecule for a sequence of molecules designed using pyrazolone as a parent moiety by computer-aided drug design techniques. Materials and Methods: Derivatives are generated by various substituted functional groups and further, pharmacokinetic profi le, the biological spectrum, adverse drug eff ect, molecular docking, molecular dynamic, and MMPBSA evaluation was performed. Results: Thirty-four compounds follow a pharmacokinetic profi le. 15 compounds were predicted to possess a positive central nervous system activity score. The compounds C13, C12, C14, A1, C9, and C7 possessed the highest binding affi nity of -7.81, -3.15, -8.49, -3.43, -6.09, and -2.77 kcal/mol with various targets involved in Parkinson’s disease. Compound C13 exhibit ed highest binding score of -9.78 kcal/mol formono amino oxidase-B. Conclusion: From our investigations, we hope that novel substituted pyrazolone derivatives can act as an assuring virtual hit molecule for developing anti-Parkinson’s agents. These predictions obtained via in-silico techniques could aid the development of pharmacological inhibitors for Parkinson’s disease.

Molecular Dynamics Simulation and Pharmacoinformatic Integrated Analysis of Bioactive Phytochemicals from Azadirachta indica (Neem) to Treat Diabetes Mellitus

Diabetes mellitus is a chronic hormonal and metabolic disorder in which our body cannot generate necessary insulin or does not act in response to it, accordingly, ensuing in discordantly high blood sugar (glucose) levels. Diabetes mellitus can lead to systemic dysfunction in the multiorgan system, including cardiac dysfunction, severe kidney disease, lowered quality of life, and increased mortality risk from diabetic complications. To uncover possible therapeutic targets to treat diabetes mellitus, the in silico drug design technique is widely used, which connects the ligand molecules with target proteins to construct a protein-ligand network. To identify new therapeutic targets for type 2 diabetes mellitus, Azadirachta indica is subjected to phytochemical screening using in silico molecular docking, pharmacokinetic behavior analysis, and simulation-based molecular dynamic analysis. This study has analyzed around 63 phytochemical compounds, and the initial selection of the compounds was made by analyzing their pharmacokinetic properties by comparing them with Lipinski’s rule of 5. The selected compounds were subjected to molecular docking. The top four ligand compounds were reported along with the control drug nateglinide based on their highest negative molecular binding affinity. The protein-ligand interaction of selected compounds has been analyzed to understand better how compounds interact with the targeted protein structure. The results of the in silico analysis revealed that 7-Deacetyl-7-oxogedunin had the highest negative docking score of −8.9 Kcal/mol and also demonstrated standard stability in a 100 ns molecular dynamic simulation performed with insulin receptor ectodomain. It has been found that these substances may rank among the essential supplementary antidiabetic drugs for treating type 2 diabetes mellitus. It is suggested that more in vivo and in vitro research studies be carried out to support the conclusions drawn from this in silico research strategy.

Designing Bcl-2 protein binding aptamers using screened Coulomb interaction method & regulate apoptosis.

Cancer cells develop different physiological abnormalities. One of them is overexpression of Bcl-2 protein, an anti-apoptotic biomolecule helping cancer cells to sustain. We planned to regulate the apoptosis by inhibiting the Bcl-2 protein's functions in cancer cells. We used computer aided drug design techniques to make a nucleic acid-based aptamer drug that targets Bcl-2 proteins. We applied a theoretical biophysics technique ‘screened Coulomb interactions approach (SCIA)’ for designing aptamers. This technique was patented (US10916330B1) and found suitable for designing novel aptamers to target biomoleculesin physiological conditions. Physicochemical analysis of Bcl-2 protein helps detect drug binding site(s) and charge distribution inamino acid structures. We made a judicious choice to select amino acid arginine as our target molecule in Bcl-2 structure. We constructed a universal drug design platform by applying SCIA, developed our algorithms and applied in Mathematica 13 to perform numerical computations (NCs) addressing the drug-target binding energetics. The NC results show that nucleotide guanine has the lowest binding energy over other nucleotides while interacting with arginine. Selection of other subsequent nucleotides as aptamer building blocks at various screening order is being made using their preferred SCI binding energetics with arginine. Thus, the discovery of a few aptamers to target arginine (specifically) and Bcl-2 protein (in general) has been made. Based on target binding energetic preferences we rank the aptamers. We are now working on the following two things: (i) perform 100 nanosecond molecular dynamics (MD) simulations to address drug-target binding kinetics and (ii) perform in vitro protein binding assays using top selected aptamers. Thus, we shall be able to finalize a few candidate drugs as optimal therapeutic agents capable of binding with Bcl-2 proteins and thus inhibit the protein functions. This research will lead us to discovering a few aptamer candidates for future chemotherapy applications.

Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs.

Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.

Past, present and future of xanthine oxidase inhibitors: design strategies, structural and pharmacological insights, patents and clinical trials.

Xanthine oxidase, a molybdo-flavoenzyme, and an isoform of xanthine dehydrogenase both exist as xanthine oxidoreductase and are responsible for purine catabolism. Xanthine oxidase is more involved in pathological conditions when extensively modulated. Elevation of xanthine oxidase is not only the prime cause of gout but is also responsible for various hyperuricemia associated pathological conditions like diabetes, chronic wounds, cardiovascular disorders, Alzheimer's disease, etc. Currently available xanthine oxidase inhibitors in clinical practice (allopurinol, febuxostat and topiroxostat) suffer from fatal side effects that pose a serious problem to the healthcare system, raising global emergency to develop novel, potent and safer xanthine oxidase inhibitors. This review will provide key and systematic information about: a. design strategies (inspired from both marketed drugs in clinical practice and natural products), structural insights and pharmacological output (xanthine oxidase inhibition and associated activities) of various pre-clinical candidates reported by various research groups across the globe in the past two decades; b. patented xanthine oxidase inhibitors published in the last three decades and c. clinical trials and their outcomes on approved drug candidates. Information generated in this review has suggested fragment-based drug design (FBDD) and molecular hybridization techniques to be most suitable for development of desired xanthine oxidase inhibitors as one provides high selectivity toward the enzyme and the other imparts multifunctional properties to the structure and both may possess capabilities to surpass the limitations of currently available clinical drugs. All in combination will exclusively update researchers working on xanthine oxidase inhibitors and allied areas and potentially help in designing rational, novel, potent and safer xanthine oxidase inhibitors that can effectively tackle xanthine oxidase related disease conditions and disorders.

Obstructing Salmonella typhi's virulence in eukaryotic cells through design of its SipB protein antagonists

ObjectiveTyphoid fever, a disease caused by Salmonella typhi, is a leading cause of morbidity and mortality, particularly in developing nations. The evolution of resistance mechanisms to existing antibiotics has necessitated a search for newer drug candidates. This study used computer aided drug design techniques to design novel antibiotics that function by antagonizing SipB, an effector protein of the bacterium that is responsible for its pathogenicity and virulence in eukaryotic host cells. MethodsA data set of 32 bioactive molecules with established antibacterial activity against S. typhi was retrieved from the PubChem data base; optimized through a DFT approach in Spartan 14 software; and further subjected to QSAR modeling in BIOVIA-Accelrys Material Studio. The validated model (R2 = 0.80, R2Adj = 0.78, Q2LOO = 0.74, R2pred = 0.54, lack of fit = 0.07) revealed the dominant influence of MATS6c and E3p descriptors on the observed antibacterial activity of the compounds. Information from the model was used to optimize the structures of selected lead compounds in the data set, thus leading to the design of a highly potent set of novel analogues denoted D-1, D-2 and D-3. ResultsThe predicted MIC values of D-1, D-2 and D-3 were 1.03, 0.73 and 0.30 μg/mL, respectively. Furthermore, molecular docking studies on these novel ligands against the active sites of SipB revealed binding energy values of −8.0, −7.7 and −7.7 kcal/mol for D-1, D-2 and D-3, respectively. These values were better than the −7.0 kcal/mol recorded for ciprofloxacin, the reference antibiotic used herein for quality assurance. In addition, drug-likeness and ADMET evaluation of the designed compounds revealed that they are orally bioavailable and exhibit excellent pharmacokinetic and toxicological profiles. ConclusionThe current findings may provide a roadmap for the discovery of more potent antibiotics against S. typhi.

Artificial Intelligence: The Milestone in Modern Biomedical Research

In recent years, the advent of new experimental methodologies for studying the high complexity of the human genome and proteome has led to the generation of an increasing amount of digital information, hence bioinformatics, which harnesses computer science, biology, and chemistry, playing a mandatory role for the analysis of the produced datasets. The emerging technology of Artificial Intelligence (AI), including Machine Learning (ML) and Artificial Neural Networks (ANNs), is nowadays at the core of biomedical research and has already paved the way for significant breakthroughs in both biological and medical sciences. AI and computer science have transformed traditional medicine into modern biomedicine, thus promising a new era in systems biology that will enhance drug discovery strategies and facilitate clinical practice. The current review defines the main categories of AI and thoroughly describes the fundamental principles of the widely used ML, ANNs and DL approaches. Furthermore, we aim to underline the determinant role of AI-based methods in various biological research fields, such as proteomics and drug design techniques, and finally, investigate the implication of AI in everyday clinical practice and healthcare systems. Finally, this review also highlights the challenges and future directions of AI in Modern Biomedical study.