Reverse docking: a powerful tool for drug repositioning and drug rescue

Drug repurposing for glomerular diseases: an underutilized resource

Drug repurposing in glomerular disease can deliver opportunities for steroid-free regimens, enable personalized multi-target options for resistant or relapsing disease and enhance treatment options for understudied populations (for example, children) and in resource-limited settings. Identification of drug-repurposing candidates can be data driven, which utilizes existing data on disease pathobiology, drug features and clinical outcomes, or experimental, which involves high-throughput drug screens. Information from databases of approved drugs, clinical trials and PubMed registries suggests that at least 96 drugs on the market cover 49 targets with immunosuppressive potential that could be candidates for drug repurposing in glomerular disease. Furthermore, evidence to support drug repurposing is available for 191 immune drug target-glomerular disease pairs. Non-immunological drug repurposing includes strategies to reduce haemodynamic overload, podocyte injury and kidney fibrosis. Recommended strategies to expand drug-repurposing capacity in glomerular disease include enriching drug databases with glomeruli-specific information, enhancing the accessibility of primary clinical trial data, biomarker discovery to improve participant selection into clinical trials and improve surrogate outcomes and initiatives to reduce patent, regulatory and organizational hurdles.

Epigenetic target identification strategy based on multi-feature learning

The identification of potential epigenetic targets for a known bioactive compound is essential and promising as more and more epigenetic drugs are used in cancer clinical treatment and the availability of chemogenomic data related to epigenetics increases. In this study, we introduce a novel epigenetic target identification strategy (ETI-Strategy) that integrates a multi-task graph convolutional neural network prior model and a protein-ligand interaction classification discriminating model using large-scale bioactivity data for a panel of 55 epigenetic targets. Our approach utilizes machine learning techniques to achieve an AUC value of 0.934 for the prior model and 0.830 for the discriminating model, outperforming inverse docking in predicting protein-ligand interactions. When comparing with other open-source target identification tools, it was found that only our tool was able to accurately predict all the targets corresponding to each compound. This further demonstrates the ability of our strategy to take full advantage of molecular-level information as well as protein-level information in molecular activity prediction. Our work highlights the contribution of machine learning in the identification of potential epigenetic targets and offers a novel approach for epigenetic drug discovery and development.Communicated by Ramaswamy H. Sarma.

A consensus reverse docking approach for identification of a competitive inhibitor of acetyltransferase enhanced intracellular survival protein from Mycobacterium tuberculosis

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb), which remains a significant global health challenge. The emergence of multidrug-resistant (MDR) Mtb strains imposes the development of new therapeutic strategies. This study focuses on the identification and evaluation of potential inhibitors against Mtb H37Ra through a comprehensive screening of an in-house chemolibrary. Subsequently, a promising pyrimidine derivative (LQM495) was identified as promising and then further investigated by experimental and in silico approaches. In this context, computational techniques were used to elucidate the potential molecular target underlying the inhibitory action of LQM495. Then, a consensus reverse docking (CRD) protocol was used to investigate the interactions between this compound and several Mtb targets. Out of 98 Mtb targets investigated, the enhanced intracellular survival (Eis) protein emerged as a target for LQM495. To gain insights into the stability of the LQM495-Eis complex, molecular dynamics (MD) simulations were conducted over a 400 ns trajectory. Further insights into its binding modes within the Eis binding site were obtained through a Quantum mechanics (QM) approach, using density functional theory (DFT), with B3LYP/D3 basis set. These calculations shed light on the electronic properties and reactivity of LQM495. Subsequently, inhibition assays and kinetic studies of the Eis activity were used to investigate the activity of LQM495. Then, an IC50 value of 11.0 ± 1.4 µM was found for LQM495 upon Eis protein. Additionally, its Vmax, Km, and Ki parameters indicated that it is a competitive inhibitor. Lastly, this study presents LQM495 as a promising inhibitor of Mtb Eis protein, which could be further explored for developing novel anti-TB drugs in the future.

Exploring the potential of drug repurposing for treating depression

Researchers are interested in drug repurposing or drug repositioning of existing pharmaceuticals because of rising costs and slower rates of new medication development. Other investigations that authorized these treatments used data from experimental research and off-label drug use. More research into the causes of depression could lead to more effective pharmaceutical repurposing efforts. In addition to the loss of neurotransmitters like serotonin and adrenaline, inflammation, inadequate blood flow, and neurotoxins are now thought to be plausible mechanisms. Because of these other mechanisms, repurposing drugs has resulted for treatment-resistant depression. This chapter focuses on therapeutic alternatives and their effectiveness in drug repositioning. Atypical antipsychotics, central nervous system stimulants, and neurotransmitter antagonists have investigated for possible repurposing. Nonetheless, extensive research is required to ensure their formulation, effectiveness, and regulatory compliance.

Synergizing drug repurposing and target identification for neurodegenerative diseases

Despite dedicated research efforts, the absence of disease-curing remedies for neurodegenerative diseases (NDDs) continues to jeopardize human society and stands as a challenge. Drug repurposing is an attempt to find new functionality of existing drugs and take it as an opportunity to discourse the clinically unmet need to treat neurodegeneration. However, despite applying this approach to rediscover a drug, it can also be used to identify the target on which a drug could work. The primary objective of target identification is to unravel all the possibilities of detecting a new drug or repurposing an existing drug. Lately, scientists and researchers have been focusing on specific genes, a particular site in DNA, a protein, or a molecule that might be involved in the pathogenesis of the disease. However, the new era discusses directing the signaling mechanism involved in the disease progression, where receptors, ion channels, enzymes, and other carrier molecules play a huge role. This review aims to highlight how target identification can expedite the whole process of drug repurposing. Here, we first spot various target-identification methods and drug-repositioning studies, including drug-target and structure-based identification studies. Moreover, we emphasize various drug repurposing approaches in NDDs, namely, experimental-based, mechanism-based, and in silico approaches. Later, we draw attention to validation techniques and stress on drugs that are currently undergoing clinical trials in NDDs. Lastly, we underscore the future perspective of synergizing drug repurposing and target identification in NDDs and present an unresolved question to address the issue.

Structure-Based Identification of Organoruthenium Compounds as Nanomolar Antagonists of Cannabinoid Receptors

Metal complexes exhibit a diverse range of coordination geometries, representing novel privileged scaffolds with convenient click types of preparation inaccessible for typical carbon-centered organic compounds. Herein, we explored the opportunity to identify biologically active organometallic complexes by reverse docking of a rigid, minimum-size octahedral organoruthenium scaffold against thousands of protein-binding pockets. Interestingly, cannabinoid receptor type 1 (CB1) was identified based on the docking scores and the degree of overlap between the docked organoruthenium scaffold and the hydrophobic scaffold of the cocrystallized ligand. Further structure-based optimization led to the discovery of organoruthenium complexes with nanomolar binding affinities and high selectivity toward CB2. Our work indicates that octahedral organoruthenium scaffolds may be advantageous for targeting the large and hydrophobic binding pockets and that the reverse docking approach may facilitate the discovery of novel privileged scaffolds, such as organometallic complexes, for exploring chemical space in lead discovery.

Schizandrin A induces non-small cell lung cancer apoptosis by suppressing the epidermal growth factor receptor activation

OBJECTIVE

The purpose of this study is to explore the biological mechanism of Schizandrin A (SchA) inducing non-small cell lung cancer (NSCLC) apoptosis.

METHODS

The reverse molecular docking tool "Swiss Target Prediction" was used to predict the targets of SchA. Protein-protein interaction analysis was performed on potential targets using the String database. Functional enrichment analyses of potential targets were performed with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. The conformation of SchA binding to target was simulated by chemical-protein interactomics and molecular docking. The effect of SchA on the expression and phosphorylation level of EGFR was detected by Western blot. Lipofectamine 3000 and EGFR plasmids were used to overexpress EGFR. Apoptosis was tested with Annexin V-FITC and propidium iodide staining, and cell cycle was detected by propidium iodide staining.

RESULTS

The "Swiss Target Prediction" database predicted 112 and 111 targets based on the 2D and 3D structures of SchA, respectively, of which kinases accounted for the most, accounting for 24%. Protein interaction network analyses showed that molecular targets such as ERBB family and SRC were at the center of the network. Functional enrichment analyses indicated that ERBB-related signaling pathways were enriched. Compound-protein interactomics and molecular docking revealed that SchA could bind to the ATP-active pocket of the EGFR tyrosine kinase domain. Laboratory results showed that SchA inhibited the phosphorylation of EGFR. Insulin could counteract the cytotoxic effect of SchA. EGFR overexpression and excess EGF or IGF-1 had limited impacts on the cytotoxicity of SchA.

CONCLUSIONS

Network pharmacology analyses suggested that ERBB family members may be the targets of SchA. SchA can inhibit NSCLC at least in part by inhibiting EGFR phosphorylation, and activating the EGFR bypass can neutralize the cytotoxicity of SchA.

Applying molecular docking to pesticides

Pesticide creation is related to the development of sustainable agricultural and ecological safety, and molecular docking technology can effectively help in pesticide innovation. This paper introduces the basic theory behind molecular docking, pesticide databases, and docking software. It also summarizes the application of molecular docking in the pesticide field, including the virtual screening of lead compounds, detection of pesticides and their metabolites in the environment, reverse screening of pesticide targets, and the study of resistance mechanisms. Finally, problems with the use of molecular docking technology in pesticide creation are discussed, and prospects for the future use of molecular docking technology in new pesticide development are discussed. © 2023 Society of Chemical Industry.

A universal programmable Gaussian boson sampler for drug discovery

Gaussian boson sampling (GBS) has the potential to solve complex graph problems, such as clique finding, which is relevant to drug discovery tasks. However, realizing the full benefits of quantum enhancements requires large-scale quantum hardware with universal programmability. Here we have developed a time-bin-encoded GBS photonic quantum processor that is universal, programmable and software-scalable. Our processor features freely adjustable squeezing parameters and can implement arbitrary unitary operations with a programmable interferometer. Leveraging our processor, we successfully executed clique finding on a 32-node graph, achieving approximately twice the success probability compared to classical sampling. As proof of concept, we implemented a versatile quantum drug discovery platform using this GBS processor, enabling molecular docking and RNA-folding prediction tasks. Our work achieves GBS circuitry with its universal and programmable architecture, advancing GBS toward use in real-world applications.

More simple, efficient and accurate food research promoted by intermolecular interaction approaches: A review

The investigation of intermolecular interactions has become increasingly important in many studies, mainly by combining different analytical approaches to reveal the molecular mechanisms behind specific experimental phenomena. From spectroscopic analysis to sophisticated molecular simulation techniques like molecular docking, molecular dynamics (MD) simulation, and quantum chemical calculations (QCC), the mechanisms of intermolecular interactions are gradually being characterized more clearly and accurately, leading to revolutionary advances. This article aims to review the progression in the main techniques involving intermolecular interactions in food research and the corresponding experimental results. Finally, we discuss the significant impact that cutting-edge molecular simulation technologies may have on the future of conducting deeper exploration. Applications of molecular simulation technology may revolutionize the food research, making it possible to design new future foods with precise nutrition and desired properties.

Drug repurposing-a promising approach for patients with angina but non-obstructive coronary artery disease (ANOCA)

In today's era of individualized precision medicine drug repurposing represents a promising approach to offer patients fast access to novel treatments. Apart from drug repurposing in cancer treatments, cardiovascular pharmacology is another attractive field for this approach. Patients with angina pectoris without obstructive coronary artery disease (ANOCA) report refractory angina despite standard medications in up to 40% of cases. Drug repurposing also appears to be an auspicious option for this indication. From a pathophysiological point of view ANOCA patients frequently suffer from vasomotor disorders such as coronary spasm and/or impaired microvascular vasodilatation. Consequently, we carefully screened the literature and identified two potential therapeutic targets: the blockade of the endothelin-1 (ET-1) receptor and the stimulation of soluble guanylate cyclase (sGC). Genetically increased endothelin expression results in elevated levels of ET-1, justifying ET-1 receptor blockers as drug candidates to treat coronary spasm. sGC stimulators may be beneficial as they stimulate the NO-sGC-cGMP pathway leading to GMP-mediated vasodilatation.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Anti-inflammatory effect and antihepatoma mechanism of carrimycin

BACKGROUND

New drugs are urgently needed for the treatment of liver cancer, a feat that could be feasibly accomplished by finding new therapeutic purposes for marketed drugs to save time and costs. As a new class of national anti-infective drugs, carrimycin (CAM) has strong activity against gram-positive bacteria and no cross resistance with similar drugs. Studies have shown that the components of CAM have anticancer effects.

AIM

To obtain a deeper understanding of CAM, its distribution, metabolism and anti-inflammatory effects were assessed in the organs of mice, and its mechanism of action against liver cancer was predicted by a network pharmacology method.

METHODS

In this paper, the content of isovaleryl spiramycin III was used as an index to assess the distribution and metabolism of CAM and its effect on inflammatory factors in various mouse tissues and organs. Reverse molecular docking technology was utilized to determine the target of CAM, identify each target protein based on disease type, and establish a target protein-disease type network to ascertain the effect of CAM in liver cancer. Then, the key action targets of CAM in liver cancer were screened by a network pharmacology method, and the core targets were verified by molecular docking and visual analyses.

RESULTS

The maximum CAM concentration was reached in the liver, kidney, lung and spleen 2.5 h after intragastric administration. In the intestine, the maximum drug concentration was reached 0.5 h after administration. In addition, CAM significantly reduced the interleukin-4 (IL-4) levels in the lung and kidney and especially the liver and spleen; moreover, CAM significantly reduced the IL-1β levels in the spleen, liver, and kidney and particularly the small intestine and lung. CAM is predicted to regulate related pathways by acting on many targets, such as albumin, estrogen receptor 1, epidermal growth factor receptor and caspase 3, to treat cancer, inflammation and other diseases.

CONCLUSION

We determined that CAM inhibited inflammation. We also predicted the complex multitargeted effects of CAM that involve multiple pathways and the diversity of these effects in the treatment of liver cancer, which provides a basis and direction for further clinical research.

Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility

Protein-ligand docking is an essential tool in structure-based drug design with applications ranging from virtual high-throughput screening to pose prediction for lead optimization. Most docking programs for pose prediction are optimized for redocking to an existing cocrystallized protein structure, ignoring protein flexibility. In real-world drug design applications, however, protein flexibility is an essential feature of the ligand-binding process. Flexible protein-ligand docking still remains a significant challenge to computational drug design. To target this challenge, we present a deep learning (DL) model for flexible protein-ligand docking based on the prediction of an intermolecular Euclidean distance matrix (EDM), making the typical use of iterative search algorithms obsolete. The model was trained on a large-scale data set of protein-ligand complexes and evaluated on independent test sets. Our model generates high quality poses for a diverse set of protein and ligand structures and outperforms comparable docking methods.

Anxiolytic and anticonvulsant effect of Ibuprofen derivative through GABAergic neuromodulation in adult Zebrafish

Anxiety and epilepsy affect millions of people worldwide, and the treatment of these pathologies involves the use of Benzodiazepines, drugs that have serious adverse effects such as dependence and sedation, so the discovery of new anxiolytic and antiepileptic drugs are necessary. Many routes for synthesizing ibuprofen derivatives have been developed, and these derivatives have shown promising pharmacological effects. Therefore, this study aims to evaluate its anxiolytic and anticonvulsant effect against the adult Zebrafish animal model of Ibuprofen (IBUACT) and its interaction with the GABAergic receptor through in silico studies. The light/dark preference test (Scototaxis test) was used to evaluate the anxiolytic behavior of adult Zebrafish acutely treated with IBUACT and Diazepam, and their anticonvulsant effects were investigated through the pentylenetetrazol (PTZ)-induced seizure model. Animals treated with IBUACT showed anxiolytic behavior similar to Diazepam, and pretreatment with flumazenil reversed this behavior. PTZ-induced seizures were delayed by IBUACT in all three stages and were shown to bind strongly in the Diazepam region of GABAA. In addition, this work presents evidence of new pharmacological applications of ibuprofen derivative in pathologies of the central nervous system (CNS), opening the horizon for new studies.Communicated by Ramaswamy H. Sarma.

Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery

Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein‐ligand interactions between 296 proteins spanning Escherichia coli's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning‐based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true‐positive rate to false‐positive rate. This work indicates that advances in modeling protein‐ligand interactions, particularly using machine learning‐based approaches, are needed to better harness AlphaFold2 for drug discovery.

Ganoderic acid D prevents oxidative stress-induced senescence by targeting 14-3-3ε to activate CaM/CaMKII/NRF2 signaling pathway in mesenchymal stem cells

Stem cell senescence is an important cause of aging. Delaying senescence may present a novel way to combat aging and age-associated diseases. This study provided a mechanistic insight into the protective effect of ganoderic acid D (GA-D) against human amniotic mesenchymal stem cell (hAMSCs) senescence. GA-D, a Ganoderma lucidum-derived triterpenoid, markedly prevented hAMSCs senescence via activating the Ca²⁺ calmodulin (CaM)/CaM-dependent protein kinase II (CaMKII)/nuclear erythroid 2-related factor 2 (Nrf2) axis, and 14-3-3ε was identified as a target of GA-D. 14-3-3ε-encoding gene (YWHAE) knockdown in hAMSCs reversed the activation of the CaM/CaMKII/Nrf2 signals to attenuate the GA-D anti-aging effect and increase senescence-associated β-galactosidase (SA-β-gal), p16 and p21 expression levels, including reactive oxygen species (ROS) production, thereby promoting cell cycle arrest and decreasing differentiation potential. YWHAE overexpression maintained or slightly enhanced the GA-D anti-aging effect. GA-D prevented d-galactose-caused aging in mice by significantly increasing the total antioxidant capacity, as well as superoxide dismutase and glutathione peroxidase activity, and reducing the formation of malondialdehyde, advanced glycation end products, and receptor of advanced glycation end products. Consistent with the protective mechanism of GA-D against hAMSCs senescence, GA-D delayed the senescence of bone-marrow mesenchymal stem cells in this aging model in vivo, reduced SA-β-gal and ROS production, alleviated cell cycle arrest, and enhanced cell viability and differentiation via regulating 14-3-3ε and CaM/CaMKII/Nrf2 axis. Therefore, GA-D retards hAMSCs senescence by targeting 14-3-3ε to activate the CaM/CaMKII/Nrf2 signaling pathway. Furthermore, the in vivo GA-D anti-aging effect may involve the regulation of stem cell senescence via the same signal axis.

Quercetin encapsulated in Folic Acid-Modified Liposomes is therapeutic against osteosarcoma by non-Covalent binding to the JH2 Domain of JAK2 Via the JAK2-STAT3-PDL1

Osteosarcoma (OS) is a malignant solid tumor prone to lung metastasis that occurs in adolescents aged 15-19 years. Neoadjuvant chemotherapy and surgical treatment aimed at curing OS have gained limited progress over the last 30 years. Exploring new effective second-line therapies for OS patients is a serious challenge for researchers. Quercetin, a multiple biologically active polyphenolic flavonoid, has been used in tumor therapy. However, the exact mechanism of quercetin is still unknown, which limits the application of quercetin. In the current study, we found that quercetin could inhibit JAK2 through the JH2 domain in a non-covalent manner, resulting in the inhibition of OS proliferation and immune escape via the JAK2-STAT3-PD-L1 signaling axis. More importantly, to overcome the shortcomings of quercetin, including low water solubility and low oral availability, we encapsulated it with folic acid-modified liposomes. The transportation of quercetin by folic acid-modified liposomes may provide a feasible strategy to cure OS.

Inverse Molecular Docking Elucidating the Anticarcinogenic Potential of the Hop Natural Product Xanthohumol and Its Metabolites

Natural products from plants exert a promising potential to act as antioxidants, antimicrobials, anti-inflammatory, and anticarcinogenic agents. Xanthohumol, a natural compound from hops, is indeed known for its anticarcinogenic properties. Xanthohumol is converted into three metabolites: isoxanthohumol (non-enzymatically) as well as 8- and 6-prenylnaringenin (enzymatically). An inverse molecular docking approach was applied to xanthohumol and its three metabolites to discern their potential protein targets. The aim of our study was to disclose the potential protein targets of xanthohumol and its metabolites in order to expound on the potential anticarcinogenic mechanisms of xanthohumol based on the found target proteins. The investigated compounds were docked into the predicted binding sites of all human protein structures from the Protein Data Bank, and the best docking poses were examined. Top scoring human protein targets with successfully docked compounds were identified, and their experimental connection with the anticarcinogenic function or cancer was investigated. The obtained results were carefully checked against the existing experimental findings from the scientific literature as well as further validated using retrospective metrics. More than half of the human protein targets of xanthohumol with the highest docking scores have already been connected with the anticarcinogenic function, and four of them (including two important representatives of the matrix metalloproteinase family, MMP-2 and MMP-9) also have a known experimental correlation with xanthohumol. Another important protein target is acyl-protein thioesterase 2, to which xanthohumol, isoxanthohumol, and 6-prenylnaringenin were successfully docked with the lowest docking scores. Moreover, the results for the metabolites show that their most promising protein targets are connected with the anticarcinogenic function as well. We firmly believe that our study can help to elucidate the anticarcinogenic mechanisms of xanthohumol and its metabolites as after consumption, all four compounds can be simultaneously present in the organism.

Bioactivity-Guided Separation of Anti-Cholinesterase Alkaloids from Uncaria rhynchophlly (Miq.) Miq. Ex Havil Based on HSCCC Coupled with Molecular Docking

As an important source of cholinesterase inhibitors, alkaloids in natural products have high potential value in terms of exerting pharmacological activities. In this study, a strategy for targeted preparation of cholinesterase inhibitors in Uncaria rhynchophlly (Miq.) Miq. ex Havil (UR) by high-speed counter-current chromatography was provided. In the method, a two-phase polar solvent system composed of ethyl acetate/n-butanol/water (1:4:5, v/v/v) was used, which isolated five alkaloids from the UR extract for the first time. All alkaloids were identified by HR-ESI-MS and NMR as 7-epi-javaniside (1), vincosamide (2), strictosamide (3), cadambine (4), and 3α-dihydrocadambine (5). The poorly resolved compounds 2 and 3 were separated by preparative HPLC (prep-HPLC). Among them, compounds 1, 4, and 5 were firstly obtained from UR. The purity of these plant isolates was 98.8%, 98.7%, 99.2%, 95.7%, and 98.5%, respectively. Compounds 1–5 exhibited an inhibitory effect on acetyl-cholinesterase and butyryl-cholinesterase with an IC₅₀ from 1.47 to 23.24 µg/mL and 1.01 to 18.24 µg/mL. Molecular docking and inhibitory activities indicated that compound 1 showed stronger inhibitory activity on acetyl-cholinesterase and butyryl-cholinesterase.

Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term

Scoring functions are important components in molecular docking for structure-based drug discovery. Traditional scoring functions, generally empirical- or force field-based, are robust and have proven to be useful for identifying hits and lead optimizations. Although multiple highly accurate deep learning- or machine learning-based scoring functions have been developed, their direct applications for docking and screening are limited. We describe a novel strategy to develop a reliable protein–ligand scoring function by augmenting the traditional scoring function Vina score using a correction term (OnionNet-SFCT). The correction term is developed based on an AdaBoost random forest model, utilizing multiple layers of contacts formed between protein residues and ligand atoms. In addition to the Vina score, the model considerably enhances the AutoDock Vina prediction abilities for docking and screening tasks based on different benchmarks (such as cross-docking dataset, CASF-2016, DUD-E and DUD-AD). Furthermore, our model could be combined with multiple docking applications to increase pose selection accuracies and screening abilities, indicating its wide usage for structure-based drug discoveries. Furthermore, in a reverse practice, the combined scoring strategy successfully identified multiple known receptors of a plant hormone. To summarize, the results show that the combination of data-driven model (OnionNet-SFCT) and empirical scoring function (Vina score) is a good scoring strategy that could be useful for structure-based drug discoveries and potentially target fishing in future.

Repurposing of Drugs for SARS-CoV-2 Using Inverse Docking Fingerprints

Severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 is a virus that belongs to the Coronaviridae family. This group of viruses commonly causes colds but possesses a tremendous pathogenic potential. In humans, an outbreak of SARS caused by the SARS-CoV virus was first reported in 2003, followed by 2012 when the Middle East respiratory syndrome coronavirus (MERS-CoV) led to an outbreak of Middle East respiratory syndrome (MERS). Moreover, COVID-19 represents a serious socioeconomic and global health problem that has already claimed more than four million lives. To date, there are only a handful of therapeutic options to combat this disease, and only a single direct-acting antiviral, the conditionally approved remdesivir. Since there is an urgent need for active drugs against SARS-CoV-2, the strategy of drug repurposing represents one of the fastest ways to achieve this goal. An in silico drug repurposing study using two methods was conducted. A structure-based virtual screening of the FDA-approved drug database on SARS-CoV-2 main protease was performed, and the 11 highest-scoring compounds with known 3CLpro activity were identified while the methodology was used to report further 11 potential and completely novel 3CLpro inhibitors. Then, inverse molecular docking was performed on the entire viral protein database as well as on the Coronaviridae family protein subset to examine the hit compounds in detail. Instead of target fishing, inverse docking fingerprints were generated for each hit compound as well as for the five most frequently reported and direct-acting repurposed drugs that served as controls. In this way, the target-hitting space was examined and compared and we can support the further biological evaluation of all 11 newly reported hits on SARS-CoV-2 3CLpro as well as recommend further in-depth studies on antihelminthic class member compounds. The authors acknowledge the general usefulness of this approach for a full-fledged inverse docking fingerprint screening in the future.

Current Role of Nanotechnology Used in Food Processing Industry to Control Food Additives and Exploring Their Biochemical Mechanisms

BACKGROUND

With the advent of food additives centuries ago, the human race has found ways to improve and maintain the safety of utility, augment the taste, color, texture, nutritional value, and appearance of the food. Since the 19th century, when the science behind food spoilage was discerned, the use of food additives in food preservation has been increasing worldwide and at a fast pace to get along with modern lifestyles. Although food additives are thought to be used to benefit the food market, some of them are found to be associated with several health issues at an alarming rate. Studies are still going on regarding the mechanisms by which food additives affect public health. Therefore, an attempt has been made to find out the remedies by exploiting technologies that may convey new properties of food additives that can only enhance the quality of food without having any systemic side effects. Thus, this review focuses on the applications of nanotechnology in the production of nano-food additives and evaluates its success regarding reduction in the health-related hazards collaterally maintaining the food nutrient value.

METHODOLOGY

A thorough literature study was performed using scientific databases like PubMed, Science Direct, Scopus, Web of Science for determining the design of the study, and each article was checked for citation and referred to formulate the present review article.

CONCLUSION

Nanotechnology can be applied in the food processing industry to control the unregulated use of food additives and to intervene in the biochemical mechanisms at a cellular and physiological level for the ensuring safety of food products. The prospective of nano-additive of chemical origin could be useful to reduce risks of hazards related to human health that are caused majorly due to the invasion of food contaminants (either intentional or non-intentional) into food, though this area still needs scientific validation. Therefore, this review provides comprehensive knowledge on different facets of food contaminants and also serves as a platform of ideas for encountering health risk problems about the design of improved versions of nano-additives.

Target identification for repurposed drugs active against SARS-CoV-2 via high-throughput inverse docking

Screening already approved drugs for activity against a novel pathogen can be an important part of global rapid-response strategies in pandemics. Such high-throughput repurposing screens have already identified several existing drugs with potential to combat SARS-CoV-2. However, moving these hits forward for possible development into drugs specifically against this pathogen requires unambiguous identification of their corresponding targets, something the high-throughput screens are not typically designed to reveal. We present here a new computational inverse-docking protocol that uses all-atom protein structures and a combination of docking methods to rank-order targets for each of several existing drugs for which a plurality of recent high-throughput screens detected anti-SARS-CoV-2 activity. We demonstrate validation of this method with known drug-target pairs, including both non-antiviral and antiviral compounds. We subjected 152 distinct drugs potentially suitable for repurposing to the inverse docking procedure. The most common preferential targets were the human enzymes TMPRSS2 and PIKfyve, followed by the viral enzymes Helicase and PLpro. All compounds that selected TMPRSS2 are known serine protease inhibitors, and those that selected PIKfyve are known tyrosine kinase inhibitors. Detailed structural analysis of the docking poses revealed important insights into why these selections arose, and could potentially lead to more rational design of new drugs against these targets.

Application of Reverse Docking in the Research of Small Molecule Drugs and Traditional Chinese Medicine

With the development of structural biology and data mining, computer-aided drug design (CADD) has been playing an important role in all aspects of new drug development. Reverse docking, a method of virtual screening based on molecular docking in CADD, is widely used in drug repositioning, drug rescue, and traditional Chinese medicine (TCM) research, for it can search for macromolecular targets that can bind to a given ligand molecule. This review revealed the principle of reverse docking, summarized common target protein databases and docking procedures, and enumerated the applications of reverse docking in drug repositioning, adverse drug reactions, traditional Chinese medicine, and COVID-19 treatment. Hope our work can give some inspiration to researchers engaged in drug development.

A data-driven methodology towards evaluating the potential of drug repurposing hypotheses

Drug repurposing has become a widely used strategy to accelerate the process of finding treatments. While classical de novo drug development involves high costs, risks, and time-consuming paths, drug repurposing allows to reuse already-existing and approved drugs for new indications. Numerous research has been carried out in this field, both in vitro and in silico. Computational drug repurposing methods make use of modern heterogeneous biomedical data to identify and prioritize new indications for old drugs. In the current paper, we present a new complete methodology to evaluate new potentially repurposable drugs based on disease-gene and disease-phenotype associations, identifying significant differences between repurposing and non-repurposing data. We have collected a set of known successful drug repurposing case studies from the literature and we have analysed their dissimilarities with other biomedical data not necessarily participating in repurposing processes. The information used has been obtained from the DISNET platform. We have performed three analyses (at the genetical, phenotypical, and categorization levels), to conclude that there is a statistically significant difference between actual repurposing-related information and non-repurposing data. The insights obtained could be relevant when suggesting new potential drug repurposing hypotheses.

Nanoparticles Loaded with a New Thiourea Derivative: Development and In vitro Evaluation Against Leishmania amazonensis

BACKGROUND

Leishmaniasis is a neglected tropical disease caused by protozoa of the genus Leishmania. Current treatments are restricted to a small number of drugs that display both severe side effects and a potential for parasites to develop resistance. A new N-(3,4-methylenedioxyphenyl)-N'- (2-phenethyl) thiourea compound (thiourea 1) has shown promising in vitro activity against Leishmania amazonensis with an IC₅₀ of 54.14 μM for promastigotes and an IC₅₀ of 70 μM for amastigotes.

OBJECTIVE

To develop a formulation of thiourea 1 as an oral treatment for leishmaniasis, it was incorporated into Nanoparticles (NPs), a proven approach to provide long-acting drug delivery systems.

METHODS

Poly (D,L-Lactic-co-Glycolic Acid) (PLGA) polymeric NPs containing thiourea 1 were obtained through a nanoprecipitation methodology associated with solvent evaporation. The NPs containing thiourea 1 were characterized for Encapsulation Efficiency (EE%), reaction yield (% w/w), surface charge, particle size and morphology by Transmission Electron Microscopy (TEM).

RESULTS

NPs with thiourea 1 showed an improved in vitro leishmanicidal activity with a reduction in its cytotoxicity against macrophages (CC₅₀>100 μg/mL) while preserving its IC₅₀ against intracellular amastigotes (1.46 ± 0.09 μg/mL). This represents a parasite Selectivity Index (SI) of 68.49, which is a marked advancement from the reference drug pentamidine (SI = 30.14).

CONCLUSION

The results suggest that the incorporation into NPs potentiated the therapeutic effect of thiourea 1, most likely by improving the selective delivery of the drug to the phagocytic cells that are targeted for infection by L. amazonensis. This work reinforces the importance of nanotechnology in the acquisition of new therapeutic alternatives for oral treatments.

A novel moniliformin derivative as pan-inhibitor of histone deacetylases triggering apoptosis of leukemia cells

New and potent agents that evade multidrug resistance (MDR) and inhibit epigenetic modifications are of great interest in cancer drug development. Here, we describe that a moniliformin derivative (IUPAC name: 3-(naphthalen-2-ylsulfanyl)-4-{[(2Z)-1,3,3-trimethyl-2,3-dihydro-1H-indol-2-ylidene]methyl}cyclobut-3-ene-1,2-dione; code: MCC1381) bypasses P-gp-mediated MDR. Using transcriptomics, we identified a large number of genes significantly regulated in response to MCC1381, which affected the cell cycle and disturbed cellular death and survival. The potential targets of MCC1381 might be histone deacetylases (HDACs) as predicted by SwissTargetPrediction. In silico studies confirmed that MCC1381 presented comparable affinity with HDAC1, 2, 3, 6, 8 and 11. Besides, the inhibition activity of HDACs was dose-dependently inhibited by MCC1381. Particularly, a strong binding affinity was observed between MCC1381 and HDAC6 by microscale thermophoresis analysis. MCC1381 decreased the expression of HDAC6, inversely correlated with the increase of acetylated HDAC6 substrates, acetylation p53 and α-tubulin. Furthermore, MCC1381 arrested the cell cycle at the G₂/M phase, induced the generation of reactive oxygen species and collapse of the mitochondrial membrane potential. MCC1381 exhibited in vivo anti-cancer activity in xenografted zebrafish. Collectively, MCC1381 extended cytotoxicity towards P-gp-resistant leukemia cancer cells and may act as a pan-HDACs inhibitor, indicating that MCC1381 is a novel candidate for cancer therapy.

Appraisal of the Role of In silico Methods in Pyrazole Based Drug Design

Pyrazole and its derivatives are a pharmacologically and significantly active scaffolds that have innumerable physiological and pharmacological activities. They can be very good targets for the discovery of novel anti-bacterial, anti-cancer, anti-inflammatory, anti-fungal, anti-tubercular, antiviral, antioxidant, antidepressant, anti-convulsant and neuroprotective drugs. This review focuses on the importance of in silico manipulations of pyrazole and its derivatives for medicinal chemistry. The authors have discussed currently available information on the use of computational techniques like molecular docking, structure-based virtual screening (SBVS), molecular dynamics (MD) simulations, quantitative structure activity relationship (QSAR), comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to drug design using pyrazole moieties. Pyrazole based drug design is mainly dependent on the integration of experimental and computational approaches. The authors feel that more studies need to be done to fully explore the pharmacological potential of the pyrazole moiety and in silico method can be of great help.

Use of molecular docking computational tools in drug discovery

Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.

Computational Approaches for the Design of (Mutant-)Selective Tyrosine Kinase Inhibitors: State-of-the-Art and Future Prospects

Kinases remain one of the major attractive therapeutic targets for a large number of indications such as cancer, rheumatoid arthritis, cardiac failure and many others. Design and development of kinase inhibitors (ATP-competitive, allosteric or covalent) is a clinically validated and successful strategy in the pharmaceutical industry. The perks come with limitations, particularly the development of resistance to highly potent and selective inhibitors. When this happens, the cycle needs to be repeated, i.e., the design and development of kinase inhibitors active against the mutated forms. The complexity of tumor milieu makes it awfully difficult for these molecularly-targeted therapies to work. Every year newer and better versions of these agents are introduced in the clinic. Several computational approaches such as structure-, ligand-based or hybrid ones continue to live up to their potential in discovering novel kinase inhibitors. New schools of thought in this area continue to emerge, e.g., development of dual-target kinase inhibitors. But there are fundamental issues with this approach. It is indeed difficult to selectively optimize binding at two entirely different or related kinases. In addition to the conventional strategies, modern technologies (machine learning, deep learning, artificial intelligence, etc.) started yielding the results and building success stories. Computational tools invariably played a critical role in catalysing the phenomenal progress in kinase drug discovery field. The present review summarized the progress in utilizing computational methods and tools for discovering (mutant-)selective tyrosine kinase inhibitor drugs in the last three years (2017-2019). Representative investigations have been discussed, while others are merely listed. The author believes that the enthusiastic reader will be inspired to dig out the cited literature extensively to appreciate the progress made so far and the future prospects of the field.

Nitroimidazoles Part 9. Synthesis, molecular docking, and anticancer evaluations of piperazine-tagged imidazole derivatives

New piperazine-tagged imidazole derivatives were synthesized via reaction of 1-alkyl/aryl-5-bromo-2-alkyl-4-nitro-1H-imidazoles 1–3 with piperazine nucleophiles. Nine selected compounds were assessed for their antiproliferative inhibition potency against five human cancer cell lines (MCF-7, PC3, Du145, HepG2 and Dermal/Fibroblast). Compounds 7 and 10 are the most potent anticancer agents on HepG2 cell line with IC50 values of (5.6 ± 0.5 µm) and (29.6 ± 7.6 µm) respectively, and on MCF-7 with IC50 values of (32.1 ± 5.6 µm) and (46.2 ± 8.2 µm) respectively. The molecular docking of compounds 7 and 10 has been studied, and the results reveal that the newly designed piperazine-tagged imidazole derivatives bind to the hydrophobic pocket and polar contact with high affinity.

Bioactivity-guided separation of potential α-glycosidase inhibitor from clerodendranthus spicatus based on HSCCC coupled with molecular docking

Clerodendranthus Spicatus is a traditional Dais medi-edible plant and it has been proven to have good blood glucose-lowering efficacy. However, the material basis of Clerodendranthus Spicatus has not been clarified yet and therefore needs to be determined. In this paper, the effective ingredients of this medicine were purified by high-speed counter-current chromatography. Alongside, their potential hypoglycemic activity was determined by α-glucosidase inhibitory activities in vitro and molecular docking. Finally, five compounds were purified and identified as 2-caffeoyl-L-tartaric acid (1), N-(E)-caffeoyldopamine (2), rosmarinc acid (3), methyl rosmarinate (4), 6,7,8,3',4'-Pentamethoxyflavone (5). Examination of α-glucosidase inhibitory activity in vitro showed that 2-caffeoyl-L-tartaric acid and rosmarinic acid had a higher inhibitory activity than acarbose. Molecular docking indicated that the affinity energy of the identified compounds ranged from - 7.6 to - 8.6 kcal/mol, a more desirable result than acarbose (- 6.6 kcal/mol). Particularly, rosmarinc acid with the lowest affinity energy of - 8.6 kcal/mol was wrapped with 6 hydrogen bonds. Overall, α-glucosidase inhibitory activities and molecular docking suggested that rosmarinc acid was likely to be a promising hypoglycemic drug.

The latest automated docking technologies for novel drug discovery

INTRODUCTION

Molecular docking has been consolidated as one of the most important methods in the molecular modeling field. It has been recognized as a prominent tool in the study of protein-ligand complexes, to describe intermolecular interactions, to accurately predict poses of multiple ligands, to discover novel promising bioactive compounds. Molecular docking methods have evolved in terms of their accuracy and reliability; but there are pending issues to solve for improving the connection between the docking results and the experimental evidence.

AREAS COVERED

In this article, the author reviews very recent innovative molecular docking applications with special emphasis on reverse docking, treatment of protein flexibility, the use of experimental data to guide the selection of docking poses, the application of Quantum mechanics(QM) in docking, and covalent docking.

EXPERT OPINION

There are several issues being worked on in recent years that will lead to important breakthroughs in molecular docking methods in the near future These developments are related to more efficient exploration of large datasets and receptor conformations, advances in electronic description, and the use of structural information for guiding the selection of results.

Glycogen Phosphorylase: A Drug Target of Amino Alcohols in Echinococcus granulosus, Predicted by a Computer-Aided Method

Echinococcosis is an important parasitic disease that threats human health and animal husbandry worldwide. However, the low cure rate of clinical drugs for this disease is a challenge. Hence, novel compounds and specific drug targets are urgently needed. In this study, we identified drug targets of amino alcohols with effects on Echinococcus species. The drug targets were predicted with the idTarget web server. Corresponding three-dimensional structures of the drug targets were built after sequence BLAST analysis and homology modeling. After further screening by molecular docking, the activities of the candidate targets were validated in vitro. We ultimately identified glycogen phosphorylase as a potential drug target for amino alcohols. There are two genes coding glycogen phosphorylase in Echinococcus granulosus (EgGp1 and EgGp2). EgGp1 was abundant in E. granulosus PSCs, while EgGp2 was abundant in the cysts. These proteins were located at suckers and somas of E. granulosus PSCs and near the rostellum of cysts developed from PSCs. The effective compounds docked into a pocket consisting of E124, K543 and K654 and affected (either inhibited or enhanced) the activity of E. granulosus glycogen phosphorylase. In this study, we designed a method to predict drug targets for echinococcosis treatment based on inverse docking. The candidate targets found by this method can contribute not only to understanding of the modes of action of amino alcohols but also to modeling-aided drug design based on targets.

In Ovo and In Silico Evaluation of the Anti-Angiogenic Potential of Syringin

INTRODUCTION

Cancer is considered as one of the deadliest human diseases today. Angiogenesis, the propagation of new blood vessels from pre-existing vasculature, is a critical step in the progression of cancer as it is essential in the growth and metastasis of tumors. Hence, suppression of angiogenesis is a promising approach in cancer therapy. Syringin, a phenylpropanoid glycoside with a molecular formula of C17H24O9, has been found to exhibit chemopreventive effects. However, its anti-angiogenic activity and the underlying mechanism of action are still unknown.

METHODS

In this work, in ovo chorioallantoic membrane (CAM) assay has been conducted to evaluate the effect of syringin on neovascularization. Additionally, reverse molecular docking studies have been performed in order to identify the probable enzyme targets in the angiogenesis pathway.

RESULTS

Treatment with syringin showed significant dose-dependent inhibition of blood vessel length and junctions in the CAM of duck eggs; the anti-angiogenic activity of syringin at 100 µM and 200 µM is comparable with 200 µM of the positive control celecoxib. The results of reverse docking studies indicate that syringin binds the strongest to dihydrofolate reductase (DHFR) and, to some extent, with transforming growth factor-beta receptor type 1 (TGF-βR1), vascular endothelial growth factor receptor 2 (VEGFR2), and matrix metalloproteinase-2 (MMP-2). Furthermore, ADMET models revealed that syringin potentially possesses excellent pharmacokinetic and toxicity profiles.

CONCLUSION

This study demonstrates the potential of syringin as an anti-angiogenic agent and elicits further investigations to establish its application in cancer suppression.

Recent Advances in Molecular Docking for the Research and Discovery of Potential Marine Drugs

Marine drugs have long been used and exhibit unique advantages in clinical practices. Among the marine drugs that have been approved by the Food and Drug Administration (FDA), the protein–ligand interactions, such as cytarabine–DNA polymerase, vidarabine–adenylyl cyclase, and eribulin–tubulin complexes, are the important mechanisms of action for their efficacy. However, the complex and multi-targeted components in marine medicinal resources, their bio-active chemical basis, and mechanisms of action have posed huge challenges in the discovery and development of marine drugs so far, which need to be systematically investigated in-depth. Molecular docking could effectively predict the binding mode and binding energy of the protein–ligand complexes and has become a major method of computer-aided drug design (CADD), hence this powerful tool has been widely used in many aspects of the research on marine drugs. This review introduces the basic principles and software of the molecular docking and further summarizes the applications of this method in marine drug discovery and design, including the early virtual screening in the drug discovery stage, drug target discovery, potential mechanisms of action, and the prediction of drug metabolism. In addition, this review would also discuss and prospect the problems of molecular docking, in order to provide more theoretical basis for clinical practices and new marine drug research and development.

In silico study for prediction of novel bioactivities of the endophytic fungal alkaloid, mycoleptodiscin B for human targets

Mycoleptodiscin B is a natural product extracted from the endophytic fungus Mycoleptodiscus sp. found in Sri Lanka and Panama with experimentally unexplored activities for human targets. In this study, a computational methodology was applied to determine druggable targets of mycoleptodiscin B. According to the computational toxicity and pharmacokinetics assessment, mycoleptodiscin B was proven to be a suitable drug candidate. Druggable targets for this compound, aromatase, acidic plasma glycoprotein and androgen receptor, were predicted using reverse docking. A two-step validation of those targets was performed using conventional molecular docking and molecular dynamic (MD) simulations, resulting in aromatase being determined as the potential therapeutic target. Based on molecular mechanics/Generalized Born Surface Area (GBSA) free energies and ligand stability inside the active site cavity during its 120 ns MD run, it can be concluded that mycoleptodiscin B is a potent aromatase inhibitor and could be subjected to further in vitro and in vivo experiments in the drug development pipeline. Consequently, natural product chemists can quickly identify the hidden medicinal properties of their miracle compounds using the computational approach applied in this research.

Computational/in silico methods in drug target and lead prediction

Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with the fundamental stage being the identification and validation of drug targets of interest for further downstream processes. Thus, various computational methods have been developed to complement experimental approaches in drug discovery. Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during the computational drug discovery process.

Repurposing old drugs to fight multidrug resistant cancers

Overcoming multidrug resistance represents a major challenge for cancer treatment. In the search for new chemotherapeutics to treat malignant diseases, drug repurposing gained a tremendous interest during the past years. Repositioning candidates have often emerged through several stages of clinical drug development, and may even be marketed, thus attracting the attention and interest of pharmaceutical companies as well as regulatory agencies. Typically, drug repositioning has been serendipitous, using undesired side effects of small molecule drugs to exploit new disease indications. As bioinformatics gain increasing popularity as an integral component of drug discovery, more rational approaches are needed. Herein, we show some practical examples of in silico approaches such as pharmacophore modelling, as well as pharmacophore- and docking-based virtual screening for a fast and cost-effective repurposing of small molecule drugs against multidrug resistant cancers. We provide a timely and comprehensive overview of compounds with considerable potential to be repositioned for cancer therapeutics. These drugs are from diverse chemotherapeutic classes. We emphasize the scope and limitations of anthelmintics, antibiotics, antifungals, antivirals, antimalarials, antihypertensives, psychopharmaceuticals and antidiabetics that have shown extensive immunomodulatory, antiproliferative, pro-apoptotic, and antimetastatic potential. These drugs, either used alone or in combination with existing anticancer chemotherapeutics, represent strong candidates to prevent or overcome drug resistance. We particularly focus on outcomes and future perspectives of drug repositioning for the treatment of multidrug resistant tumors and discuss current possibilities and limitations of preclinical and clinical investigations.

D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19

A highly effective medicine is urgently required to cure coronavirus disease 2019 (COVID-19). For the purpose, we developed a molecular docking based webserver, namely D3Targets-2019-nCoV, with two functions, one is for predicting drug targets for drugs or active compounds observed from clinic or in vitro/in vivo studies, the other is for identifying lead compounds against potential drug targets via docking. This server has its unique features, (1) the potential target proteins and their different conformations involving in the whole process from virus infection to replication and release were included as many as possible; (2) all the potential ligand-binding sites with volume larger than 200 Å3 on a protein structure were identified for docking; (3) correlation information among some conformations or binding sites was annotated; (4) it is easy to be updated, and is accessible freely to public (https://www.d3pharma.com/D3Targets-2019-nCoV/index.php). Currently, the webserver contains 42 proteins [20 severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) encoded proteins and 22 human proteins involved in virus infection, replication and release] with 69 different conformations/structures and 557 potential ligand-binding pockets in total. With 6 examples, we demonstrated that the webserver should be useful to medicinal chemists, pharmacologists and clinicians for efficiently discovering or developing effective drugs against the SARS-CoV-2 to cure COVID-19.

Computational Methods for the Identification of Molecular Targets of Toxic Food Additives. Butylated Hydroxytoluene as a Case Study

Butylated hydroxytoluene (BHT) is one of the most commonly used synthetic antioxidants in food, cosmetic, pharmaceutical and petrochemical products. BHT is considered safe for human health; however, its widespread use together with the potential toxicological effects have increased consumers concern about the use of this synthetic food additive. In addition, the estimated daily intake of BHT has been demonstrated to exceed the recommended acceptable threshold. In the present work, using BHT as a case study, the usefulness of computational techniques, such as reverse screening and molecular docking, in identifying protein-ligand interactions of food additives at the bases of their toxicological effects has been probed. The computational methods here employed have been useful for the identification of several potential unknown targets of BHT, suggesting a possible explanation for its toxic effects. In silico analyses can be employed to identify new macromolecular targets of synthetic food additives and to explore their functional mechanisms or side effects. Noteworthy, this could be important for the cases in which there is an evident lack of experimental studies, as is the case for BHT.

CB-Dock: a web server for cavity detection-guided protein-ligand blind docking

As the number of elucidated protein structures is rapidly increasing, the growing data call for methods to efficiently exploit the structural information for biological and pharmaceutical purposes. Given the three-dimensional (3D) structure of a protein and a ligand, predicting their binding sites and affinity are a key task for computer-aided drug discovery. To address this task, a variety of docking tools have been developed. Most of them focus on docking in the preset binding sites given by users. To automatically predict binding modes without information about binding sites, we developed a user-friendly blind docking web server, named CB-Dock, which predicts binding sites of a given protein and calculates the centers and sizes with a novel curvature-based cavity detection approach, and performs docking with a popular docking program, Autodock Vina. This method was carefully optimized and achieved ~70% success rate for the top-ranking poses whose root mean square deviation (RMSD) were within 2 Å from the X-ray pose, which outperformed the state-of-the-art blind docking tools in our benchmark tests. CB-Dock offers an interactive 3D visualization of results, and is freely available at http://cao.labshare.cn/cb-dock/.

ACID: a free tool for drug repurposing using consensus inverse docking strategy

Drug repurposing offers a promising alternative to dramatically shorten the process of traditional de novo development of a drug. These efforts leverage the fact that a single molecule can act on multiple targets and could be beneficial to indications where the additional targets are relevant. Hence, extensive research efforts have been directed toward developing drug based computational approaches. However, many drug based approaches are known to incur low successful rates, due to incomplete modeling of drug-target interactions. There are also many technical limitations to transform theoretical computational models into practical use. Drug based approaches may, thus, still face challenges for drug repurposing task. Upon this challenge, we developed a consensus inverse docking (CID) workflow, which has a ~ 10% enhancement in success rate compared with current best method. Besides, an easily accessible web server named auto in silico consensus inverse docking (ACID) was designed based on this workflow (http://chemyang.ccnu.edu.cn/ccb/server/ACID).

Massive in Silico Study of Noble Gas Binding to the Structural Proteome

Noble gases are chemically inert, and it was therefore thought they would have little effect on biology. Paradoxically, it was found that they do exhibit a wide range of biological effects, many of which are target-specific and potentially useful and some of which have been demonstrated in vivo. The underlying mechanisms by which useful pharmacology, such as tissue and neuroprotection, anti-addiction effects, and analgesia, is elicited are relatively unexplored. Experiments to probe the interactions of noble gases with specific proteins are more difficult with gases than those with other chemicals. It is clearly impractical to conduct the large number of gas-protein experiments required to gain a complete picture of noble gas biology. Given the simplicity of atoms as ligands, in silico methods provide an opportunity to gain insight into which noble gas-protein interactions are worthy of further experimental or advanced computational investigation. Our previous validation studies showed that in silico methods can accurately predict experimentally determined noble gas binding sites in X-ray structures of proteins. Here, we summarize the largest reported in silico reverse docking study involving 127 854 protein structures and the five nonradioactive noble gases. We describe how these computational screening methods are implemented, summarize the main types of interactions that occur between noble gases and target proteins, describe how the massive data set that this study generated can be analyzed (freely available at group18.csiro.au), and provide the NDMA receptor as an example of how these data can be used to understand the molecular pharmacology underlying the biology of the noble gases. We encourage chemical biologists to access the data and use them to expand the knowledge base of noble gas pharmacology, and to use this information, together with more efficient delivery systems, to develop "atomic drugs" that can fully exploit their considerable and relatively unexplored potential in medicine.

Molecular Docking: Shifting Paradigms in Drug Discovery

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

Multi-targeting protein-protein interaction inhibitors: Evolution of macrocyclic ligands with embedded carbohydrates (MECs) to improve selectivity

Compounds targeting multiple proteins can have synergistic effects and are therefore of interest in medicinal chemistry. At the same time, inhibiting protein-protein interactions (PPI) is increasingly desired in the treatment of disorders or diseases. The development of non-peptidomimetic inhibitors is still a challenge. Herein we investigate macrocyclic scaffolds with one or two embedded carbohydrates (MECs) that present amino acid side chains, or related isosteres, as pharmacophoric groups. Firstly, retroscreening of the previously reported eannaphane-40 (E40, 40), a MEC presenting two pharmacophoric groups, against a set of 55 receptor-subtypes led to a finding of sub-micromolar inhibitory activity for E40 against three serotonergic isoforms (5HT_1A/_2A/2B) as well as the Na⁺ channel and the NK-2 receptor. We synthesised MECs with an additional pharmacophoric group compared to E40, with a view to identifying compounds where the selectivity profile was altered among the protein hits from the retroscreening. MECs were produced based on scaffolds with two monosaccharide residues, leading to the incorporation of a third pharmacophoric group. Later, homology models were prepared for four proteins (5HT_1A, 5HT_2A, NK₂ and site-2 of the sodium channel) whose 3D structure is unknown. Inverse docking of the synthesised compounds led to the selection of a new MEC (MEC-B) for protein binding assays. MEC-B was found to have its selectivity profile modulated, in line with docking prediction, compared to E40. MEC-B is dual inhibitor of both 5-HT_1A and the sodium channel with improved selectivity for these proteins compared to 5-HT_2A/2B/2C, 5-HT transporter and NK₂ receptor. Thus, a new multitargeting compound, with an improved selectivity profile was identified, based on a MEC peptidomimetic scaffold.