Rational drug design

Druggable upregulated proteins in EWS-FLI-driven Ewing sarcoma as emerging new therapeutic targets

Ewing sarcoma (ES) is a highly aggressive soft tissue tumor that primarily affects the long bones of children and young adults. It is distinguished by a characteristic chromosomal translocation between the Ewing sarcoma breakpoint region 1 (EWS) gene and the erythroblast transformation-specific (ETS) family of genes, most commonly resulting in the EWS-friend leukemia integration 1 (EWS-FLI1) fusion gene. This translocation is observed in approximately 80%-85% of ES cases. This fusion gene encodes a non-physiological chimeric fusion protein that plays a central role in tumorigenesis by interacting with numerous partner proteins. Several studies have demonstrated the tumorigenic potential of the EWS-FLI1 protein when transfected into non-cancer cell lines. However, targeting EWS-FLI1 directly remains a significant challenge, as no drug to date has been reported to bind to and inhibit its activity effectively. An alternative therapeutic strategy involves targeting key overexpressed protein complexes implicated in ES tumorigenesis, many of which may be downstream interacting partners of EWS-FLI1. This review explores emerging protein targets as potential therapeutic avenues in ES treatment.

Molecular and structure-based drug design: From theory to practice

Structure-based drug design (SBDD) and molecular docking have revolutionized drug discovery by providing effective strategies for identifying and optimizing therapeutic agents. This review highlights the principles and methodologies of SBDD, which uses high-resolution structural data of biological targets to design drugs with enhanced selectivity and efficacy. Techniques like nuclear magnetic resonance (NMR) spectroscopy, cryo-electron microscopy (cryo-EM), and X-ray crystallography are key in providing the structural information necessary for SBDD. Molecular docking, a crucial component of modern drug discovery, simulates interactions between drug candidates and biological targets. By predicting how a ligand fits into a receptor's binding site, researchers can assess the strength and nature of these interactions, guiding compound selection. Advances in molecular docking have incorporated machine learning to improve scoring functions and prediction accuracy. Docking studies include search algorithms, scoring functions, and binding site identification to predict the optimal orientation of a ligand when bound to a protein. Despite its widespread use, molecular docking has limitations, such as challenges in achieving high prediction accuracy, modeling protein flexibility, and accounting for solvation effects. Improvements in computational power and the integration of machine learning techniques are addressing these issues. This review emphasizes the importance of ongoing innovation and interdisciplinary collaboration in enhancing molecular docking and its role in discovering novel therapies.

Structure-activity relationship of pharmacophores and toxicophores: the need for clinical strategy

OBJECTIVES

Sometimes clinical efficacy and potential risk of therapeutic and toxic agents are difficult to predict over a long period of time. Hence there is need for literature search with a view to assessing cause of toxicity and less efficacy of drugs used in clinical practice.

METHOD

Hence literatures were searched for physicochemical properties, chemical formulas, molecular masses, pH values, ionization, receptor type, agonist and antagonist, therapeutic, toxic and structure-activity relationship of chemical compounds with pharmacophore and toxicophore, with a view to identifying high efficacious and relative low toxic agents. Inclusion criteria were manuscripts published on PubMed, Scopus, Web of Science, PubMed Central, Google Scholar among others, between 1960 and 2023. Keywords such as pharmacophore, toxicophore, structure-activity-relationship and disease where also searched. The exclusion criteria were the chemicals that lack pharmacophore, toxicophore and manuscripts published before 1960.

RESULTS

Findings have shown that pharmacophore and toxicophore functional groups determine clinical efficacy and safety of therapeutics, but if they overlap therapeutic and toxicity effects go concurrently. Hence the functional groups, dose, co-administration and concentration of drugs at receptor, drug-receptor binding and duration of receptor binding are the determining factors of pharmacophore and toxicophore activity. Molecular mass, chemical configuration, pH value, receptor affinity and binding capacity, multiple pharmacophores, hydrophilic/lipophilic nature of the chemical contribute greatly to functionality of pharmacophore and toxicophore.

CONCLUSION

Daily single therapy, avoidance of reversible pharmacology, drugs with covalent adduct, maintenance of therapeutic dose, and the use of multiple pharmacophores for terminal diseases will minimize toxicity and improve efficacy.

Analysis of Haemonchus embryos at single cell resolution identifies two eukaryotic elongation factors as intervention target candidates

Advances in single cell technologies are allowing investigations of a wide range of biological processes and pathways in animals, such as the multicellular model organism Caenorhabditis elegans - a free-living nematode. However, there has been limited application of such technology to related parasitic nematodes which cause major diseases of humans and animals worldwide. With no vaccines against the vast majority of parasitic nematodes and treatment failures due to drug resistance or inefficacy, new intervention targets are urgently needed, preferably informed by a deep understanding of these nematodes' cellular and molecular biology - which is presently lacking for most worms. Here, we created the first single cell atlas for an early developmental stage of Haemonchus contortus - a highly pathogenic, C. elegans-related parasitic nematode. We obtained and curated RNA sequence (snRNA-seq) data from single nuclei from embryonating eggs of H. contortus (150,000 droplets), and selected high-quality transcriptomic data for > 14,000 single nuclei for analysis, and identified 19 distinct clusters of cells. Guided by comparative analyses with C. elegans, we were able to reproducibly assign seven cell clusters to body wall muscle, hypodermis, neuronal, intestinal or seam cells, and identified eight genes that were transcribed in all cell clusters/types, three of which were inferred to be essential in H. contortus. Two of these genes (i.e. Hc-eef-1A and Hc-eef1G), coding for eukaryotic elongation factors (called Hc-eEF1A and Hc-eEF1G), were also demonstrated to be transcribed and expressed in all key developmental stages of H. contortus. Together with these findings, sequence- and structure-based comparative analyses indicated the potential of Hc-eEF1A and/or Hc-eEF1G as intervention targets within the protein biosynthesis machinery of H. contortus. Future work will focus on single cell studies of all key developmental stages and tissues of H. contortus, and on evaluating the suitability of the two elongation factor proteins as drug targets in H. contortus and related nematodes, with a view to finding new nematocidal drug candidates.

Text-guided small molecule generation via diffusion model

The de novo generation of molecules with targeted properties is crucial in biology, chemistry, and drug discovery. Current generative models are limited to using single property values as conditions, struggling with complex customizations described in detailed human language. To address this, we propose the text guidance instead, and introduce TextSMOG, a new text-guided small molecule generation approach via diffusion model, which integrates language and diffusion models for text-guided small molecule generation. This method uses textual conditions to guide molecule generation, enhancing both stability and diversity. Experimental results show TextSMOG's proficiency in capturing and utilizing information from textual descriptions, making it a powerful tool for generating 3D molecular structures in response to complex textual customizations.

The In vitro evaluation of in silico-designed synthetic peptides AKVUAM-1 and AKVUAM-2 on human lung fibroblast cell line MRC5 and Mycobacterium tuberculosis isolates

Tuberculosis is a major global health problem caused by Mycobacterium tuberculosis and the increase in drug resistance is driving the need for new treatments. Today, various approaches are being applied in the development of drugs for the treatment of tuberculosis. Computer-aided drug design (CADD) enables the prediction of pharmacological efficacy for potential drug molecules during the design process. Thus, new therapeutic compounds can be developed that are more potent, less toxic and have fewer side effects than existing drugs. In this study, we investigated the in vitro activities of AKVUAM-1 and AKVUAM-2 synthetic peptides designed in silico by computer-aided drug design method to inhibit the interaction between M. tuberculosis outer membrane protein Cpn T and macrophage surface receptor CR-1 and Surfactant D protein. Notably, these synthetic peptides do not show cytotoxic effect on normal lung tissue and do not kill M. tuberculosis directly. The MIC values for AKVUAM-1 were higher than 512 μg/ml for all bacterial strains except IST-16 strain (128 μg/ml). According to our results, AKVUAM-1 and AKVUAM-2 synthetic peptides have the potential to be successful candidates for investigating their potential to block macrophage entry of bacilli as targeted.

A phenotypic drug discovery approach by latent interaction in deep learning

Contemporary drug discovery paradigms rely heavily on binding assays about the bio-physicochemical processes. However, this dominant approach suffers from overlooked higher-order interactions arising from the intricacies of molecular mechanisms, such as those involving cis-regulatory elements. It introduces potential impairments and restrains the potential development of computational methods. To address this limitation, I developed a deep learning model that leverages an end-to-end approach, relying exclusively on therapeutic information about drugs. By transforming textual representations of drug and virus genetic information into high-dimensional latent representations, this method evades the challenges arising from insufficient information about binding specificities. Its strengths lie in its ability to implicitly consider complexities such as epistasis and chemical-genetic interactions, and to handle the pervasive challenge of data scarcity. Through various modeling skills and data augmentation techniques, the proposed model demonstrates outstanding performance in out-of-sample validations, even in scenarios with unknown complex interactions. Furthermore, the study highlights the importance of chemical diversity for model training. While the method showcases the feasibility of deep learning in data-scarce scenarios, it reveals a promising alternative for drug discovery in situations where knowledge of underlying mechanisms is limited.

An update on the novel methods for the discovery of antiseizure and antiepileptogenic medications: where are we in 2024?

INTRODUCTION

Despite the availability of around 30 antiseizure medications, 1/3 of patients with epilepsy fail to become seizure-free upon pharmacological treatment. Available medications provide adequate symptomatic control in two-thirds of patients, but disease-modifying drugs are still scarce. Recently, though, new paradigms have been explored.

AREAS COVERED

Three areas are reviewed in which a high degree of innovation in the search for novel antiseizure and antiepileptogenic medications has been implemented: development of novel screening approaches, search for novel therapeutic targets, and adoption of new drug discovery paradigms aligned with a systems pharmacology perspective.

EXPERT OPINION

In the past, worldwide leaders in epilepsy have reiteratively stated that the lack of progress in the field may be explained by the recurrent use of the same molecular targets and screening procedures to identify novel medications. This landscape has changed recently, as reflected by the new Epilepsy Therapy Screening Program and the introduction of many in vitro and in vivo models that could possibly improve our chances of identifying first-in-class medications that may control drug-resistant epilepsy or modify the course of disease. Other milestones include the study of new molecular targets for disease-modifying drugs and exploration of a systems pharmacology perspective to design new drugs.

Dynamics of Molecular Self-Assembly of Short Peptides at Liquid-Solid Interfaces - Effect of Charged Amino Acid Point Mutations

Self-organizing solid-binding peptides on atomically flat solid surfaces offer a unique bio/nano hybrid platform, useful for understanding the basic nature of biology/solid coupling and their practical applications. The surface behavior of peptides is determined by their molecular folding, which is influenced by various factors and is challenging to study. Here, the effect of charged amino acids is studied on the self-assembly behavior of a directed evolution selected graphite-binding dodecapeptide on graphite surface. Two mutations, M6 and M8, are designed to introduce negatively and positively charged moieties, respectively, at the anchoring domain of the wild-type (WT) peptide, affecting both binding and assembly. The questions addressed here are whether mutant peptides exhibit molecular crystal formation and demonstrate molecular recognition on the solid surface based on the specific mutations. Frequency-modulated atomic force microscopy is used for observations of the surface processes dynamically in water at molecular resolution over several hours at the ambient. The results indicate that while the mutants display distinct folding and surface behavior, each homogeneously nucleates and forms 2D self-organized patterns, akin to the WT peptide. However, their growth dynamics, domain formation, and crystalline lattice structures differ significantly. The results represent a significant step toward the rational design of bio/solid interfaces, potent facilitators of a variety of future implementations.

A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation

Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent complexity of the drug design process, creates a difficult landscape for new researchers to enter. In this survey, we organize de novo drug design into two overarching themes: small molecule and protein generation. Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models. We take a broad approach to AI-driven drug design, allowing for both micro-level comparisons of various methods within each subtask and macro-level observations across different fields. We discuss parallel challenges and approaches between the two applications and highlight future directions for AI-driven de novo drug design as a whole. An organized repository of all covered sources is available at https://github.com/gersteinlab/GenAI4Drug.

The small molecule CBR-5884 inhibits the Candida albicans phosphatidylserine synthase

Systemic infections by Candida spp. are associated with high mortality rates, partly due to limitations in current antifungals, highlighting the need for novel drugs and drug targets. The fungal phosphatidylserine synthase, Cho1, from Candida albicans is a logical antifungal drug target due to its importance in virulence, absence in the host, and conservation among fungal pathogens. Inhibitors of Cho1 could serve as lead compounds for drug development, so we developed a target-based screen for inhibitors of purified Cho1. This enzyme condenses serine and cytidyldiphosphate-diacylglycerol (CDP-DAG) into phosphatidylserine (PS) and releases cytidylmonophosphate (CMP). Accordingly, we developed an in vitro nucleotidase-coupled malachite-green-based high throughput assay for purified C. albicans Cho1 that monitors CMP production as a proxy for PS synthesis. Over 7,300 molecules curated from repurposing chemical libraries were interrogated in primary and dose-responsivity assays using this platform. The screen had a promising average Z' score of ~0.8, and seven compounds were identified that inhibit Cho1. Three of these, ebselen, LOC14, and CBR-5884, exhibited antifungal effects against C. albicans cells, with fungicidal inhibition by ebselen and fungistatic inhibition by LOC14 and CBR-5884. Only CBR-5884 showed evidence of disrupting in vivo Cho1 function by inducing phenotypes consistent with the cho1∆∆ mutant, including a reduction of cellular PS levels. Kinetics curves and computational docking indicate that CBR-5884 competes with serine for binding to Cho1 with a Ki of 1,550 ± 245.6 nM. Thus, this compound has the potential for development into an antifungal compound.

IMPORTANCE

Fungal phosphatidylserine synthase (Cho1) is a logical antifungal target due to its crucial role in the virulence and viability of various fungal pathogens, and since it is absent in humans, drugs targeted at Cho1 are less likely to cause toxicity in patients. Using fungal Cho1 as a model, there have been two unsuccessful attempts to discover inhibitors for Cho1 homologs in whole-cell screens prior to this study. The compounds identified in these attempts do not act directly on the protein, resulting in the absence of known Cho1 inhibitors. The significance of our research is that we developed a high-throughput target-based assay and identified the first Cho1 inhibitor, CBR-5884, which acts both on the purified protein and its function in the cell. This molecule acts as a competitive inhibitor with a Ki value of 1,550 ± 245.6 nM and, thus, has the potential for development into a new class of antifungals targeting PS synthase.

Design and Evaluation of NSAID Derivatives as AKR1C3 Inhibitors for Breast Cancer Treatment through Computer-Aided Drug Design and In Vitro Analysis

Breast cancer is a major global health issue, causing high incidence and mortality rates as well as psychological stress for patients. Chemotherapy resistance is a common challenge, and the Aldo-keto reductase family one-member C3 enzyme is associated with resistance to anthracyclines like doxorubicin. Recent studies have identified celecoxib as a potential treatment for breast cancer. Virtual screening was conducted using a quantitative structure-activity relationship model to develop similar drugs; this involved backpropagation of artificial neural networks and structure-based virtual screening. The screening revealed that the C-6 molecule had a higher affinity for the enzyme (-11.4 kcal/mol), a lower half-maximal inhibitory concentration value (1.7 µM), and a safer toxicological profile than celecoxib. The compound C-6 was synthesized with an 82% yield, and its biological activity was evaluated. The results showed that C-6 had a more substantial cytotoxic effect on MCF-7 cells (62%) compared to DOX (63%) and celecoxib (79.5%). Additionally, C-6 had a less harmful impact on healthy L929 cells than DOX and celecoxib. These findings suggest that C-6 has promising potential as a breast cancer treatment.

Biophysics-Guided Lead Discovery of HBV Capsid Assembly Modifiers

Hepatitis B virus (HBV) is the leading cause of chronic liver pathologies worldwide. HBV nucleocapsid, a key structural component, is formed through the self-assembly of the capsid protein units. Therefore, interfering with the self-assembly process is a promising approach for the development of novel antiviral agents. Applied to HBV, this approach has led to several classes of capsid assembly modulators (CAMs). Here, we report structurally novel CAMs with moderate activity and low toxicity, discovered through a biophysics-guided approach combining docking, molecular dynamics simulations, and a series of assays with a particular emphasis on biophysical experiments. Several of the identified compounds induce the formation of aberrant capsids and inhibit HBV DNA replication in vitro, suggesting that they possess modest capsid assembly modulation effects. The synergistic computational and experimental approaches provided key insights that facilitated the identification of compounds with promising activities. The discovery of preclinical CAMs presents opportunities for subsequent optimization efforts, thereby opening new avenues for HBV inhibition.

Innovations in Antifungal Drug Discovery among Cell Envelope Synthesis Enzymes through Structural Insights

Life-threatening systemic fungal infections occur in immunocompromised patients at an alarming rate. Current antifungal therapies face challenges like drug resistance and patient toxicity, emphasizing the need for new treatments. Membrane-bound enzymes account for a large proportion of current and potential antifungal targets, especially ones that contribute to cell wall and cell membrane biosynthesis. Moreover, structural biology has led to a better understanding of the mechanisms by which these enzymes synthesize their products, as well as the mechanism of action for some antifungals. This review summarizes the structures of several current and potential membrane-bound antifungal targets involved in cell wall and cell membrane biosynthesis and their interactions with known inhibitors or drugs. The proposed mechanisms of action for some molecules, gleaned from detailed inhibitor-protein studeis, are also described, which aids in further rational drug design. Furthermore, some potential membrane-bound antifungal targets with known inhibitors that lack solved structures are discussed, as these might be good enzymes for future structure interrogation.

Reinvent 4: Modern AI-driven generative molecule design

REINVENT 4 is a modern open-source generative AI framework for the design of small molecules. The software utilizes recurrent neural networks and transformer architectures to drive molecule generation. These generators are seamlessly embedded within the general machine learning optimization algorithms, transfer learning, reinforcement learning and curriculum learning. REINVENT 4 enables and facilitates de novo design, R-group replacement, library design, linker design, scaffold hopping and molecule optimization. This contribution gives an overview of the software and describes its design. Algorithms and their applications are discussed in detail. REINVENT 4 is a command line tool which reads a user configuration in either TOML or JSON format. The aim of this release is to provide reference implementations for some of the most common algorithms in AI based molecule generation. An additional goal with the release is to create a framework for education and future innovation in AI based molecular design. The software is available from https://github.com/MolecularAI/REINVENT4 and released under the permissive Apache 2.0 license. Scientific contribution. The software provides an open-source reference implementation for generative molecular design where the software is also being used in production to support in-house drug discovery projects. The publication of the most common machine learning algorithms in one code and full documentation thereof will increase transparency of AI and foster innovation, collaboration and education.

Structure determination needs to go viral

Viral diseases are expected to cause new epidemics in the future, therefore, it is essential to assess how viral diversity is represented in terms of deposited protein structures. Here, data were collected from the Protein Data Bank to screen the available structures of viruses of interest to WHO. Excluding SARS-CoV-2 and HIV-1, less than 50 structures were found per year, indicating a lack of diversity. Efforts to determine viral structures are needed to increase preparedness for future public health challenges.

From intuition to AI: evolution of small molecule representations in drug discovery

Within drug discovery, the goal of AI scientists and cheminformaticians is to help identify molecular starting points that will develop into safe and efficacious drugs while reducing costs, time and failure rates. To achieve this goal, it is crucial to represent molecules in a digital format that makes them machine-readable and facilitates the accurate prediction of properties that drive decision-making. Over the years, molecular representations have evolved from intuitive and human-readable formats to bespoke numerical descriptors and fingerprints, and now to learned representations that capture patterns and salient features across vast chemical spaces. Among these, sequence-based and graph-based representations of small molecules have become highly popular. However, each approach has strengths and weaknesses across dimensions such as generality, computational cost, inversibility for generative applications and interpretability, which can be critical in informing practitioners' decisions. As the drug discovery landscape evolves, opportunities for innovation continue to emerge. These include the creation of molecular representations for high-value, low-data regimes, the distillation of broader biological and chemical knowledge into novel learned representations and the modeling of up-and-coming therapeutic modalities.

Methylene blue in anticancer photodynamic therapy: systematic review of preclinical studies

Background: Methylene blue has a long history of clinical application. Thanks to phenothiazine chromophore, it has potential in photodynamic anticancer therapy. In spite of the growing body of literature that has evaluated the action of this dye on different types of cancer, the systematic understanding of this problem is still lacking. Therefore, this systematic review was performed to study the efficacy of methylene blue in photodynamic anticancer therapy. Methods: This systematic review was carried out in accordance with the PRISMA guidelines, and the study protocol was registered in PROSPERO (CRD42022368738). Articles for the systematic review were identified through the PubMed database. SYRCLE's risk of bias tool was used to assess the studies. The results of systematic analysis are presented as narrative synthesis. Results: Ten studies met the inclusion criteria and these full texts were reviewed. In the selected articles, the dosage of dye infusion ranged from 0.04 to 24.12 mg/kg. The effectiveness of photodynamic therapy with methylene blue against different types of cancer was confirmed by a decrease in tumor sizes in seven articles. Conclusion: The results of the systematic review support the suggestions that photodynamic therapy with methylene blue helps against different types of cancer, including colorectal tumor, carcinoma, and melanoma. In cases of nanopharmaceutics use, a considerable increase of anticancer therapy effectiveness was observed. The further research into methylene blue in photodynamic anticancer therapy is needed. Systematic Review Registration: (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=368738), identifier (CRD42022368738).

Exploring N-myristoyltransferase as a promising drug target against parasitic neglected tropical diseases

Neglected tropical diseases (NTDs) constitute a group of approximately 20 infectious diseases that mainly affect the impoverished population without basic sanitation in tropical countries. These diseases are responsible for many deaths worldwide, costing billions of dollars in public health investment to treat and control these infections. Among them are the diseases caused by protozoa of the Trypanosomatid family, which constitute Trypanosoma cruzi (Chagas disease), Trypanosoma brucei (sleeping sickness), and Leishmaniasis. In addition, there is a classification of other diseases, called the big three, AIDS, tuberculosis, and malaria, which are endemic in countries with tropical conditions. Despite the high mortality rates, there is still a gap in the treatment. The drugs have a high incidence of side effects and protozoan resistance, justifying the investment in developing new alternatives. In fact, the Target-Based Drug Design (TBDD) approach is responsible for identifying several promising compounds, and among the targets explored through this approach, N-myristoyltransferase (NMT) stands out. It is an enzyme related to the co-translational myristoylation of N-terminal glycine in various peptides. The myristoylation process is a co-translation that occurs after removing the initiator methionine. This process regulates the assembly of protein complexes and stability, which justifies its potential as a drug target. In order to propose NMT as a potential target for parasitic diseases, this review will address the entire structure and function of this enzyme and the primary studies demonstrating its promising potential against Leishmaniasis, T. cruzi, T. brucei, and malaria. We hope our information can help researchers worldwide search for potential drugs against these diseases that have been threatening the health of the world's population.

Recent Advances in Covalent Drug Discovery

In spite of the increasing number of biologics license applications, the development of covalent inhibitors is still a growing field within drug discovery. The successful approval of some covalent protein kinase inhibitors, such as ibrutinib (BTK covalent inhibitor) and dacomitinib (EGFR covalent inhibitor), and the very recent discovery of covalent inhibitors for viral proteases, such as boceprevir, narlaprevir, and nirmatrelvir, represent a new milestone in covalent drug development. Generally, the formation of covalent bonds that target proteins can offer drugs diverse advantages in terms of target selectivity, drug resistance, and administration concentration. The most important factor for covalent inhibitors is the electrophile (warhead), which dictates selectivity, reactivity, and the type of protein binding (i.e., reversible or irreversible) and can be modified/optimized through rational designs. Furthermore, covalent inhibitors are becoming more and more common in proteolysis, targeting chimeras (PROTACs) for degrading proteins, including those that are currently considered to be 'undruggable'. The aim of this review is to highlight the current state of covalent inhibitor development, including a short historical overview and some examples of applications of PROTAC technologies and treatment of the SARS-CoV-2 virus.

Design, synthesis and cytotoxic evaluation of a selective serotonin reuptake inhibitor (SSRI) by virtual screening

Depression is one of the most common mental illnesses, affecting almost 300 million people. According to the WHO, depression is one of the world's leading causes of disability and morbidity. People with this illness require both psychological and pharmaceutical treatment because severe depressive episodes often result in suicide. Selective serotonin reuptake inhibitors (SSRI) are widely used antidepressants that target the human serotonin transporter (hSERT). The crystallization of hSERT and the experimental data available allows cost and time-efficient computational tools like virtual screening (VS) to be utilized in the development of therapeutic agents. Here, we synthesized, characterized, and evaluated the biological activity of a novel SSRI analog of paroxetine, rationally designed by applying an artificial neural network-based QSAR model and a molecular docking analysis on hSERT. The analog N-substituted 18a showed higher affinity for the transporter (-10.2 kcal/mol), lower Ki value (1.19 nM) and a safer toxicological profile than paroxetine and was synthesized with a 71% yield. The in vitro cytotoxicity of the analog was evaluated using human glioblastoma (U87 MG), human neuroblastoma (SH SY5Y) and murine fibroblast (L929) cell lines. Also, the hemolytic ability of the compound was assessed on human erythrocytes. Results showed that analog 18a did not exhibit cytotoxic activity on the cell lines used and has no hemolytic activity at any of the concentrations tested, whereas with paroxetine, hemolysis was observed at 2.3, 1.29 y 0.67 mM. Based on these results, it is possible to suggest that analog 18a could be a promising new SSRI candidate for the treatment of this illness.

Identification of Putative Drug Targets in Highly Resistant Gram-Negative Bacteria; and Drug Discovery Against Glycyl-tRNA Synthetase as a New Target

Background

Gram-negative bacterial infections are on the rise due to the high prevalence of multidrug-resistant bacteria, and efforts must be made to identify novel drug targets and then new antibiotics.

Methods

In the upstream part, we retrieved the genome sequences of 4 highly resistant Gram-negative bacteria (e.g., Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Enterobacter cloacae). The core proteins were assessed to find common, cytoplasmic, and essential proteins with no similarity to the human proteome. Novel drug targets were identified using DrugBank, and their sequence conservancy was evaluated. Protein Data Bank files and STRING interaction networks were assessed. Finally, the aminoacylation cavity of glycyl-tRNA synthetase (GlyQ) was virtually screened to identify novel inhibitors using AutoDock Vina and the StreptomeDB library. Ligands with high binding affinity were clustered, and then the pharmacokinetics properties of therapeutic agents were investigated.

Results

A total of 6 common proteins (e.g., RP-L28, RP-L30, RP-S20, RP-S21, Rnt, and GlyQ) were selected as novel and widespread drug targets against highly resistant Gram-negative superbugs based on different criteria. In the downstream analysis, virtual screening revealed that Rimocidin, Flavofungin, Chaxamycin, 11,11'-O-dimethyl-14'-deethyl-14'-methylelaiophylin, and Platensimycin were promising hit compounds against GlyQ protein. Finally, 11,11'-O-dimethyl-14'-deethyl-14'-methylelaiophylin was identified as the best potential inhibitor of GlyQ protein. This compound showed high absorption capacity in the human intestine.

Conclusion

The results of this study provide 6 common putative new drug targets against 4 highly resistant and Gram-negative bacteria. Moreover, we presented 5 different hit compounds against GlyQ protein as a novel therapeutic target. However, further in vitro and in vivo studies are needed to explore the bactericidal effects of proposed hit compounds against these superbugs.

Structure activity relationship (SAR) and anticancer activity of pyrrolidine derivatives: Recent developments and future prospects (A review)

Pyrrolidine molecules are a significant class of synthetic and natural plant metabolites, which show the diversity of pharmacological activities. An extensive variety of synthetic pyrrolidine compounds with numerous derivatization like spirooxindole, thiazole, metal complexes, coumarin, etc have revealed significant anticancer activity. Pyrrolidine molecules are found not only as potential anticancer candidates but also retain the lowest side effects. Depending upon the diverse substitution patterns of the derivatives, these molecules have demonstrated an incredible ability to regulate the various targets to give excellent anti-proliferative activities. Taking these into consideration, efforts have been taken by the scientific fraternity to design and develop a potent anticancer scaffold with negligible side effects. In the present review, we cover the latest advancements in the synthesis of pyrrolidine molecules which have promising anticancer activity toward numerous cancer cell lines. Additionally, it also highlights the effectiveness of derivatives via elucidation of Structural-Activity-Relationship (SAR) which is discussed in detail.

Repurposing FDA-approved drugs cetilistat, abiraterone, diiodohydroxyquinoline, bexarotene, and remdesivir as potential inhibitors against RNA dependent RNA polymerase of SARS-CoV-2: A comparative in silico perspective

Vaccines are undoubtedly the most effective means of combating viral diseases like COVID-19. However, there are risks associated with vaccination, such as incomplete viral deactivation or potential adverse effects in humans. However, designing and developing a panel of new drug molecules is always encouraged. In an emergency, drug repurposing research is one of the most potent and rapid options. RdRp (RNA-dependent RNA polymerase) has been discovered to play a pivotal role in viral replication. In this study, FDA-approved drugs bexarotene, diiodohydroxyquinoline, abiraterone, cetilistat, and remdesivir were repurposed against the RdRp by molecular modeling, docking, and dynamic simulation. Furthermore, to validate the potency of these drugs, we compared them to the antiviral remdesivir, which inhibits RdRp. Our finding indicated that the selected drugs have a high potential to be developed as RdRp inhibitors and, with further validation studies, could serve as potential drugs for the treatment of COVID-19.

Exploring new antiviral targets for influenza and COVID-19: Mapping promising hot spots in viral RNA polymerases

Influenza and COVID-19 are infectious respiratory diseases that represent a major concern to public health with social and economic impact worldwide, for which the available therapeutic options are not satisfactory. The RdRp has a central role in viral replication and thus represents a major target for the development of antiviral approaches. In this study, we focused on Influenza A virus PB1 polymerase protein and the betacoronaviruses nsp12 polymerase protein, considering their functional and structural similarities. We have performed conservation and druggability analysis to map conserved druggable regions, that may have functional or structural importance in these proteins. We disclosed the most promising and new targeting regions for the discovery of new potential polymerase inhibitors. Conserved druggable regions of putative interaction with favipiravir and molnupiravir were also mapped. We have also compared and integrated the current findings with previous research.

Structure-based identification of novel scaffolds as potential HIV-1 entry inhibitors involving CCR5

C-C chemokine receptor 5 (CCR5), which is part of the chemokine receptor family, is a member of the G protein-coupled receptor superfamily. The interactions of CCR5 with HIV-1 during viral entry position it as an effective therapeutic target for designing potent antiviral therapies. The small-molecule Maraviroc was approved by the FDA as a CCR5 drug in 2007, while clinical trials failure has characterised many of the other CCR5 inhibitors. Thus, the continual identification of potential CCR5 inhibitors is, therefore, warranted. In this study, a structure-based discovery approach has been utilised to screen and retrieved novel potential CCR5 inhibitors from the Asinex antiviral compound (∼ 8,722) database. Explicit lipid-bilayer molecular dynamics simulation, in silico physicochemical and pharmacokinetic analyses, were further performed for the top compounds. A total of 23 structurally diverse compounds with binding scores higher than Maraviroc were selected. Subsequent molecular dynamics (MD) simulations analysis of the top four compounds LAS 51495192, BDB 26405401, BDB 26419079, and LAS 34154543, maintained stability at the CCR5 binding site. Furthermore, these compounds made pertinent interactions with CCR5 residues critical for the HIV-1 gp120-V3 loop binding such as Trp86, Tyr89, Phe109, Tyr108, Glu283 and Tyr251. Additionally, the predicted in silico physicochemical and pharmacokinetic descriptors of the selected compounds were within the acceptable range for drug-likeness. The results suggest positive indications that the identified molecules may represent promising CCR5 entry inhibitors. Further structural optimisations and biochemical testing of the proposed compounds may assist in the discovery of effective HIV-1 therapy.Communicated by Ramaswamy H. Sarma.

Complete reconstruction of dasatinib unbinding pathway from c-Src kinase by supervised molecular dynamics simulation method; assessing efficiency and trustworthiness of the method

Over the past years, rational drug design has gained lots of attention since employing it gave the world targeted therapy and more effective treatment solutions. Structure-based drug design (SBDD) is an excellent tool in rational drug design that takes advantage of accurate methods such as unbiased molecular dynamics (UMD) simulation for designing and optimizing molecular entities by understanding the binding and unbinding pathways of the binders. Supervised molecular dynamics (SuMD) simulation is a branch of UMD in which long-duration simulations are turned into short simulations, called replica, and a specific parameter is monitored throughout the simulation. In this work, we utilized this strategy to reconstruct the unbinding pathway of the anticancer drug dasatinib from its target protein, the c-Src kinase. Several unbinding events with valuable details were achieved. Then, to assess the efficiency and trustworthiness of the SuMD method, the unbinding pathway was also reconstructed by conventional UMD simulation, which uncovered some of the limitations of this method, such as limited sampling of the active site and finding the metastable states in the unbinding pathway. Furthermore, in times like these, when the world is desperate to find treatments for the Covid-19 disease, we think these methods are of exceptional value.Communicated by Ramaswamy H. Sarma.

Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments

The question of how cell-to-cell differences in transcription rate affect RNA count distributions is fundamental for understanding biological processes underlying transcription. Answering this question requires quantitative models that are both interpretable (describing concrete biophysical phenomena) and tractable (amenable to mathematical analysis). This enables the identification of experiments which best discriminate between competing hypotheses. As a proof of principle, we introduce a simple but flexible class of models involving a continuous stochastic transcription rate driving a discrete RNA transcription and splicing process, and compare and contrast two biologically plausible hypotheses about transcription rate variation. One assumes variation is due to DNA experiencing mechanical strain, while the other assumes it is due to regulator number fluctuations. We introduce a framework for numerically and analytically studying such models, and apply Bayesian model selection to identify candidate genes that show signatures of each model in single-cell transcriptomic data from mouse glutamatergic neurons.

Photodynamic antitumor activity of Gallium(III) and Phosphorus(V) complexes of trimethoxyl A2B triaryl corrole

Two new trimethoxyl A₂B triaryl corroles 10-(2,4,6-trimethoxyphenyl)-5,15-bis(pentafluorophenyl)- corrole (1) and 10-(3,4,5-trimethoxyphenyl)-5,15-bis(pentafluorophenyl)-corrole (2) and their gallium(III) and phosphorus(V) (1-Ga, 1-P, 2-Ga and 2-P) complexes had been prepared and well characterized by UV-vis, NMR and HR-MS. Among all compounds, 2-Ga, 1-P and 2-P showed excellent in vivo photodynamic activity against the MDA-MB-231, A549, Hela and HepG2 cell lines upon light irradiation at 625 nm. And 2-P even exhibited higher phototoxicity than the clinical photosensitizer temoporfin. Also, 2-P exhibited the highest singlet oxygen quantum yield and photostability. The preliminary investigation revealed that 2-P could be rapidly absorbed by tumor cells and mainly located in the cytoplasm. After photodynamic therapy (PDT) treatment with 2-P, mitochondrial membrane potential destruction, intracellular ROS level increasing and nuclear fragmentation of cancer cells could be observed. Cell cycle analysis demonstrated that the 2-P PDT may cause tumor cell arrest at sub-G1 stage and induce early and late apoptosis of cells. These results suggest that 2-P is a promising candidate as a photosensitizer for photodynamic therapy.

Significant perspectives on various viral infections targeted antiviral drugs and vaccines including COVID-19 pandemicity

A virus enters a living organism and recruits host metabolism to reproduce its own genome and proteins. The viral infections are intricate and cannot be completely removed through existing antiviral drugs. For example, the herpes, influenza, hepatitis and human immunodeficiency viruses are a few dreadful ones amongst them. Significant studies are needed to understand the viral entry and their growth in host cells to design effective antivirals. This review emphasizes the range of therapeutical antiviral drugs, inhibitors along with vaccines to fight against viral pathogens, especially for combating COVID-19. Moreover, we have provided the basic and in depth information about viral targets, drugs availability, their mechanisms of action, method of prevention of viral diseases and highlighted the significances of anticoagulants, convalescent plasma for COVID-19 treatment, scientific details of airborne transmission, characteristics of antiviral drug delivery using nanoparticles/carriers, nanoemulsions, nanogels, metal based nanoparticles, alike the future nanosystems through nanobubbles, nanofibers, nanodiamonds, nanotraps, nanorobots and eventually, the therapeutic applications of micro- and nanoparticulates, current status for clinical development against COVID-19 together with environmental implications of antivirals, gene therapy etc., which may be useful for repurposing and designing of novel antiviral drugs against various dreadful diseases, especially the SARS-CoV-2 and other associated variants.

Trends and patterns in cancer nanotechnology research: A survey of NCI's caNanoLab and nanotechnology characterization laboratory

Cancer nanotechnologies possess immense potential as therapeutic and diagnostic treatment modalities and have undergone significant and rapid advancement in recent years. With this emergence, the complexities of data standards in the field are on the rise. Data sharing and reanalysis is essential to more fully utilize this complex, interdisciplinary information to answer research questions, promote the technologies, optimize use of funding, and maximize the return on scientific investments. In order to support this, various data-sharing portals and repositories have been developed which not only provide searchable nanomaterial characterization data, but also provide access to standardized protocols for synthesis and characterization of nanomaterials as well as cutting-edge publications. The National Cancer Institute's (NCI) caNanoLab is a dedicated repository for all aspects pertaining to cancer-related nanotechnology data. The searchable database provides a unique opportunity for data mining and the use of artificial intelligence and machine learning, which aims to be an essential arm of future research studies, potentially speeding the design and optimization of next-generation therapies. It also provides an opportunity to track the latest trends and patterns in nanomedicine research. This manuscript provides the first look at such trends extracted from caNanoLab and compares these to similar metrics from the NCI's Nanotechnology Characterization Laboratory, a laboratory providing preclinical characterization of cancer nanotechnologies to researchers around the globe. Together, these analyses provide insight into the emerging interests of the research community and rise of promising nanoparticle technologies.

Metabolomics and Network Pharmacology in the Exploration of the Multi-Targeted Therapeutic Approach of Traditional Medicinal Plants

Metabolomic is generally characterized as a comprehensive and the most copious analytical technique for the identification of targeted and untargeted metabolite diversity in a biological system. Recently, it has exponentially been used for phytochemical analysis and variability among plant metabolites, followed by chemometric analysis. Network pharmacology analysis is a computational technique used for the determination of multi-mechanistic and therapeutic evaluation of chemicals via interaction with the genomes involved in targeted or untargeted diseases. In considering the facts, the present review aims to explore the role of metabolomics and network pharmacology in the scientific validation of therapeutic claims as well as to evaluate the multi-targeted therapeutic approach of traditional Indian medicinal plants. The data was collected from different electronic scientific databases such as Google Scholar, Science Direct, ACS publication, PubMed, Springer, etc., using different keywords such as metabolomics, techniques used in metabolomics, chemometric analysis, a bioinformatic tool for drug discovery and development, network pharmacology, methodology and its role in biological evaluation of chemicals, etc. The screened articles were gathered and evaluated by different experts for their exclusion and inclusion in the final draft of the manuscript. The review findings suggest that metabolomics is one of the recent most precious and effective techniques for metabolite identification in the plant matrix. Various chemometric techniques are copiously used for metabolites discrimination analysis hence validating the unique characteristic of herbal medicines and their derived products concerning their authenticity. Network pharmacology remains the only option for the unique and effective analysis of hundreds of chemicals or metabolites via genomic interaction and thus validating the multi-mechanistic and therapeutic approach to explore the pharmacological aspects of herbal medicines for the management of the disease.

Screening, molecular simulation & in silico kinetics of virtually designed covid-19 main protease inhibitors

Coronavirus (covid-19) infection is considered to be deadliest ever pandemic experienced by the human being. It has very badly affected the socio-economic health of human and stuck the scientific community to think and rethink about its complete eradication. But due to no effective treatment or unavailability of vaccine the health professional could not show any significant improvement to control the pandemic. The situation needs newer molecule, vaccine or effective treatment to control covid-19 infection. Different target in viruses has been explored and proteases enzymes were found to be therapeutically effective target for the design of potential anti-covid-19 molecule as it plays the vital role in viral replication and assembly. Structure-based drug design was employed to discover the small molecule of anti-covid-19. Here we considered the small library of naturally occurring polyphenolic compounds and molecular docking, Molecular dynamics (MD) simulations, free binding energy calculation and in-silico ADME calculations to identify the newer HITs. Based upon their score the two molecules were identified as promising candidate. The docking scores were found to be −7.643 and −7.065 for the HIT1 and HIT-2 respectively. In MD simulations study the RMSD values were found to be 4.3 Å & 4.9 Å respectively. To validate these results MM-GBSA was performed and their binding free energies were computationally determined. The prime energy values of identified HITs (−13412.45 & −13441.8 kJ/mole) were found to be very close proximity to reference molecule (−13493.05 kJ/mole). Then in-silico ADME calculations were performed to calculate the drug likeliness identified HITs. BY considering all the values comparative to reference molecule and obtained in-silico pharmacokinetic properties of identified HITs we can suggest that HIT-1 and HIT-2 would be the most promising molecules that can inhibit the main protease enzyme of covid-19. These two molecules would become the potential drug candidate for the treatment of covid-19 infections.

Biochemical and Kinetic Characterization of the Glucose-6-Phosphate Dehydrogenase from Helicobacter pylori Strain 29CaP

Helicobacter pylori (H. pylori) has been proposed as the foremost risk factor for the development of gastric cancer. We found that H. pylori express the enzyme glucose-6-phosphate dehydrogenase (HpG6PD), which participates in glucose metabolism via the pentose phosphate pathway. Thus, we hypothesized that if the biochemical and physicochemical characteristics of HpG6PD contrast with the host G6PD (human G6PD, HsG6PD), HpG6PD becomes a potential target for novel drugs against H. pylori. In this work, we characterized the biochemical properties of the HpG6PD from the H.pylori strain 29CaP and expressed the active recombinant protein, to analyze its steady-state kinetics, thermostability, and biophysical aspects. In addition, we analyzed the HpG6PD in silico structural properties to compare them with those of the HsG6PD. The optimal pH for enzyme activity was 7.5, with a T_1/2 of 46.6 °C, at an optimum stability temperature of 37 °C. The apparent K_m values calculated for G6P and NADP⁺ were 75.0 and 12.8 µM, respectively. G6P does not protect HpG6PD from trypsin digestion, but NADP⁺ does protect the enzyme from trypsin and guanidine hydrochloride (Gdn-HCl). The biochemical characterization of HpG6PD contributes to knowledge regarding H. pylori metabolism and opens up the possibility of using this enzyme as a potential target for specific and efficient treatment against this pathogen; structural alignment indicates that the three-dimensional (3D) homodimer model of the G6PD protein from H. pylori is different from the 3D G6PD of Homo sapiens.

Homology Modeling, de Novo Design of Ligands, and Molecular Docking Identify Potential Inhibitors of Leishmania donovani 24-Sterol Methyltransferase

The therapeutic challenges pertaining to leishmaniasis due to reported chemoresistance and toxicity necessitate the need to explore novel pathways to identify plausible inhibitory molecules. Leishmania donovani 24-sterol methyltransferase (LdSMT) is vital for the synthesis of ergosterols, the main constituents of Leishmania cellular membranes. So far, mammals have not been shown to possess SMT or ergosterols, making the pathway a prime candidate for drug discovery. The structural model of LdSMT was elucidated using homology modeling to identify potential novel 24-SMT inhibitors via virtual screening, scaffold hopping, and de-novo fragment-based design. Altogether, six potential novel inhibitors were identified with binding energies ranging from −7.0 to −8.4 kcal/mol with e-LEA3D using 22,26-azasterol and S1–S4 obtained from scaffold hopping via the ChEMBL, DrugBank, PubChem, ChemSpider, and ZINC15 databases. These ligands showed comparable binding energy to 22,26-azasterol (−7.6 kcal/mol), the main inhibitor of LdSMT. Moreover, all the compounds had plausible ligand efficiency-dependent lipophilicity (LELP) scores above 3. The binding mechanism identified Tyr92 to be critical for binding, and this was corroborated via molecular dynamics simulations and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) calculations. The ligand A1 was predicted to possess antileishmanial properties with a probability of activity (Pa) of 0.362 and a probability of inactivity (Pi) of 0.066, while A5 and A6 possessed dermatological properties with Pa values of 0.205 and 0.249 and Pi values of 0.162 and 0.120, respectively. Structural similarity search via DrugBank identified vabicaserin, daledalin, zanapezil, imipramine, and cefradine with antileishmanial properties suggesting that the de-novo compounds could be explored as potential antileishmanial agents.

Inverse design of 3d molecular structures with conditional generative neural networks

The rational design of molecules with desired properties is a long-standing challenge in chemistry. Generative neural networks have emerged as a powerful approach to sample novel molecules from a learned distribution. Here, we propose a conditional generative neural network for 3d molecular structures with specified chemical and structural properties. This approach is agnostic to chemical bonding and enables targeted sampling of novel molecules from conditional distributions, even in domains where reference calculations are sparse. We demonstrate the utility of our method for inverse design by generating molecules with specified motifs or composition, discovering particularly stable molecules, and jointly targeting multiple electronic properties beyond the training regime.

Development of tBu-phenyl Acetamide Appended Thiacalix[4]arene as "Turn-ON" Fluorescent Probe for Selective Recognition of Hg(II) Ions

Herein, a novel N-(4-(tert-butyl)-phenyl)-2-chloroacetamide functionalized thiacalix[4]arene architecture, viz TCAN2PA has been synthesized and the sensing behaviour towards metal ions were explored. The probe, TCAN2PA displayed "turn-on" fluorescence response towards Hg(II) ions in acetonitrile over a series of competing common metal ions. A bathochromic shift in absorption band along with a significant "Turn-On" fluorescence behaviour of TCAN2PA was observed upon interaction with Hg(II) ions. The lower rim modification of thiacalixarene with N-(4-(tert-butyl)-phenyl)-2-chloroacetamide actively contributes toward the fluorescence property due to the presence of strong electron-donating aryl amido substituent. Fluorescence titration experiments were conducted to find out the limit of detection and to understand binding stoichiometry as well. The electron transfer interactions between the electron rich TCAN2PA host with Hg(II) ions have been postulated which is also supported by computational modelling insights.

CTV Based Sensor for the Detection of Ni2+ Ions With Real Sample Analysis Based on Mechanism of Fluorescence Along with Computational Insights

Identification and detection of harmful contaminants such as nickel and other materials from soil and water is critical necessity at the present moment. So with this motive to detect and identify harmful pollutants, a novel cyclotriveratrylene based derivative was prepared for the detection and binding of harmful pollutants which had the properties of fluorescence. The newly derivative of Cyclotriveratrylene was found to be highly sensitive and selective towards Ni²⁺ ions. The complexation behaviour of this newly synthesised molecule was studied in presence of transition elements. Also computational methods such as docking, molecular modelling and DFT were used to study the molecular orbitals and energies of CTG-NBEP. The detection of Ni²⁺ from water samples were also carried out successfully.

Mapping the Substrate-Binding Sites in the Phosphatidylserine Synthase in Candida albicans

The fungal phosphatidylserine (PS) synthase, a membrane protein encoded by the CHO1 gene, is a potential drug target for pathogenic fungi, such as Candida albicans. However, both substrate-binding sites of C. albicans Cho1 have not been characterized. Cho1 has two substrates: cytidyldiphosphate-diacylglycerol (CDP-DAG) and serine. Previous studies identified a conserved CDP-alcohol phosphotransferase (CAPT) binding motif, which is present within Cho1. We tested the CAPT motif for its role in PS synthesis by mutating conserved residues using alanine substitution mutagenesis. PS synthase assays revealed that mutations in all but one conserved amino acid within the CAPT motif resulted in decreased Cho1 function. In contrast, there were no clear motifs in Cho1 for binding serine. Therefore, to identify the serine binding site, PS synthase sequences from three fungi were aligned with sequences of a similar enzyme, phosphatidylinositol (PI) synthase, from the same fungi. This revealed a motif that was unique to PS synthases. Using alanine substitution mutagenesis, we found that some of the residues in this motif are required for Cho1 function. Two alanine substitution mutants, L184A and R189A, exhibited contrasting impacts on PS synthase activity, and were characterized for their Michaelis-Menten kinetics. The L184A mutant displayed enhanced PS synthase activity and showed an increased V_max. In contrast, R189A showed decreased PS synthase activity and increased K_m for serine, suggesting that residue R189 is involved in serine binding. These results help to characterize PS synthase substrate binding, and should direct rational approaches for finding Cho1 inhibitors that may lead to better antifungals.

Fundamental considerations in drug design

The drug discovery paradigm has been very time-consuming, challenging, and expensive; however, the disease conditions originating from bacteria, virus, protozoa, fungus and other microorganisms are steadily shooting up. For instance, COVID-19 is the latest viral infection that affects millions of people and the world’s economy very severely. Therefore, the quest for discovery of novel and potent drug compounds against deadly pathogens is crucial at the moment. Despite a lot of drawbacks in drug discovery and development and its pertaining technology, the advancement must be taken into account so the time duration and cost would be minimized. In this chapter, basic principles in drug design and discovery have been discussed together with advances in drug development.

An outlook on suicide enzyme inhibition and drug design

There have been recent renewed interests in the importance of suicide enzyme inhibition. The principal objective of this review is to investigate all types of suicide inhibitions for natural enzymes, artificial biocatalysts as well as therapeutic potential of enzyme suicide inhibition. It is discussed the suicide inhibition beneficial in drug design and treatments and non-beneficial achievements for some industrial enzymes such as HRP peroxidase enzyme. The design of biomimetic artificial enzymes explained to prevent inhibition by protecting the active site via environmental conditions. Suicide enzyme inhibition development can be the key mechanism against sever diseases such as SARS. In this report, suicide enzyme inactivation classes are classified based on target enzyme groups via their substrates.

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.

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.