The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization

Targeting bacterial growth in biofilm conditions: rational design of novel inhibitors to mitigate clinical and food contamination using QSAR

Antimicrobial resistance (AMR) is a pressing global issue exacerbated by the abuse of antibiotics and the formation of bacterial biofilms, which cause up to 80% of human bacterial infections. This study presents a computational strategy to address AMR by developing three novel quantitative structure-activity relationship (QSAR) models based on molecular topology to identify potential anti-biofilm and antibacterial agents. The models aim to determine the chemo-topological pattern of Gram (+) antibacterial, Gram (-) antibacterial, and biofilm formation inhibition activity. The models were applied to the virtual screening of a commercial chemical database, resulting in the selection of 58 compounds. Subsequent in vitro assays showed that three of these compounds exhibited the most promising antibacterial activity, with potential applications in enhancing food and medical device safety.

Discovery of Nanomolar Inhibitors for Human Dihydroorotate Dehydrogenase Using Structure-Based Drug Discovery Methods

We used a structure-based drug discovery approach to identify novel inhibitors of human dihydroorotate dehydrogenase (DHODH), which is a therapeutic target for treating cancer and autoimmune and inflammatory diseases. In the case of acute myeloid leukemia, no previously discovered DHODH inhibitors have yet succeeded in this clinical application. Thus, there remains a strong need for new inhibitors that could be used as alternatives to the current standard-of-care. Our goal was to identify novel inhibitors of DHODH. We implemented prefiltering steps to omit PAINS and Lipinski violators at the earliest stages of this project. This enriched compounds in the data set that had a higher potential of favorable oral druggability. Guided by Glide SP docking scores, we found 20 structurally unique compounds from the ChemBridge EXPRESS-pick library that inhibited DHODH with IC50, DHODH values between 91 nM and 2.7 μM. Ten of these compounds reduced MOLM-13 cell viability with IC50, MOLM-13 values between 2.3 and 50.6 μM. Compound 16 (IC50, DHODH = 91 nM) inhibited DHODH more potently than the known DHODH inhibitor, teriflunomide (IC50, DHODH = 130 nM), during biochemical characterizations and presented a promising scaffold for future hit-to-lead optimization efforts. Compound 17 (IC50, MOLM-13 = 2.3 μM) was most successful at reducing survival in MOLM-13 cell lines compared with our other hits. The discovered compounds represent excellent starting points for the development and optimization of novel DHODH inhibitors.

Principal Component Analysis (PCA) of Molecular Descriptors for Improving Permeation through the Blood-Brain Barrier of Quercetin Analogues

Despite its beneficial pharmacological effects in the brain, partly by modulating inositol phosphate multikinase (IPMK) activity, the therapeutic use of quercetin is limited due to its poor solubility, low oral bioavailability, and low permeability through the blood-brain barrier (BBB). We aimed to identify quercetin analogues with improved BBB permeability and preserved binding affinities towards IPMK and to identify the molecular characteristics required for them to permeate the BBB. Binding affinities of quercetin analogues towards IPMK were determined by molecular docking. Principal component analysis (PCA) was applied to identify the molecular descriptors contributing to efficient permeation through the BBB. Among 34 quercetin analogues, 19 compounds were found to form more stable complexes with IPMK, and the vast majority were found to be more lipophilic than quercetin. Using two distinct in silico techniques, insufficient BBB permeation was determined for all quercetin analogues. However, using the PCA method, the descriptors related to intrinsic solubility and lipophilicity (logP) were identified as mainly responsible for clustering four quercetin analogues (trihydroxyflavones) with the highest BBB permeability. The application of PCA revealed that quercetin analogues could be classified with respect to their structural characteristics, which may be utilized in further analogue syntheses and lead optimization of BBB-penetrating IPMK modulators as neuroprotective agents.

Predicting drug-target binding affinity with cross-scale graph contrastive learning

Identifying the binding affinity between a drug and its target is essential in drug discovery and repurposing. Numerous computational approaches have been proposed for understanding these interactions. However, most existing methods only utilize either the molecular structure information of drugs and targets or the interaction information of drug-target bipartite networks. They may fail to combine the molecule-scale and network-scale features to obtain high-quality representations. In this study, we propose CSCo-DTA, a novel cross-scale graph contrastive learning approach for drug-target binding affinity prediction. The proposed model combines features learned from the molecular scale and the network scale to capture information from both local and global perspectives. We conducted experiments on two benchmark datasets, and the proposed model outperformed existing state-of-art methods. The ablation experiment demonstrated the significance and efficacy of multi-scale features and cross-scale contrastive learning modules in improving the prediction performance. Moreover, we applied the CSCo-DTA to predict the novel potential targets for Erlotinib and validated the predicted targets with the molecular docking analysis.

Physical-chemical repurposing of drugs. History of its formation in Russia

It is reported that the traditional scheme of finding and developing a new drug and conducting the whole complex of preclinical studies requires several thousand chemical compounds, hundreds of millions of US dollars and more than 12 years of work. It is shown that physicochemical pharmacology was born in Russia at the end of the 20th century, which in our days has been transformed into physicochemical repurposing of known medicines. The first successfully repurposed known drug was a solution of 4% potassium chloride, which had previously traditionally belonged to the group of macro- and microelements, used by intravenous injections to regulate acid-base balance and rhythmic activity of the heart. In 1983, it was stated that this medicinal solution, when heated to 3942C and applied topically by irrigation of the bleeding surface, could be classified as a vasoconstrictor and hemostatic drug. Hyperthermia was used as a physico-chemical reprofiling factor, which, according to the Arrhenius law, accelerated and intensified, on the one hand, the spastic action of K+ cations on the gaping blood vessels (formation of hyperkalium contracture in the smooth muscles of the vascular wall) and, on the other hand, the blood clotting process in the wound. In subsequent years, the promise of physicochemical repurposing of known drugs was shown on the example of water, hydrogen peroxide, sodium chloride and sodium bicarbonate by purposefully changing their temperature, acid, osmotic activity, as well as the amount and quality of gas content (passing). A chronology of the physicochemical repurposing of known drug solutions and tablets is described and the essence of such new groups of drugs as bleachers of bruises and pyolytics is given. It is shown that both groups of drugs were discovered in Russia and are intended for local use to bleach bruises (blood stains) and dissolve thick mucus, sputum, pus, blood clots, meconium and other dense biological tissues containing the enzyme catalase. It is pointed out that the advantage and at the same time the limitation of the known drugs repurposed according to this scheme is their local application, since their new pharmacological activity is caused mainly by the physical and chemical principle of action, which is manifested by local interaction with the selected area of the patients organism.

Drug target of natural products and COVID-19: how far has science progressed?

The new coronavirus [severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)] that caused a viral disease with a high risk of mortality (coronavirus disease 2019) was found toward the end of 2019. This was a significant acute respiratory syndrome. In a brief period, this virus spread throughout the entire planet, causing tremendous loss of life and economic damage. The process of developing new treatments takes time, and there are presently no recognized specific treatments to treat this infection. The most promising participants, who subsequently developed into prospective leads, were dropped from the clinical research in their latter phases. Medication that has previously acquired permission may only be repurposed for use for various medical reasons following a thorough investigation for safety and effectiveness. Because there are now no effective treatments available, natural products are being used haphazardly as antiviral medications and immunity boosters. The fundamental statement that most natural compounds have powerful antiviral action does not apply to SARS-CoV-2. Middle East respiratory syndrome coronavirus and severe acute respiratory syndrome coronavirus infections are inhibited by natural treatments. According to an in silico study, the virus' nonstructural proteins, including PLpro, Mpro, and RdRp, as well as structural proteins like the spike (S) protein, have been shown to have a strong affinity for several natural products and to be inhibited by them. The virus also suggests that it is a valid candidate for therapeutic research since it utilizes the intracellular angiotensin-converting enzyme 2 receptor of the host cell. In this study, interesting targets for SARS-CoV-2 medication development are explored, as well as the antiviral properties of some well-known natural compounds.

Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins

We present a comparative study that evaluates the performance of a machine learning potential (ANI-2x), a conventional force field (GAFF), and an optimally tuned GAFF-like force field in the modeling of a set of 10 γ-fluorohydrins that exhibit a complex interplay between intra- and intermolecular interactions in determining conformer stability. To benchmark the performance of each molecular model, we evaluated their energetic, geometric, and sampling accuracies relative to quantum-mechanical data. This benchmark involved conformational analysis both in the gas phase and chloroform solution. We also assessed the performance of the aforementioned molecular models in estimating nuclear spin-spin coupling constants by comparing their predictions to experimental data available in chloroform. The results and discussion presented in this study demonstrate that ANI-2x tends to predict stronger-than-expected hydrogen bonding and overstabilize global minima and shows problems related to inadequate description of dispersion interactions. Furthermore, while ANI-2x is a viable model for modeling in the gas phase, conventional force fields still play an important role, especially for condensed-phase simulations. Overall, this study highlights the strengths and weaknesses of each model, providing guidelines for the use and future development of force fields and machine learning potentials.

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.

Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond

The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain. Effective AI/ML tools to derisk L2C and later phases of development are important to accelerate the pharmaceutical development process, ameliorate escalating development costs, and achieve greater success rates.

Ayurveda and in silico Approach: A Challenging Proficient Confluence for Better Development of Effective Traditional Medicine Spotlighting Network Pharmacology

Coalescence of traditional medicine Ayurveda and in silico technology is a rigor for supplementary development of future-ready effective traditional medicine. Ayurveda is a popular traditional medicine in South Asia, emanating worldwide for the treatment of metabolic disorders and chronic illness. Techniques of in silico biology are not much explored for the investigation of a variety of bioactive phytochemicals of Ayurvedic herbs. Drug repurposing, reverse pharmacology, and polypharmacology in Ayurveda are areas in silico explorations that are needed to understand the rich repertoire of herbs, minerals, herbo-minerals, and assorted Ayurvedic formulations. This review emphasizes exploring the concept of Ayurveda with in silico approaches and the need for Ayurinformatics studies. It also provides an overview of in silico studies done on phytoconstituents of some important Ayurvedic plants, the utility of in silico studies in Ayurvedic phytoconstituents/formulations, limitations/challenges, and prospects of in silico studies in Ayurveda. This article discusses the convergence of in silico work, especially in the least explored field of Ayurveda. The focused coalesce of these two domains could present a predictive combinatorial platform to enhance translational research magnitude. In nutshell, it could provide new insight into an Ayurvedic drug discovery involving an in silico approach that could not only alleviate the process of traditional medicine research but also enhance its effectiveness in addressing health care.

Strategies for targeting the P2Y12 receptor in the central nervous system

The purinergic 2Y type 12 receptor (P2Y₁₂R) is a well-known biological target for anti-thrombotic drugs due to its role in platelet aggregation and blood clotting. While the importance of the P2Y₁₂R in the periphery has been known for decades, much less is known about its expression and roles in the central nervous system (CNS), where it is expressed exclusively on microglia - the first responders to brain insults and neurodegeneration. Several seminal studies have shown that P2Y₁₂ is a robust, translatable biomarker for anti-inflammatory and neuroprotective microglial phenotypes in models of degenerative diseases such as multiple sclerosis and Alzheimer's disease. An enduring problem for studying this receptor in vivo, however, is the lack of selective, high-affinity small molecule ligands that can bypass the blood-brain barrier and accumulate in the CNS. In this Digest, we discuss previous attempts by researchers to target the P2Y₁₂R in the CNS and opine on strategies that may be employed to design and assess the suitability of novel P2Y₁₂ ligands for this purpose going forward.

A New Investigation into the Molecular Mechanism of Andrographolide towards Reducing Cytokine Storm

Cytokine storm is a condition in which the immune system produces an excessive number of inflammatory signals, which can result in organ failure and death. It is also known as cytokine release syndrome, CRS, or simply cytokine storm, and it has received a lot of attention recently because of the COVID-19 pandemic. It appears to be one of the reasons why some people experience life-threatening symptoms from COVID-19, a medical condition induced by SARS-CoV-2 infection. In situations where natural substances can be exploited as therapeutics to reduce cytokine storm, the drug development process has come to the rescue. In the present study, we tested the potentiality of Andrographolide, labdane diterpenoid targeting several key cytokines that are secreted as a result of cytokine storm. We used molecular docking analyses, molecular dynamics simulations, and pharmacokinetic properties to test the stability of the complexes. The compound’s binding energy with some cytokines was over −6.5 Kcal/mol. Furthermore, a post-molecular dynamics (MD) study revealed that Andrographolide was extremely stable with these cytokines. The compound’s pharmacokinetic measurements demonstrated excellent properties in terms of adsorption, distribution, metabolism, and excretion. Our research revealed that this compound may be effective in lowering cytokine storm and treating severe symptoms.

Identifying Possible AChE Inhibitors from Drug-like Molecules via Machine Learning and Experimental Studies

Acetylcholinesterase (AChE) is one of the most important drug targets for Alzheimer's disease (AD) treatment. In this work, a machine learning model was trained to rapidly and accurately screen large chemical databases for the potential inhibitors of AChE. The obtained results were then validated via in vitro enzyme assay. Moreover, atomistic simulations including molecular docking and molecular dynamics simulations were then used to understand molecular insights into the binding process of ligands to AChE. In particular, two compounds including benzyl trifluoromethyl ketone and trifluoromethylstyryl ketone were indicated as highly potent inhibitors of AChE because they established IC₅₀ values of 0.51 and 0.33 μM, respectively. The obtained IC₅₀ of two compounds is significantly lower than that of galantamine (2.10 μM). The predicted log(BB) suggests that the compounds may be able to traverse the blood-brain barrier. A good agreement between computational and experimental studies was observed, indicating that the hybrid approach can enhance AD therapy.

Computational methods to assist in the discovery of pharmacological chaperones for rare diseases

Pharmacological chaperones are chemical compounds able to bind proteins and stabilize them against denaturation and following degradation. Some pharmacological chaperones have been approved, or are under investigation, for the treatment of rare inborn errors of metabolism, caused by genetic mutations that often can destabilize the structure of the wild-type proteins expressed by that gene. Given that, for rare diseases, there is a general lack of pharmacological treatments, many expectations are poured out on this type of compounds. However, their discovery is not straightforward. In this review, we would like to focus on the computational methods that can assist and accelerate the search for these compounds, showing also examples in which these methods were successfully applied for the discovery of promising molecules belonging to this new category of pharmacologically active compounds.

Design, Synthesis, and Development of 4‐[(7‐Chloroquinoline‐4‐yl)amino]phenol as a Potential SARS‐CoV‐2 Mpro Inhibitor

A series of chloroquine analogs were designed to search for a less toxic chloroquine derivative as a potential SARS‐CoV‐2 Mpro inhibitor. Herein, an ANN‐based QSAR model was built to predict the IC₅₀ values of each analog using the experimental values of other 4‐aminoquinolines as the training set. Subsequently, molecular docking was used to evaluate each analog's binding affinity to Mpro. The analog that showed the greatest affinity and lowest IC₅₀ values was synthesized and characterized for its posterior incorporation into a polycaprolactone‐based nanoparticulate system. After characterizing the loaded nanoparticles, an in vitro drug release assay was carried out, and the cytotoxicity of the analog and loaded nanoparticles was evaluated using murine fibroblast (L929) and human lung adenocarcinoma (A549) cell lines. Results show that the synthesized analog is much less toxic than chloroquine and that the nanoparticulate system allowed for the prolonged release of the analog without evidence of adverse effects on the cell lines used; therefore, suggesting that the analog could be a potential therapeutic option for COVID‐19.

A Comprehensive Evaluation of Sdox, a Promising H2S-Releasing Doxorubicin for the Treatment of Chemoresistant Tumors

Sdox is a hydrogen sulfide (H₂S)-releasing doxorubicin effective in P-glycoprotein-overexpressing/doxorubicin-resistant tumor models and not cytotoxic, as the parental drug, in H9c2 cardiomyocytes. The aim of this study was the assessment of Sdox drug-like features and its absorption, distribution, metabolism, and excretion (ADME)/toxicity properties, by a multi- and transdisciplinary in silico, in vitro, and in vivo approach. Doxorubicin was used as the reference compound. The in silico profiling suggested that Sdox possesses higher lipophilicity and lower solubility compared to doxorubicin, and the off-targets prediction revealed relevant differences between Dox and Sdox towards several cancer targets, suggesting different toxicological profiles. In vitro data showed that Sdox is a substrate with lower affinity for P-glycoprotein, less hepatotoxic, and causes less oxidative damage than doxorubicin. Both anthracyclines inhibited CYP3A4, but not hERG currents. Unlike doxorubicin, the percentage of zebrafish live embryos at 72 hpf was not affected by Sdox treatment. In conclusion, these findings demonstrate that Sdox displays a more favorable drug-like ADME/toxicity profile than doxorubicin, different selectivity towards cancer targets, along with a greater preclinical efficacy in resistant tumors. Therefore, Sdox represents a prototype of innovative anthracyclines, worthy of further investigations in clinical settings.

Ligand-based G Protein Coupled Receptor pharmacophore modeling: Assessing the role of ligand function in model development

Integral membrane proteins in the G Protein-Coupled Receptor (GPCR) class are attractive drug development targets. However, computational methods applicable to ligand discovery for many GPCR targets are restricted by limited numbers of known ligands. Pharmacophore models can be developed using variously sized training sets and applied in database mining to prioritize candidate ligands for subsequent validation. This in silico study assessed the impact of key pharmacophore modeling decisions that arise when known ligand numbers for a target of interest are low. GPCR included in this study are the adrenergic alpha-1A, 1D and 2A, adrenergic beta 2 and 3, kappa, delta and mu opioid, serotonin 1A and 2A, and the muscarinic 1 and 2 receptors, all of which have rich ligand data sets suitable to assess the performance of protocols intended for application to GPCR with limited ligand data availability. Impact of ligand function, potency and structural diversity in training set selection was assessed to define when pharmacophore modeling targeting GPCR with limited known ligands becomes viable. Pharmacophore elements and pharmacophore model selection criteria were also assessed. Pharmacophore model assessment was based on percent pharmacophore model generation failure, as well as Güner-Henry enrichment and goodness-of-hit scores. Three of seven pharmacophore element schemes evaluated in MOE 2018.0101, Unified, PCHD, and CHD, showed substantially lower failure rates and higher enrichment scores than the others. Enrichment and GH scores were used to compare construction protocol for pharmacophore models of varying purposes- such as function specific versus nonspecific ligand identification. Notably, pharmacophore models constructed from ligands of mixed functions (agonists and antagonists) were capable of enriching hitlists with active compounds, and therefore can be used when available sets of known ligands are limited in number.

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

Systematic risk identification and assessment using a new risk map in pharmaceutical R&D

Delivering transformative therapies to patients while maintaining growth in the pharmaceutical industry requires an efficient use of research and development (R&D) resources and technologies to develop high-impact new molecular entities (NMEs). However, increasing global R&D competition in the pharmaceutical industry, growing impact of generics and biosimilars, more stringent regulatory requirements, as well as cost-constrained reimbursement frameworks challenge current business models of leading pharmaceutical companies. Big data-based analytics and artificial intelligence (AI) approaches have disrupted various industries and are having an increasing impact in the biopharmaceutical industry, with the promise to improve and accelerate biopharmaceutical R&D processes. Here, we systematically analyze, identify, assess, and categorize key risks across the drug discovery and development value chain using a new risk map approach, providing a comprehensive risk-reward analysis for pharmaceutical R&D.

Antimetastatic Potential of Quercetin Analogues with Improved Pharmacokinetic Profile: Pharmacoinformatic Preliminary Study

BACKGROUND

Urokinase-type plasminogen activator (uPA) system is a crucial pathway for tumor invasion and metastasis. Recently, multiple anticancer effects of quercetin have been described, including inhibitory activity against uPA. However, the clinical use of this flavonoid has been limited due to its low oral bioavailability.

OBJECTIVE

The objectives of the study were to assess the antimetastatic potential of quercetin analogues by analyzing their binding affinity for uPA and to select the compounds with improved pharmacological profiles.

METHODS

Binding affinities of structural analogues of quercetin to uPA receptor were determined by molecular docking analysis using Molegro Virtual Docker software, and molecular descriptors relevant for estimating pharmacological profile were calculated from ligand structures using computational models.

RESULTS

Among 44 quercetin analogues, only one quercetin analogue (3,6,2',4',5'-pentahydroxyflavone) was found to possess both higher aqueous solubility and membrane permeability, and a stronger affinity for uPA than quercetin, which makes it the potential lead compound for anticancer drug development. Like quercetin, this compound has five hydroxyl groups but is arranged differently, which contributes to the higher aqueous solubility and higher amphiphilic moment compared to quercetin. Since membrane permeability is not recognized as the limiting factor for quercetin absorption, analogues with higher aqueous solubility and retained or stronger uPA inhibitory activity should also be further experimentally validated for potential therapeutic use.

CONCLUSION

Identified quercetin analogues with better physicochemical and pharmacological properties have a high potential to succeed in later stages of research in biological systems as potential anticancer agents with antimetastatic activity.

A review on potential of natural products in the management of COVID-19

At the end of 2019, a life threatening viral infection (COVID-19) caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was reported. This virus has spread worldwide in a short duration and forced the world to face unprecedented life and economic loss. To date, there are no known specific drugs to combat this virus and the process for new drug development is lengthy. Most promising candidates, which emerged as potential leads, were abandoned in the later phases of clinical trials. Repurposing of already approved drugs for other therapeutic applications can be done only after extensive testing for safety and efficacy. With no definite therapeutics in the horizon, natural products are in extensive use arbitrarily as anti-viral agents and immune boosters. For ages it has been known that most natural products possess potent anti-viral activity and it is no different for SARS-CoV-2. It has been shown that natural products display inhibitory effects on MERS-CoV and SARS-CoV infections. In silico studies have shown that various natural products have strong binding affinity for and inhibitory action on the non-structural proteins of the virus, namely PL^PRO, M^PRO, and RdRp, and structural proteins such as spike (S) protein. Since the virus utilizes the transmembrane ACE2 receptor of the host cell, it also proves to be a valid target for drug development. In this review promising targets for drug development against SARS-CoV-2 and anti-viral activities of some of the known natural products are discussed.

In this review promising targets for drug development against SARS-CoV-2 and anti-viral activities of some of the known natural products (including plant secondary metabolites) are discussed.

A Multifunctional Peptide From Bacillus Fermented Soybean for Effective Inhibition of SARS-CoV-2 S1 Receptor Binding Domain and Modulation of Toll Like Receptor 4: A Molecular Docking Study

Fermented soybean products are traditionally consumed and popular in many Asian countries and the northeastern part of India. To search for potential agents for the interruption of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Spike glycoprotein 1 (S1) and human angiotensin-converting enzyme 2 (ACE2) receptor interactions, the in silico antiviral prospective of peptides identified from the proteome of kinema was investigated. Soybean was fermented using Bacillus licheniformis KN1G, Bacillus amyloliquefaciens KN2G and two different strains of Bacillus subtilis (KN2B and KN2M). The peptides were screened in silico for possible antiviral activity using two different web servers (AVPpred and meta-iAVP), and binding interactions of selected 44 peptides were further explored against the receptor-binding domain (RBD) of the S1 protein (PDB ID: 6M0J) by molecular docking using ZDOCK. The results showed that a peptide ALPEEVIQHTFNLKSQ (P13) belonging to B. licheniformis KN1G fermented kinema was able to make contacts with the binding motif of RBD by blocking specific residues designated as critical (GLN493, ASN501) in the binding of human angiotensin-converting enzyme 2 (ACE2) cell receptor. The selected peptide was also observed to have a significant affinity towards human toll like receptor 4 (TLR4)/Myeloid Differentiation factor 2 (MD2) (PDB ID: 3FXI) complex known for its essential role in cytokine storm. The energy properties of the docked complexes were analyzed through the Generalized Born model and Solvent Accessibility method (MM/GBSA) using HawkDock server. The results showed peptidyl amino acids GLU5, GLN8, PHE11, and LEU13 contributed most to P13-RBD binding. Similarly, ARG90, PHE121, LEU61, PHE126, and ILE94 were appeared to be significant in P13-TLR4/MD2 complex. The findings of the study suggest that the peptides from fermented soy prepared using B. licheniformis KN1G have better potential to be used as antiviral agents. The specific peptide ALPEEVIQHTFNLKSQ could be synthesized and used in combination with experimental studies to validate its effect on SARS-CoV-2-hACE2 interaction and modulation of TLR4 activity. Subsequently, the protein hydrolysate comprising these peptides could be used as prophylaxis against viral diseases, including COVID-19.

Bioinformatics resources facilitate understanding and harnessing clinical research of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) pandemic, caused by the coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has created an unprecedented threat to public health. The pandemic has been sweeping the globe, impacting more than 200 countries, with more outbreaks still lurking on the horizon. At the time of the writing, no approved drugs or vaccines are available to treat COVID-19 patients, prompting an urgent need to decipher mechanisms underlying the pathogenesis and develop curative treatments. To fight COVID-19, researchers around the world have provided specific tools and molecular information for SARS-CoV-2. These pieces of information can be integrated to aid computational investigations and facilitate clinical research. This paper reviews current knowledge, the current status of drug development and various resources for key steps toward effective treatment of COVID-19, including the phylogenetic characteristics, genomic conservation and interaction data. The final goal of this paper is to provide information that may be utilized in bioinformatics approaches and aid target prioritization and drug repurposing. Several SARS-CoV-2-related tools/databases were reviewed, and a web-portal named OverCOVID (http://bis.zju.edu.cn/overcovid/) is constructed to provide a detailed interpretation of SARS-CoV-2 basics and share a collection of resources that may contribute to therapeutic advances. These information could improve researchers' understanding of SARS-CoV-2 and help to accelerate the development of new antiviral treatments.

The application of isatin-based multicomponent-reactions in the quest for new bioactive and druglike molecules

Oxindole derivatives are known for their great interest in the field of Medicinal Chemistry, as they display vast biological activities. Recent efforts concerning the preparation of oxindole derivatives using isatin-based multicomponent reactions (MCRs) constitute a great advance in generating druglike libraries fast and with wide scaffold diversity. In this review, we address those recent developments, exploring the synthetic pathways and biological activities described for these compounds, namely antitumor, antibacterial, antifungal, antiparasitic, antiviral, antioxidant, anti-inflammatory and central nervous system (CNS) pathologies. To add new depth to this work, we used a well-established web-based free tool (SwissADME) to evaluate the most promising scaffolds in what concerns their druglike properties, namely by evaluating their compliance with some of the most valuable rules applied by medicinal chemists in both academia and industrial settings (Lipinski, Ghose, Veber, Egan, Muegge). The aim of this review is to endorse isatin-based MCRs as a valuable synthetic approach to attain new hit compounds bearing the oxindole privileged structure, while critically exploring these scaffolds' druglike properties.

Identification of Small Molecule Inhibitors of the Deubiquitinating Activity of the SARS-CoV-2 Papain-Like Protease: in silico Molecular Docking Studies and in vitro Enzymatic Activity Assay

COVID-19 is an ongoing pandemic caused by the SARS-CoV-2 virus with important political, socio-economic, and public health consequences. Inhibiting replication represents an important antiviral approach, and in this context two viral proteases, the SARS-CoV-2 main and papain-like proteases (PLpro), which cleave pp1a and pp1ab polypeptides, are critical. Along with protease activity, the PLpro possesses deubiquitinating activity, which is important in immune regulation. Naphthalene-based inhibitors, such as the well-investigated GRL-0617 compound, have been shown to possess dual effects, inhibiting both protease and deubiquitinating activity of the PLpro. Rather than binding to the canonical catalytic triad, these type of non-covalent inhibitors target an adjacent pocket, the naphthalene-inhibitor binding site. Using a high-throughput screen, we have previously identified the dietary hypericin, rutin, and cyanidin-3-O-glucoside compounds as potential protease inhibitors targeting the naphthalene-inhibitor binding site. Here, our aim was to investigate the binding characteristics of these compounds to the PLpro, and to evaluate deubiquitinating activity, by analyzing seven different PLpro crystal structures. Molecular docking highlighted the relatively high affinity of GRL-0617 and dietary compounds. In contrast binding of the small molecules was abolished in the presence of ubiquitin in the palm subdomain of the PLpro. Further, docking the small molecules in the naphthalene-inhibitor binding site, followed by protein-protein docking revealed displacement of ubiquitin in a conformation inconsistent with functional activity. Finally, the deubiquitinating activity was validated in vitro using an enzymatic activity assay. The findings indicated that the dietary compounds inhibited deubiquitinase activity in the micromolar range with an order of activity of GRL-0167, hypericin >> rutin, cyanidin-3-O-glucoside > epigallocatechin gallate, epicatechin gallate, and cefotaxime. Our findings are in accordance with mechanisms and potential antiviral effects of the naphthalene-based, GRL-0617 inhibitor, which is currently progressing in preclinical trials. Further, our findings indicate that in particular hypericin, rutin, and cyanidin-3-O-glucoside, represent suitable candidates for subsequent evaluation as PLpro inhibitors.

FDA efficiency for approval process of COVID-19 therapeutics

Coronavirus disease 19 (COVID-19) is an infection caused by the novel Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The pandemic spread of SARS-CoV-2 has resulted in significant health, economic, and social ramifications. There are no U.S. Food and Drug Administration (FDA)-approved prophylactic or therapeutic treatment options for COVID-19. This puts unprecedented product development pressure on the medical science community to define treatment options. Additionally, in the United States of American (USA) further regulatory and quality assurance pressures impact the FDA. The regulatory therapeutic development process is complex as it relates to product mechanism, toxicity profile, and level of efficacy. The advert of a worldwide pandemic however, advanced efficiencies within many of the regulatory agencies worldwide in order to facilitate COVID-19 treatment option development within the USA. Clinical drug development pathways can include several established approaches: investigational new drug (IND), expanded access IND, emergency IND, treatment IND, and emergency use authorization (EUA). Remdesivir, an investigational drug, and hydroxyloroquine, an FDA-approved drug for autoimmune diseases, were the two early potential therapies. This review article examines the expedited FDA review process for remdesivir and hydroxychloroquine, and analyzes data and results from early clinical studies of both drugs.

Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations

Background:

The inclusion of direct effects mediated by water during the ligandreceptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions.

Objective:

In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered.

Results:

Here, we analyse the software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules.

Conclusions:

Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon.