TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database

Putative mechanism of a multivitamin treatment against insulin resistance

Insulin resistance is caused by the abnormal secretion of proinflammatory cytokines in adipose tissue, which is induced by an increase in lipid accumulation in adipocytes, hepatocytes, and myocytes. The inflammatory pathway involves multiple targets such as nuclear factor kappa B, inhibitor of nuclear factor κ-B kinase, and mitogen-activated protein kinase. Vitamins are micronutrients with anti-inflammatory activities that have unclear mechanisms. The present study aimed to describe the putative mechanisms of vitamins involved in the inflammatory pathway of insulin resistance. The strategy to achieve this goal was to integrate data mining and analysis, target prediction, and molecular docking simulation calculations to support our hypotheses. Our results suggest that the multitarget activity of vitamins A, B1, B2, B3, B5, B6, B7, B12, C, D3, and E inhibits nuclear factor kappa B and mitogen-activated protein kinase, in addition to vitamins A and B12 against inhibitor of nuclear factor κ-B kinase. The findings of this study highlight the pharmacological potential of using an anti-inflammatory and multitarget treatment based on vitamins and open new perspectives to evaluate the inhibitory activity of vitamins against nuclear factor kappa B, mitogen-activated protein kinase, and inhibitor of nuclear factor κ-B kinase in an insulin-resistant context.

Discovering novel targets of abscisic acid using computational approaches

Abscisic acid (ABA) is a crucial plant hormone that is naturally produced in various mammalian tissues and holds significant potential as a therapeutic molecule in humans. ABA is selected for this study due to its known roles in essential human metabolic processes, such as glucose homeostasis, immune responses, cardiovascular system, and inflammation regulation. Despite its known importance, the molecular mechanism underlying ABA's action remain largely unexplored. This study employed computational techniques to identify potential human ABA receptors. We screened 64 candidate molecules using online servers and performed molecular docking to assess binding affinity and interaction types with ABA. The stability and dynamics of the best complexes were investigated using molecular dynamics simulation over a 100 ns time period. Root mean square fluctuations (RMSF), root mean square deviation (RMSD), solvent-accessible surface area (SASA), radius of gyration (Rg), free energy landscape (FEL), and principal component analysis (PCA) were analyzed. Next, the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method was employed to calculate the binding energies of the complexes based on the simulated data. Our study successfully pinpointed four key receptors responsible for ABA signaling (androgen receptor, glucocorticoid receptor, mineralocorticoid receptor, and retinoic acid receptor beta) that have a strong affinity for binding with ABA and remained structurally stable throughout the simulations. The simulations with Hydralazine as an unrelated ligand were conducted to validate the specificity of the identified receptors for ABA. The findings of this study can contribute to further experimental validation and a better understanding of how ABA functions in humans.

Praelolide alleviates collagen-induced arthritis through increasing catalase activity and activating Nrf2 pathway

BACKGROUND

Marine diterpenes represent a promising reservoir for identifying potential anti-rheumatoid arthritis (RA) candidates. Praelolide is a gorgonian-derived briarane-type diterpenoid with antioxidative and anti-osteoclastogenetic properties.

OBJECTIVE

This study aims to evaluate the therapeutic efficacy of praelolide against RA and investigate its underlying mechanisms both in vivo and in vitro.

METHOD

Collagen-induced arthritis (CIA) mice and human RA fibroblast-like synoviocyte MH7A cells were employed for bioassays. The VisuGait system was utilized to assess gait dysfunction resulting from joint pain. Histopathological changes in ankle and synovial tissues were evaluated using micro-computed tomography, hematoxylin and eosin staining, Safranin-O/Fast Green staining, tartrate resistant acid phosphatase staining, and immunohistochemistry. Fluorescence spectroscopy, circular dichroism, and surface plasmon resonance were employed to investigate interactions between praelolide and catalase. The production of inflammatory cytokines and expression levels of proteins were assessed using ELISA and Western blotting, respectively.

RESULT

Praelolide significantly reduced paw swelling and arthritis scores, improved gait deficits, and restored synovial histopathological alterations and bone erosion in CIA mice. In vivo and in vitro, praelolide effectively decreased the expression and production of inflammatory cytokines such as interleukin (IL)-1β and IL-6. Additionally, praelolide inhibited osteoclastogenesis on bone surface of the ankle joints and in a tumor necrosis factor-α (TNF-α)-induced MH7A/bone marrow-derived macrophages (BMMs) co-culture system, and it strongly suppressed reactive oxygen species (ROS) production. Mechanistically, praelolide modulated catalase through non-covalent interactions, inducing conformational alterations that enhanced catalase activity and stability against time- and temperature-induced degradation. Further investigation revealed that praelolide significantly upregulated the expression of Nrf2, subsequently activating downstream antioxidant enzymes.

CONCLUSION

Praelolide markedly alleviated synovial inflammation and bone destruction in CIA mice by enhancing catalase activity and activating the Nrf2 pathway to reduce disease-related ROS accumulation, highlighting praelolide as a promising candidate for multitarget treatment of RA.

In silico exploration of 4(α-l-rhamnosyloxy)-benzyl isothiocyanate: A promising phytochemical-based drug discovery approach for combating multi-drug resistant Staphylococcus aureus

Multidrug-resistant (MDR) Staphylococcus aureus infections significantly threaten global health. With rising resistance to current antibiotics and limited solutions, the urgent discovery of new, effective, and affordable antibacterials with low toxicity is imperative to combat diverse MDR S. aureus strains. Hence, in this study, we introduce an in silico phytochemical-based approach for discovering novel antibacterial agents, underscoring the potential of computational approaches in therapeutic discovery. Glucomoringin Isothiocyanate (GMG-ITC) from Moringa oleifera Lam. is one of the phytochemical compounds with several biological activities, including antimicrobial, anti-inflammatory, and antioxidant activities, and is also effective against S. aureus. This study focuses on screening GMG-ITC as a potential drug candidate to combat MDR S. aureus infections through a molecular docking approach. Moreover, interaction amino acid analysis, in silico pharmacokinetics, compound target prediction, pathway enrichment analysis and molecular dynamics (MD) simulations were conducted for further investigation. Molecular docking and interaction analysis showed strong binding affinity towards S. aureus lipase, dihydrofolate reductase, and other MDR S. aureus proteins, including penicillin-binding protein 2a, MepR, D-Ala:D-Ala ligase, and RPP TetM, through hydrophilic and hydrophobic interactions. GMG-ITC also showed a strong binding affinity to cyclooxygenase-2 and FAD-dependent NAD(P)H oxidase, suggesting that it is a potential anti-inflammatory and antioxidant candidate that may eliminate inflammation and oxidative stress associated with S. aureus infections. MD simulations validated the stability of the GMG-ITC molecular interactions determined by molecular docking. In silico pharmacokinetic analysis highlights its potency as a drug candidate, showing strong absorption, distribution, and excretion properties in combination with low toxicity. It acts as an active protease and enzyme inhibitor with moderate activity against GPCR ligands, ion channels, nuclear receptor ligands, and kinases. Enrichment analysis further elucidated its involvement in important biological, molecular, and cellular functions with potential therapeutic applications in diseases like cancer, hepatitis B, and influenza. Results suggest that GMG-ITC is an effective antibacterial agent that could treat MDR S. aureus-associated infections.

CACTI: an in silico chemical analysis tool through the integration of chemogenomic data and clustering analysis

It is well-accepted that knowledge of a small molecule's target can accelerate optimization. Although chemogenomic databases are helpful resources for predicting or finding compound interaction partners, they tend to be limited and poorly annotated. Furthermore, unlike genes, compound identifiers are often not standardized, and many synonyms may exist, especially in the biological literature, making batch analysis of compounds difficult. Here, we constructed an open-source annotation and target hypothesis prediction tool that explores some of the largest chemical and biological databases, mining these for both common name, synonyms, and structurally similar molecules. We used this Chemical Analysis and Clustering for Target Identification (CACTI) tool to analyze the Pathogen Box collection, an open-source set of 400 drug-like compounds active against a variety of microbial pathogens. Our analysis resulted in 4,315 new synonyms, 35,963 pieces of new information and target prediction hints for 58 members.Scientific contributionsWith the employment of this tool, a comprehensive report with known evidence, close analogs and drug-target prediction can be obtained for large-scale chemical libraries that will facilitate their evaluation and future target validation and optimization efforts.

Current Small Molecule-Based Medicinal Chemistry Approaches for Neurodegeneration Therapeutics

Neurodegenerative diseases (NDDs) like Alzheimer's disease (AD), Parkinson's disease (PD), and Amyotrophic lateral sclerosis (ALS) possess multifactorial aetiologies. In recent years, our understanding of the biochemical and molecular pathways across NDDs has increased, however, new advances in small molecule-based therapeutic strategies targeting NDDs are obscure and scarce. Moreover, NDDs have been studied for more than five decades, however, there is a paucity of drugs that can treat NDDs. Further, the highly lipoidal blood-brain barrier (BBB) limits the uptake of many therapeutic molecules into the brain and is a complicating factor in the development of new agents to treat neurodegeneration. Considering the highly complex nature of NDDs, the association of multiple risk factors, and the challenges to overcome the BBB junction, medicinal chemists have developed small organic molecule-based novel approaches to target NDDs over the last few decades, such as designing lipophilic molecules and applying prodrug strategies. Attempts have been made to utilize a multitarget approach to modulate different biochemical molecular pathways involved in NDDs, in addition to, medicinal chemists making better decisions in identifying optimized drug candidates for the central nervous system (CNS) by using web-based computational tools. To increase the clinical success of these drug candidates, an in vitro assay modeling the BBB has been utilized by medicinal chemists in the pre-clinical phase as a further screening measure of small organic molecules. Herein, we examine some of the intriguing strategies taken by medicinal chemists to design small organic molecules to combat NDDs, with the intention of increasing our awareness of neurodegenerative therapeutics.

PT-Finder: A multi-modal neural network approach to target identification

Efficient target identification for bioactive compounds, including novel synthetic analogs, is crucial for accelerating the drug discovery pipeline. However, the process of target identification presents significant challenges and is often expensive, which in turn can hinder the drug discovery efforts. To address these challenges machine learning applications have arisen as a promising approach for predicting the targets for novel chemical compounds. These methods allow the exploration of ligand-target interactions, uncovering of biochemical mechanisms, and the investigation of drug repurposing. Typically, the current target identification tools rely on assessing ligand structural similarities. Herein, a multi-modal neural network model was built using a library of proteins, their respective sequences, and active inhibitors. Subsequent validations showed the model to possess accuracy of 82 % and MPRAUC of 0.80. Leveraging the trained model, we developed PT-Finder (Protein Target Finder), a user-friendly offline application that is capable of predicting the target proteins for hundreds of compounds within a few seconds. This combination of offline operation, speed, and accuracy positions PT-Finder as a powerful tool to accelerate drug discovery workflows. PT-Finder and its source codes have been made freely accessible for download at https://github.com/PT-Finder/PT-Finder.

Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity

In the early stages of drug development, large chemical libraries are typically screened to identify compounds of promising potency against the chosen targets. Often, however, the resulting hit compounds tend to have poor drug metabolism and pharmacokinetics (DMPK), with negative developability features that may be difficult to eliminate. Therefore, starting the drug discovery process with a "null library", compounds that have highly desirable DMPK properties but no potency against the chosen targets, could be advantageous. Here, we explore the opportunities offered by machine learning to realize this strategy in the case of the inhibition of α-synuclein aggregation, a process associated with Parkinson's disease. We apply MolDQN, a generative machine learning method, to build an inhibitory activity against α-synuclein aggregation into an initial inactive compound with good DMPK properties. Our results illustrate how generative modeling can be used to endow initially inert compounds with desirable developability properties.

Predicting drug-target interactions using matrix factorization with self-paced learning and dual similarity information

BACKGROUND

Drug repositioning (DR) refers to a method used to find new targets for existing drugs. This method can effectively reduce the development cost of drugs, save time on drug development, and reduce the risks of drug design. The traditional experimental methods related to DR are time-consuming, expensive, and have a high failure rate. Several computational methods have been developed with the increase in data volume and computing power. In the last decade, matrix factorization (MF) methods have been widely used in DR issues. However, these methods still have some challenges. (1) The model easily falls into a bad local optimal solution due to the high noise and high missing rate in the data. (2) Single similarity information makes the learning power of the model insufficient in terms of identifying the potential associations accurately.

OBJECTIVE

We proposed self-paced learning with dual similarity information and MF (SPLDMF), which introduced the self-paced learning method and more information related to drugs and targets into the model to improve prediction performance.

METHODS

Combining self-paced learning first can effectively alleviate the model prone to fall into a bad local optimal solution because of the high noise and high data missing rate. Then, we incorporated more data into the model to improve the model's capacity for learning.

RESULTS

Our model achieved the best results on each dataset tested. For example, the area under the receiver operating characteristic curve and the precision-recall curve of SPLDMF was 0.982 and 0.815, respectively, outperforming the state-of-the-art methods.

CONCLUSION

The experimental results on five benchmark datasets and two extended datasets demonstrated the effectiveness of our approach in predicting drug-target interactions.

Frentizole, a Nontoxic Immunosuppressive Drug, and Its Analogs Display Antitumor Activity via Tubulin Inhibition

Antimitotic agents are one of the more successful types of anticancer drugs, but they suffer from toxicity and resistance. The application of approved drugs to new indications (i.e., drug repurposing) is a promising strategy for the development of new drugs. It relies on finding pattern similarities: drug effects to other drugs or conditions, similar toxicities, or structural similarity. Here, we recursively searched a database of approved drugs for structural similarity to several antimitotic agents binding to a specific site of tubulin, with the expectation of finding structures that could fit in it. These searches repeatedly retrieved frentizole, an approved nontoxic anti-inflammatory drug, thus indicating that it might behave as an antimitotic drug devoid of the undesired toxic effects. We also show that the usual repurposing approach to searching for targets of frentizole failed in most cases to find such a relationship. We synthesized frentizole and a series of analogs to assay them as antimitotic agents and found antiproliferative activity against HeLa tumor cells, inhibition of microtubule formation within cells, and arrest at the G2/M phases of the cell cycle, phenotypes that agree with binding to tubulin as the mechanism of action. The docking studies suggest binding at the colchicine site in different modes. These results support the repurposing of frentizole for cancer treatment, especially for glioblastoma.

In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling

The "Long-COVID syndrome" has posed significant challenges due to a lack of validated therapeutic options. We developed a novel multi-step virtual screening strategy to reliably identify inhibitors against 3-chymotrypsin-like protease of SARS-CoV-2 from abundant flavonoids, which represents a promising source of antiviral and immune-boosting nutrients. We identified 57 interacting residues as contributors to the protein-ligand binding pocket. Their energy interaction profiles constituted the input features for Machine Learning (ML) models. The consensus of 25 classifiers trained using various ML algorithms attained 93.9% accuracy and a 6.4% false-positive-rate. The consensus of 10 regression models for binding energy prediction also achieved a low root-mean-square error of 1.18 kcal/mol. We screened out 120 flavonoid hits first and retained 50 drug-like hits after predefined ADMET filtering to ensure bioavailability and safety profiles. Furthermore, molecular dynamics simulations prioritized nine bioactive flavonoids as promising anti-SARS-CoV-2 agents exhibiting both high structural stability (root-mean-square deviation < 5 Å for 218 ns) and low MM/PBSA binding free energy (<-6 kcal/mol). Among them, KB-2 (PubChem-CID, 14630497) and 9-O-Methylglyceofuran (PubChem-CID, 44257401) displayed excellent binding affinity and desirable pharmacokinetic capabilities. These compounds have great potential to serve as oral nutraceuticals with therapeutic and prophylactic properties as care strategies for patients with long-COVID syndrome.

Analgesic Activity of 5-Acetamido-2-Hydroxy Benzoic Acid Derivatives and an In-Vivo and In-Silico Analysis of Their Target Interactions

The design, synthesis, and evaluation of novel non-steroidal anti-inflammatory drugs (NSAIDs) with better activity and lower side effects are big challenges today. In this work, two 5-acetamido-2-hydroxy benzoic acid derivatives were proposed, increasing the alkyl position (methyl) in an acetamide moiety, and synthesized, and their structural elucidation was performed using 1H NMR and 13C NMR. The changes in methyl in larger groups such as phenyl and benzyl aim to increase their selectivity over cyclooxygenase 2 (COX-2). These 5-acetamido-2-hydroxy benzoic acid derivatives were prepared using classic methods of acylation reactions with anhydride or acyl chloride. Pharmacokinetics and toxicological properties were predicted using computational tools, and their binding affinity (kcal/mol) with COX-2 receptors (Mus musculus and Homo sapiens) was analyzed using docking studies (PDB ID 4PH9, 5KIR, 1PXX and 5F1A). An in-silico study showed that 5-acetamido-2-hydroxy benzoic acid derivates have a better bioavailability and binding affinity with the COX-2 receptor, and in-vivo anti-nociceptive activity was investigated by means of a writhing test induced by acetic acid and a hot plate. PS3, at doses of 20 and 50 mg/kg, reduced painful activity by 74% and 75%, respectively, when compared to the control group (20 mg/kg). Regarding the anti-nociceptive activity, the benzyl showed reductions in painful activity when compared to acetaminophen and 5-acetamido-2-hydroxy benzoic acid. However, the proposed derivatives are potentially more active than 5-acetamido-2-hydroxy benzoic acid and they support the design of novel and safer derivative candidates. Consequently, more studies need to be conducted to evaluate the different pharmacological actions, the toxicity of possible metabolites that can be generated, and their potential use in inflammation and pain therapy.

CODD-Pred: A Web Server for Efficient Target Identification and Bioactivity Prediction of Small Molecules

Target identification and bioactivity prediction are critical steps in the drug discovery process. Here we introduce CODD-Pred (COmprehensive Drug Design Predictor), an online web server with well-curated data sets from the GOSTAR database, which is designed with a dual purpose of predicting potential protein drug targets and computing bioactivity values of small molecules. We first designed a double molecular graph perception (DMGP) framework for target prediction based on a large library of 646 498 small molecules interacting with 640 human targets. The framework achieved a top-5 accuracy of over 80% for hitting at least one target on both external validation sets. Additionally, its performance on the external validation set comprising 200 molecules surpassed that of four existing target prediction servers. Second, we collected 56 targets closely related to the occurrence and development of cancer, metabolic diseases, and inflammatory immune diseases and developed a multi-model self-validation activity prediction (MSAP) framework that enables accurate bioactivity quantification predictions for small-molecule ligands of these 56 targets. CODD-Pred is a handy tool for rapid evaluation and optimization of small molecules with specific target activity. CODD-Pred is freely accessible at http://codd.iddd.group/.

Huperzine A injection ameliorates motor and cognitive abnormalities via regulating multiple pathways in a murine model of Parkinson's disease

As a common progressive neurodegenerative disorder, the satisfied therapies for Parkinson's disease (PD) are still unavailable. As a natural acetylcholinesterase inhibitor, the neuroprotective characteristic of Huperzine A (HupA) was supported by previous studies. However, questions remain on whether HupA injection (HAI, a main preparation of HupA) intervention conduces to PD treatment and if so, the potential molecular mechanisms. In this study, the efficacies of HAI treatment on PD-like pathological phenotypes were evaluated in a MPTP-induced PD murine model. The network pharmacology, transcriptome sequencing and experimental verification were integrated to comprehensively reveal the primary molecular mechanisms. Therapeutically, HAI intervention significantly improved the impaired locomotor behaviors as well as learning and memory abilities, and prevented the degeneration of dopaminergic neurons of PD mice. The network pharmacology analysis combined with experimental results showed that HAI treatment could effectively restore the disordered transcriptional levels of inflammatory factors and apoptosis related genes in the SNpc and striatum tissues of PD mice. Transcriptome sequencing results found that inflammation and oxidative phosphorylation served as significant functional mechanisms involved in HAI administration. The experimental verification indicated that HAI treatment effectively regulated the abnormal transcription levels of inflammation and oxidative phosphorylation related hub genes in the hippocampal samples of PD mice. In addition, molecular docking suggested strong affinity between HupA and the above core targets. Overall, this work displayed the reliable therapeutic effects of HAI on ameliorating the pathological symptoms of PD mice via modulating multiple pathways. The current findings were expected to provide a potential anti-PD agent.

Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances

INTRODUCTION

Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks.

AREAS COVERED

This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets.

EXPERT OPINION

Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.

ProfhEX: AI-based platform for small molecules liability profiling

Off-target drug interactions are a major reason for candidate failure in the drug discovery process. Anticipating potential drug's adverse effects in the early stages is necessary to minimize health risks to patients, animal testing, and economical costs. With the constantly increasing size of virtual screening libraries, AI-driven methods can be exploited as first-tier screening tools to provide liability estimation for drug candidates. In this work we present ProfhEX, an AI-driven suite of 46 OECD-compliant machine learning models that can profile small molecules on 7 relevant liability groups: cardiovascular, central nervous system, gastrointestinal, endocrine, renal, pulmonary and immune system toxicities. Experimental affinity data was collected from public and commercial data sources. The entire chemical space comprised 289'202 activity data for a total of 210'116 unique compounds, spanning over 46 targets with dataset sizes ranging from 819 to 18896. Gradient boosting and random forest algorithms were initially employed and ensembled for the selection of a champion model. Models were validated according to the OECD principles, including robust internal (cross validation, bootstrap, y-scrambling) and external validation. Champion models achieved an average Pearson correlation coefficient of 0.84 (SD of 0.05), an R² determination coefficient of 0.68 (SD = 0.1) and a root mean squared error of 0.69 (SD of 0.08). All liability groups showed good hit-detection power with an average enrichment factor at 5% of 13.1 (SD of 4.5) and AUC of 0.92 (SD of 0.05). Benchmarking against already existing tools demonstrated the predictive power of ProfhEX models for large-scale liability profiling. This platform will be further expanded with the inclusion of new targets and through complementary modelling approaches, such as structure and pharmacophore-based models. ProfhEX is freely accessible at the following address: https://profhex.exscalate.eu/ .

Significant Broad-Spectrum Antiviral Activity of Bi121 against Different Variants of SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has so far infected 762 million people with over 6.9 million deaths worldwide. Broad-spectrum viral inhibitors that block the initial stages of infection by reducing virus binding and proliferation, thereby reducing disease severities, are still an unmet global medical need. We studied Bi121, which is a standardized polyphenolic-rich compound isolated from Pelargonium sidoides, against recombinant vesicular stomatitis virus (rVSV)-pseudotyped SARS-CoV-2S (mutations in the spike protein) of six different variants of SARS-CoV-2. Bi121 was effective at neutralizing all six rVSV-ΔG-SARS-CoV-2S variants. The antiviral activity of Bi121 was also assessed against SARS-CoV-2 variants (USA WA1/2020, Hongkong/VM20001061/2020, B.1.167.2 (Delta), and Omicron) in Vero cells and HEK-ACE2 cell lines using RT-qPCR and plaque assays. Bi121 showed significant antiviral activity against all the four SARS-CoV-2 variants tested, suggesting a broad-spectrum activity. Bi121 fractions generated using HPLC showed antiviral activity in three fractions out of eight against SARS-CoV-2. The dominant compound identified in all three fractions using LC/MS/MS analysis was Neoilludin B. In silico structural modeling studies with Neoilludin B showed that it has a novel RNA-intercalating activity toward RNA viruses. In silico findings and the antiviral activity of this compound against several SARS-CoV-2 variants support further evaluation as a potential treatment of COVID-19.

The critical role of mode of action studies in kinetoplastid drug discovery

Understanding the target and mode of action of compounds identified by phenotypic screening can greatly facilitate the process of drug discovery and development. Here, we outline the tools currently available for target identification against the neglected tropical diseases, human African trypanosomiasis, visceral leishmaniasis and Chagas' disease. We provide examples how these tools can be used to identify and triage undesirable mechanisms, to identify potential toxic liabilities in patients and to manage a balanced portfolio of target-based campaigns. We review the primary targets of drugs that are currently in clinical development that were initially identified via phenotypic screening, and whose modes of action affect protein turnover, RNA trans-splicing or signalling in these protozoan parasites.

In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences

Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale information of chemical structures and protein sequences. By combining the information of a compound with multiple protein targets together and putting these compound-target pairs into a well-established model, the scores to indicate whether there are interactions between compounds and targets can be derived, and thus a target prediction task can be completed by sorting the outputted scores. To improve the prediction performance, we constructed several chemogenomic models using multi-scale information of chemical structures and protein sequences, and the ensemble model with the best performance was used as our final model. The model was validated by various strategies and external datasets and the promising target prediction capability of the model, i.e., the fraction of known targets identified in the top-k (1 to 10) list of the potential target candidates suggested by the model, was confirmed. Compared with multiple state-of-art target prediction methods, our model showed equivalent or better predictive ability in terms of the top-k predictions. It is expected that our method can be utilized as a powerful computational tool to narrow down the potential targets for experimental testing.

Pretreatment with rosavin attenuates PM2.5-induced lung injury in rats through antiferroptosis via PI3K/Akt/Nrf2 signaling pathway

Inflammation and oxidative stress caused by fine particulate matter (PM2.5) increase the incidence and mortality rates of respiratory disorders. Rosavin is the main chemical component of Rhodiola plants, which exerts anti-oxidative and antiinflammatory effects. In this research, the potential therapeutic effect of rosavin was investigated by the PM2.5-induced lung injury rat model. Rats were instilled with PM2.5 (7.5 mg/kg) suspension intratracheally, while rosavin (50 mg/kg, 100 mg/kg) was delivered by intraperitoneal injection before the PM2.5 injection. It was observed that rosavin could prevent lung injury caused by PM2.5. PM2.5 showed obvious ferroptosis-related ultrastructural alterations, which were significantly corrected by rosavin. The pretreatment with rosavin downregulated the levels of tissue iron, malondialdehyde, and 4-hydroxynonenal, and increased the levels of glutathione. The expression of nuclear factor E2-related factor 2 (Nrf2) was upregulated by rosavin, together with other ferroptosis-related proteins. RSL3, a specific ferroptosis agonist, reversed the beneficial impact of rosavin. The network pharmacology approach predicted the activation of rosavin on the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) signaling pathway. LY294002, a potent PI3K inhibitor, decreased the upregulation of Nrf2 induced by rosavin. In conclusion, rosavin prevented lung injury induced by PM2.5 stimulation and suppressed ferroptosis via upregulating PI3K/Akt/Nrf2 signaling pathway.

Exploring potential inhibitors against Kyasanur forest disease by utilizing molecular dynamics simulations and ensemble docking

Kyasanur forest disease (KFD) is a tick-borne, neglected tropical disease, caused by KFD virus (KFDV) which belongs to Flavivirus (Flaviviridae family). This emerging viral disease is a major threat to humans. Currently, vaccination is the only controlling method against the KFDV, and its effectiveness is very low. An effective control strategy is required to combat this emerging tropical disease using the existing resources. In this regard, in silico drug repurposing method offers an effective strategy to find suitable antiviral drugs against KFDV proteins. Drug repurposing is an effective strategy to identify new use for approved or investigational drugs that are outside the scope of their initial usage and the repurposed drugs have lower risk and higher safety compared to de novo developed drugs, because their toxicity and safety issues are profoundly investigated during the preclinical trials in human/other models. In the present work, we evaluated the effectiveness of the FDA approved and natural compounds against KFDV proteins using in silico molecular docking and molecular simulations. At present, no experimentally solved 3D structures for the KFD viral proteins are available in Protein Data Bank and hence their homology model was developed and used for the analysis. The present analysis successfully developed the reliable homology model of NS3 of KFDV, in terms of geometry and energy contour. Further, in silico molecular docking and molecular dynamics simulations successfully presented four FDA approved drugs and one natural compound against the NS3 homology model of KFDV. Communicated by Ramaswamy H. Sarma.

Virus-CKB 2.0: Viral-Associated Disease-Specific Chemogenomics Knowledgebase

Transmissible and infectious viruses can cause large-scale epidemics around the world. This is because the virus can constantly mutate and produce different variants and subvariants to counter existing treatments. Therefore, a variety of treatments are urgently needed to keep up with the mutation of the viruses. To facilitate the research of such treatment, we updated our Virus-CKB 1.0 to Virus-CKB 2.0, which contains 10 kinds of viruses, including enterovirus, dengue virus, hepatitis C virus, Zika virus, herpes simplex virus, Andes orthohantavirus, human immunodeficiency virus, Ebola virus, Lassa virus, influenza virus, coronavirus, and norovirus. To date, Virus-CKB 2.0 archived at least 65 antiviral drugs (such as remdesivir, telaprevir, acyclovir, boceprevir, and nelfinavir) in the market, 178 viral-related targets with 292 available 3D crystal or cryo-EM structures, and 3766 chemical agents reported for these target proteins. Virus-CKB 2.0 is integrated with established tools for target prediction and result visualization; these include HTDocking, TargetHunter, blood-brain barrier (BBB) predictor, Spider Plot, etc. The Virus-CKB 2.0 server is accessible at https://www.cbligand.org/g/virus-ckb. By using the established chemogenomic tools and algorithms and newly developed tools, we can screen FDA-approved drugs and chemical compounds that may bind to these proteins involved in viral-associated disease regulation. If the virus strain mutates and the vaccine loses its effect, we can still screen drugs that can be used to treat the mutated virus in a fleeting time. In some cases, we can even repurpose FDA-approved drugs through Virus-CKB 2.0.

In Vitro Antibacterial Activity and in Silico Analysis of the Bioactivity of Major Compounds Obtained from the Essential Oil of Virola surinamensis Warb (Myristicaceae)

Essential oils are well known for their antimicrobial activity and they are used as an effective food preservative. Virola is one of the five genera of Myristicaceae and this genus is native to the American continent, especially in neotropical regions. The largest number of species of this genus is found in the Amazon region and the most important species include Virola surinamensis Warb. and Virola sebifera Aubl. In the present study, we describe the chemical composition of the essential oil of the V. surinamensis obtained at two different periods of the day in two seasons (rainy and dry), as well as their antimicrobial activity against pathogenic bacterial strains of Pseudomonas aeruginosa, Escherichia coli, and Staphylococcus aureus. In addition, we investigated, using in silico tools, the antimicrobial activity of the major chemical compounds present in the essential oil of V. surinamensis. The samples collected at different seasons and times showed a similar chemical profile, characterized by the major constituents α-pinene (>33%) and β-pinene (>13%). The essential oil of V. surinamensis showed an interesting antibacterial activity, exhibiting low inhibitory concentrations against the tested bacterial species. The computational investigation indicated that limonene, myrcene, and β-pinene could be related to the antibacterial activity against the tested pathogenic bacterial strains. Our results shed light on the possible constituents of essential oil that could be related to its activity against bacterial species and might be useful for further experimental tests that aim to discover new potential antibacterial agents for food preservation.

Whole proteome mapping of compound-protein interactions

Off-target binding is one of the primary causes of toxic side effects of drugs in clinical development, resulting in failures of clinical trials. While off-target drug binding is a known phenomenon, experimental identification of the undesired protein binders can be prohibitively expensive due to the large pool of possible biological targets. Here, we propose a new strategy combining chemical similarity principle and deep learning to enable proteome-wide mapping of compound-protein interactions. We have developed a pipeline to identify the targets of bioactive molecules by matching them with chemically similar annotated "bait" compounds and ranking them with deep learning. We have constructed a user-friendly web server for drug-target identification based on chemical similarity (DRIFT) to perform searches across annotated bioactive compound datasets, thus enabling high-throughput, multi-ligand target identification, as well as chemical fragmentation of target-binding moieties.

Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives

Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.

Notch-mediated lactate metabolism regulates MDSC development through the Hes1/MCT2/c-Jun axis

Myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs) play critical roles in tumorigenesis. However, the mechanisms underlying MDSC and TAM development and function remain unclear. In this study, we find that myeloid-specific activation of Notch/RBP-J signaling downregulates lactate transporter MCT2 transcription via its downstream molecule Hes1, leading to reduced intracellular lactate levels, blunted granulocytic MDSC (G-MDSC) differentiation, and enhanced TAM maturation. We identify c-Jun as a novel intracellular sensor of lactate in myeloid cells using liquid-chromatography-mass spectrometry (LC-MS) followed by CRISPR-Cas9-mediated gene disruption. Meanwhile, lactate interacts with c-Jun to protect from FBW7 ubiquitin-ligase-mediated degradation. Activation of Notch signaling and blockade of lactate import repress tumor progression by remodeling myeloid development. Consistently, the relationship between the Notch-MCT2/lactate-c-Jun axis in myeloid cells and tumorigenesis is also confirmed in clinical lung cancer biopsies. Taken together, our current study shows that lactate metabolism regulated by activated Notch signaling might participate in MDSC differentiation and TAM maturation.

Computational Methods for Drug Repurposing

The wealth of knowledge and multi-omics data available in drug research has allowed the rise of several computational methods in the drug discovery field, resulting in a novel and exciting strategy called drug repurposing. Drug repurposing consists in finding new applications for existing drugs. Numerous computational methods perform a high-level integration of different knowledge sources to facilitate the discovery of unknown mechanisms. In this chapter, we present a survey of data resources and computational tools available for drug repositioning.

wSDTNBI: a novel network-based inference method for virtual screening

In recent years, the rapid development of network-based methods for the prediction of drug-target interactions (DTIs) provides an opportunity for the emergence of a new type of virtual screening (VS), namely, network-based VS. Herein, we reported a novel network-based inference method named wSDTNBI. Compared with previous network-based methods that use unweighted DTI networks, wSDTNBI uses weighted DTI networks whose edge weights are correlated with binding affinities. A two-pronged approach based on weighted DTI and drug-substructure association networks was employed to calculate prediction scores. To show the practical value of wSDTNBI, we performed network-based VS on retinoid-related orphan receptor γt (RORγt), and purchased 72 compounds for experimental validation. Seven of the purchased compounds were confirmed to be novel RORγt inverse agonists by in vitro experiments, including ursonic acid and oleanonic acid with IC50 values of 10 nM and 0.28 μM, respectively. Moreover, the direct contact between ursonic acid and RORγt was confirmed using the X-ray crystal structure, and in vivo experiments demonstrated that ursonic acid and oleanonic acid have therapeutic effects on multiple sclerosis. These results indicate that wSDTNBI might be a powerful tool for network-based VS in drug discovery.

Target Characterization of Kaempferol against Myocardial Infarction Using Novel In Silico Docking and DARTS Prediction Strategy

Target identification is a crucial process for advancing natural products and drug leads development, which is often the most challenging and time-consuming step. However, the putative biological targets of natural products obtained from traditional prediction studies are also informatively redundant. Thus, how to precisely identify the target of natural products is still one of the major challenges. Given the shortcomings of current target identification methodologies, herein, a novel in silico docking and DARTS prediction strategy was proposed. Concretely, the possible molecular weight was detected by DARTS method through examining the protected band in SDS-PAGE. Then, the potential targets were obtained from screening and identification through the PharmMapper Server and TargetHunter method. In addition, the candidate target Src was further validated by surface plasmon resonance assay, and the anti-apoptosis effects of kaempferol against myocardial infarction were further confirmed by in vitro and in vivo assays. Collectively, these results demonstrated that the integrated strategy could efficiently characterize the targets, which may shed a new light on target identification of natural products.

Validation strategies for target prediction methods

Computational methods for target prediction, based on molecular similarity and network-based approaches, machine learning, docking and others, have evolved as valuable and powerful tools to aid the challenging task of mode of action identification for bioactive small molecules such as drugs and drug-like compounds. Critical to discerning the scope and limitations of a target prediction method is understanding how its performance was evaluated and reported. Ideally, large-scale prospective experiments are conducted to validate the performance of a model; however, this expensive and time-consuming endeavor is often not feasible. Therefore, to estimate the predictive power of a method, statistical validation based on retrospective knowledge is commonly used. There are multiple statistical validation techniques that vary in rigor. In this review we discuss the validation strategies employed, highlighting the usefulness and constraints of the validation schemes and metrics that are employed to measure and describe performance. We address the limitations of measuring only generalized performance, given that the underlying bioactivity and structural data are biased towards certain small-molecule scaffolds and target families, and suggest additional aspects of performance to consider in order to produce more detailed and realistic estimates of predictive power. Finally, we describe the validation strategies that were employed by some of the most thoroughly validated and accessible target prediction methods.

In silico methods and tools for drug discovery

In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in silico approaches have been attracting considerable interest because of their potential to accelerate drug discovery in terms of time, labor, and costs. Many new drug compounds have been successfully developed using computational methods. In this review, we briefly introduce computational drug discovery strategies and outline up-to-date tools to perform the strategies as well as available knowledge bases for those who develop their own computational models. Finally, we introduce successful examples of anti-bacterial, anti-viral, and anti-cancer drug discoveries that were made using computational methods.

The Synergistic Effect of Ginkgo biloba Extract 50 and Aspirin Against Platelet Aggregation

Purpose

We aimed to investigate potential synergistic antiplatelet effects of Ginkgo biloba extract (GBE50) in combination with aspirin using in vitro models.

Methods

Arachidonic acid (AA), platelet activating factor (PAF), adenosine 5'-diphosphate (ADP) and collagen were used as inducers. The antiplatelet effects of GBE50, aspirin and 1:1 combination of GBE50 and aspirin were detected by microplate method using rabbit platelets. Synergy finder 2.0 was used to analyze the synergistic antiplatelet effect. The compounds in GBE50 were identified by UPLC-Q/TOF-MS analysis and the candidate compounds were screened by TCMSP database. The targets of candidate compounds and aspirin were obtained in TCMSP, CCGs, Swiss target prediction database and drugbank. Targets involving platelet aggregation were obtained from GenCLiP database. Compound-target network was constructed and GO and KEGG enrichment analyses were performed to identify the critical biological processes and signaling pathways. The levels of thromboxane B2 (TXB2), cyclic adenosine monophosphate (cAMP) and PAF receptor (PAFR) were detected by ELISA to determine the effects of GBE50, aspirin and their combination on these pathways.

Results

GBE50 combined with aspirin inhibited platelet aggregation more effectively. The combination displayed synergistic antiplatelet effects in AA-induced platelet aggregation, and additive antiplatelet effects occurred in PAF, ADP and collagen induced platelet aggregation. Seven compounds were identified as candidate compounds in GBE50. Enrichment analyses revealed that GBE50 could interfere with platelet aggregation via cAMP pathway, AA metabolism and calcium signaling pathway, and aspirin could regulate platelet aggregation through AA metabolism and platelet activation. ELISA experiments showed that GBE50 combined with aspirin could increase cAMP levels in resting platelets, and decreased the levels of TXB2 and PAFR.

Conclusion

Our study indicated that GBE50 combined with aspirin could enhance the antiplatelet effects. They exerted both synergistic and additive effects in restraining platelet aggregation. The study highlighted the potential application of GBE50 as a supplementary therapy to treat thrombosis-related diseases.

Recent Advances in In Silico Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

Structurally diverse diterpenoid alkaloids from the lateral roots of Aconitum carmichaelii Debx. and their anti-tumor activities based on in vitro systematic evaluation and network pharmacology analysis

Thirty-seven diterpenoid alkaloids (DAs) with diverse structures were isolated and identified from the lateral roots of Aconitum carmichaelii Debx., comprising eight C20-DAs and twenty-nine C19-DAs. Besides the 31 known DAs identified by comparing the 1H NMR and 13C NMR data with those reported in the literature, the structures of four new compounds (1, 14, 17, and 25), and two other compounds (26 and 37) which were reported to be synthesized previously, were also elucidated based on the comprehensive analysis of their HR-ESI-MS, 1D and 2D NMR spectra, including 1H-1H COSY, HSQC and HMBC and NOESY/ROESY. Among them, compound 1 represents the first example of a C20-DA glucoside. Besides, the anti-tumor activities of all the isolated compounds against human non-small-cell lung cancer A549 and H460 cells were systematically evaluated by MTT methods. The results revealed that all of the C19-DAs possessed moderate activities against both of the two cell lines with IC50 values ranging from 7.97 to 28.42 μM, and their structure-activity relationships indicated the active sites of C-8, C-10, and C-14 positions and the nitrogen atom in the C19-DA skeleton. In addition, all of the isolated DAs, with chemical structures confirmed, were further applied for network pharmacology analysis, in order to give an insight into the possible mechanisms of their anti-tumor activities. As a result, 173 potential targets and three most important pathways related to non-small-cell lung carcinoma were finally unearthed.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

LigTMap: ligand and structure-based target identification and activity prediction for small molecular compounds

Target prediction is a crucial step in modern drug discovery. However, existing experimental approaches to target prediction are time-consuming and costly. Here, we introduce LigTMap, an online server with a fully automated workflow that can identify protein targets of chemical compounds among 17 classes of therapeutic proteins extracted from the PDBbind database. It combines ligand similarity search with docking and binding similarity analysis to predict putative targets. In the validation experiment of 1251 compounds, targets were successfully predicted for more than 70% of the compounds within the top-10 list. The performance of LigTMap is comparable to the current best servers SwissTargetPrediction and SEA. When testing with our newly compiled compounds from recent literature, we get improved top 10 success rate (66% ours vs. 60% SwissTargetPrediction and 64% SEA) and similar top 1 success rate (45% ours vs. 51% SwissTargetPrediction and 41% SEA). LigTMap directly provides ligand docking structures in PDB format, so that the results are ready for further structural studies in computer-aided drug design and drug repurposing projects. The LigTMap web server is freely accessible at https://cbbio.online/LigTMap. The source code is released on GitHub (https://github.com/ShirleyWISiu/LigTMap) under the BSD 3-Clause License to encourage re-use and further developments.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

GGL-Tox: Geometric Graph Learning for Toxicity Prediction

Toxicity analysis is a major challenge in drug design and discovery. Recently significant progress has been made through machine learning due to its accuracy, efficiency, and lower cost. US Toxicology in the 21st Century (Tox21) screened a large library of compounds, including approximately 12 000 environmental chemicals and drugs, for different mechanisms responsible for eliciting toxic effects. The Tox21 Data Challenge offered a platform to evaluate different computational methods for toxicity predictions. Inspired by the success of multiscale weighted colored graph (MWCG) theory in protein-ligand binding affinity predictions, we consider MWCG theory for toxicity analysis. In the present work, we develop a geometric graph learning toxicity (GGL-Tox) model by integrating MWCG features and the gradient boosting decision tree (GBDT) algorithm. The benchmark tests of the Tox21 Data Challenge are employed to demonstrate the utility and usefulness of the proposed GGL-Tox model. An extensive comparison with other state-of-the-art models indicates that GGL-Tox is an accurate and efficient model for toxicity analysis and prediction.

Antiulcer Potential of Olea europea L. cv. Arbequina Leaf Extract Supported by Metabolic Profiling and Molecular Docking

Gastric ulceration is among the most serious humanpublic health problems. Olea europea L. cv. Arbequina is one of the numerous olive varieties which have scarcely been studied. The reported antioxidant and anti-inflammatory potential of the olive plant make it a potential prophylactic natural product against gastric ulcers. Consequently, the main goal of this study is to investigate the gastroprotective effect of Olea europea L. cv. Arbequina leaf extract. LC-HRMS-based metabolic profiling of the alcoholic extract of Olea europea L. cv. Arbequina led to the dereplication of 18 putative compounds (1-18). In vivo indomethacin-induced gastric ulcer in a rat model was established and the Olea europea extract was tested at a dose of 300 mg kg-1 compared to cimetidine (100 mg kg-1). The assessment of gastric mucosal lesions and histopathology of gastric tissue was done. It has been proved that Olea europea significantly decreased the ulcer index and protected the mucosa from lesions. The antioxidant potential of the extract was evaluated using three in vitro assays, H2O2 scavenging, xanthine oxidase inhibitory, and superoxide radical scavenging activities and showed promising activities. Moreover, an in silico based study was performed on the putatively dereplicated compounds, which highlighted that 3-hydroxy tyrosol (4) and oleacein (18) can target the 5-lipoxygenase enzyme (5-LOX) as a protective mechanism against the pathogenesis of ulceration. Upon experimental validation, both compounds 3-hydroxy tyrosol (HT) and oleacein (OC) (4 and 18, respectively) exhibited a significant in vitro 5-LOX inhibitory activity with IC50 values of 8.6 and 5.8 µg/mL, respectively. The present study suggested a possible implication of O. europea leaves as a potential candidate having gastroprotective, antioxidant, and 5-LOX inhibitory activity for the management of gastric ulcers.

Structure-activity relationship and mechanistic studies for a series of cinnamyl hydroxamate histone deacetylase inhibitors

Histone deacetylases (HDACs) are a family of enzymes that modulate the acetylation status histones and non-histone proteins. Histone deacetylase inhibitors (HDACis) have emerged as an alternative therapeutic approach for the treatment of several malignancies. Herein, a series of urea-based cinnamyl hydroxamate derivatives is presented as potential anticancer HDACis. In addition, structure-activity relationship (SAR) studies have been performed in order to verify the influence of the linker on the biological profile of the compounds. All tested compounds demonstrated significant antiproliferative effects against solid and hematological human tumor cell lines. Among them, 11b exhibited nanomolar potency against hematological tumor cells including Jurkat and Namalwa, with IC₅₀ values of 40 and 200 nM, respectively. Cellular and molecular proliferation studies, in presence of compounds 11a-d, showed significant cell growth arrest, apoptosis induction, and up to 43-fold selective cytotoxicity for leukemia cells versus non-tumorigenic cells. Moreover, compounds 11a-d increased acetylated α-tubulin expression levels, which is phenotypically consistent with HDAC inhibition, and indirectly induced DNA damage. In vitro enzymatic assays performed for 11b revealed a potent HDAC6 inhibitory activity (IC₅₀: 8.1 nM) and 402-fold selectivity over HDAC1. Regarding SAR analysis, the distance between the hydroxamate moiety and the aromatic ring as well as the presence of the double bond in the cinnamyl linker were the most relevant chemical feature for the antiproliferative activity of the series. Molecular modeling studies suggest that cinnamyl hydroxamate is the best moiety of the series for binding HDAC6 catalytic pocket whereas exploration of Ser568 by the urea connecting unity (CU) might be related with the selectivity observed for the cinnamyl derivatives. In summary, cinnamyl hydroxamate derived compounds with HDAC6 inhibitory activity exhibited cell growth arrest and increased apoptosis, as well as selectivity to acute lymphoblastic leukemia cells. This study explores interesting compounds to fight against neoplastic hematological cells.

Epigenetic Target Fishing with Accurate Machine Learning Models

Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents many structure-activity relationships that have not been exploited thus far to develop predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26 318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. We built predictive models with high accuracy for small molecules' epigenetic target profiling through a systematic comparison of the machine learning models trained on different molecular fingerprints. The models were thoroughly validated, showing mean precisions of up to 0.952 for the epigenetic target prediction task. Our results indicate that the models reported herein have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as a freely accessible web application.

Virus-CKB: an integrated bioinformatics platform and analysis resource for COVID-19 research

Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is an urgent need for medicines that can help before vaccines are available. In this study, we present a viral-associated disease-specific chemogenomics knowledgebase (Virus-CKB) and apply our computational systems pharmacology-target mapping to rapidly predict the FDA-approved drugs which can quickly progress into clinical trials to meet the urgent demand of the COVID-19 outbreak. Virus-CKB reuses the underlying platform of our DAKB-GPCRs but adds new features like multiple-compound support, multi-cavity protein support and customizable symbol display. Our one-stop computing platform describes the chemical molecules, genes and proteins involved in viral-associated diseases regulation. To date, Virus-CKB archived 65 antiviral drugs in the market, 107 viral-related targets with 189 available 3D crystal or cryo-EM structures and 2698 chemical agents reported for these target proteins. Moreover, Virus-CKB is implemented with web applications for the prediction of the relevant protein targets and analysis and visualization of the outputs, including HTDocking, TargetHunter, BBB predictor, NGL Viewer, Spider Plot, etc. The Virus-CKB server is accessible at https://www.cbligand.org/g/virus-ckb.

MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs

Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, or 2019-nCoV), there is an urgent need to identify therapeutics that are effective against COVID-19 before vaccines are available. Since the current rate of SARS-CoV-2 knowledge acquisition via traditional research methods is not sufficient to match the rapid spread of the virus, novel strategies of drug discovery for SARS-CoV-2 infection are required. Structure-based virtual screening for example relies primarily on docking scores and does not take the importance of key residues into consideration, which may lead to a significantly higher incidence rate of false-positive results. Our novel in silico approach, which overcomes these limitations, can be utilized to quickly evaluate FDA-approved drugs for repurposing and combination, as well as designing new chemical agents with therapeutic potential for COVID-19. As a result, anti-HIV or antiviral drugs (lopinavir, tenofovir disoproxil, fosamprenavir and ganciclovir), antiflu drugs (peramivir and zanamivir) and an anti-HCV drug (sofosbuvir) are predicted to bind to 3CLPro in SARS-CoV-2 with therapeutic potential for COVID-19 infection by our new protocol. In addition, we also propose three antidiabetic drugs (acarbose, glyburide and tolazamide) for the potential treatment of COVID-19. Finally, we apply our new virus chemogenomics knowledgebase platform with the integrated machine-learning computing algorithms to identify the potential drug combinations (e.g. remdesivir+chloroquine), which are congruent with ongoing clinical trials. In addition, another 10 compounds from CAS COVID-19 antiviral candidate compounds dataset are also suggested by Molecular Complex Characterizing System with potential treatment for COVID-19. Our work provides a novel strategy for the repurposing and combinations of drugs in the market and for prediction of chemical candidates with anti-COVID-19 potential.

Artificial intelligence and machine learning could support drug development for hepatitis A virus internal ribosomal entry sites

Hepatitis A virus (HAV) infection is still an important health issue worldwide. Although several effective HAV vaccines are available, it is difficult to perform universal vaccination in certain countries. Therefore, it may be better to develop antivirals against HAV for the prevention of severe hepatitis A. We found that several drugs potentially inhibit HAV internal ribosomal entry site-dependent translation and HAV replication. Artificial intelligence and machine learning could also support screening of anti-HAV drugs, using drug repositioning and drug rescue approaches.

Drug Repurposing: An Emerging Tool for Drug Reuse, Recycling and Discovery

Drug repositioning or repurposing is a revolutionary breakthrough in drug development that focuses on rediscovering new uses for old therapeutic agents. Drug repositioning can be defined more precisely as the process of exploring new indications for an already approved drug while drug repurposing includes overall re-development approaches grounded in the identical chemical structure of the active drug moiety as in the original product. The repositioning approach accelerates the drug development process, curtails the cost and risk inherent to drug development. The strategy focuses on the polypharmacology of drugs to unlocks novel opportunities for logically designing more efficient therapeutic agents for unmet medical disorders. Drug repositioning also expresses certain regulatory challenges that hamper its further utilization. The review outlines the eminent role of drug repositioning in new drug discovery, methods to predict the molecular targets of a drug molecule, advantages that the strategy offers to the pharmaceutical industries, explaining how the industrial collaborations with academics can assist in the discovering more repositioning opportunities. The focus of the review is to highlight the latest applications of drug repositioning in various disorders. The review also includes a comparison of old and new therapeutic uses of repurposed drugs, assessing their novel mechanisms of action and pharmacological effects in the management of various disorders. Various restrictions and challenges that repurposed drugs come across during their development and regulatory phases are also highlighted.

COVID19 Drug Repository: text-mining the literature in search of putative COVID19 therapeutics

The recent outbreak of COVID-19 has generated an enormous amount of Big Data. To date, the COVID-19 Open Research Dataset (CORD-19), lists ∼130,000 articles from the WHO COVID-19 database, PubMed Central, medRxiv, and bioRxiv, as collected by Semantic Scholar. According to LitCovid (11 August 2020), ∼40,300 COVID19-related articles are currently listed in PubMed. It has been shown in clinical settings that the analysis of past research results and the mining of available data can provide novel opportunities for the successful application of currently approved therapeutics and their combinations for the treatment of conditions caused by a novel SARS-CoV-2 infection. As such, effective responses to the pandemic require the development of efficient applications, methods and algorithms for data navigation, text-mining, clustering, classification, analysis, and reasoning. Thus, our COVID19 Drug Repository represents a modular platform for drug data navigation and analysis, with an emphasis on COVID-19-related information currently being reported. The COVID19 Drug Repository enables users to focus on different levels of complexity, starting from general information about (FDA-) approved drugs, PubMed references, clinical trials, recipes as well as the descriptions of molecular mechanisms of drugs' action. Our COVID19 drug repository provide a most updated world-wide collection of drugs that has been repurposed for COVID19 treatments around the world.

Effect of capsaicin on the protozoan parasite Trypanosoma cruzi

Trypanosoma cruzi is the causative agent of Chagas disease. There are only two approved treatments, both of them unsuitable for the chronic phase, therefore the development of new drugs is a priority. Trypanosoma cruzi arginine kinase (TcAK) is a promising drug target since it is absent in humans and it is involved in cellular stress responses. In a previous study, possible TcAK inhibitors were identified through computer simulations resulting the best compounds capsaicin and cyanidin derivatives. Here, we evaluate the effect of capsaicin on TcAK activity and its trypanocidal effect. Although capsaicin produced a weak enzyme inhibition, it had a strong trypanocidal effect on epimastigotes and trypomastigotes (IC50 = 6.26 µM and 0.26 µM, respectively) being 20-fold more active on trypomastigotes than mammalian cells. Capsaicin was also active on the intracellular cycle reducing by half the burst of trypomastigotes at approximately 2 µM. Considering the difference between the concentrations at which parasite death and TcAK inhibition occur, other possible targets were predicted. Capsaicin is a selective trypanocidal agent active in nanomolar concentrations, with an IC50 57-fold lower than benznidazole, the drug currently used for treating Chagas disease.

Bioactivity Comparison across Multiple Machine Learning Algorithms Using over 5000 Datasets for Drug Discovery

Machine learning methods are attracting considerable attention from the pharmaceutical industry for use in drug discovery and applications beyond. In recent studies, we and others have applied multiple machine learning algorithms and modeling metrics and, in some cases, compared molecular descriptors to build models for individual targets or properties on a relatively small scale. Several research groups have used large numbers of datasets from public databases such as ChEMBL in order to evaluate machine learning methods of interest to them. The largest of these types of studies used on the order of 1400 datasets. We have now extracted well over 5000 datasets from CHEMBL for use with the ECFP6 fingerprint and in comparison of our proprietary software Assay Central with random forest, k-nearest neighbors, support vector classification, naïve Bayesian, AdaBoosted decision trees, and deep neural networks (three layers). Model performance was assessed using an array of fivefold cross-validation metrics including area-under-the-curve, F1 score, Cohen's kappa, and Matthews correlation coefficient. Based on ranked normalized scores for the metrics or datasets, all methods appeared comparable, while the distance from the top indicated that Assay Central and support vector classification were comparable. Unlike prior studies which have placed considerable emphasis on deep neural networks (deep learning), no advantage was seen in this case. If anything, Assay Central may have been at a slight advantage as the activity cutoff for each of the over 5000 datasets representing over 570,000 unique compounds was based on Assay Central performance, although support vector classification seems to be a strong competitor. We also applied Assay Central to perform prospective predictions for the toxicity targets PXR and hERG to further validate these models. This work appears to be the largest scale comparison of these machine learning algorithms to date. Future studies will likely evaluate additional databases, descriptors, and machine learning algorithms and further refine the methods for evaluating and comparing such models.

Enhancing Chemogenomics with Predictive Pharmacology

One of the grand challenges in contemporary chemical biology is the generation of a probe for every member of the human proteome. Probe selection and optimization strategies typically rely on experimental bioactivity data to determine the potency and selectivity of candidate molecules. However, this approach is profoundly limited by the sparsity of the known data, the annotation bias often found in the literature, and the cost of physical screening. Recent advancements in predictive pharmacology, such as the application of multitask and transfer learning, as well as the use of biologically motivated, structure-agnostic features to characterize molecules, should serve to mitigate these issues. Computational modeling likely offers the only cost-effective approach to substantially increasing the bioactivity annotation density both on the local and global scale and thus, we argue, will need to make a substantial contribution if the ambitious goals of probing the human proteome are to be realized in the foreseeable future.

Pain-CKB, A Pain-Domain-Specific Chemogenomics Knowledgebase for Target Identification and Systems Pharmacology Research

A traditional single-target analgesic, though it may be highly selective and potent, may not be sufficient to mitigate pain. An alternative strategy for alleviation of pain is to seek simultaneous modulation at multiple nodes in the network of pain-signaling pathways through a multitarget analgesic or drug combinations. Here we present a comprehensive pain-domain-specific chemogenomics knowledgebase (Pain-CKB) with integrated computing tools for target identification and systems pharmacology research. Pain-CKB is constructed on the basis of our established chemogenomics technology with new features, including multiple compound support, multicavity protein support, and customizable symbol display. The determination of bioactivity is also revised to avoid the use of complex machine learning models. Our one-stop computing platform describes the chemical molecules, genes, and proteins involved in pain regulation. To date, Pain-CKB has archived 272 analgesics in the market, 84 pain-related targets with 207 available 3D crystal or cryo-EM structures, and 234 662 chemical agents reported for these target proteins. Moreover, Pain-CKB implements user-friendly web-interfaced computing tools and applications for the prediction and analysis of the relevant protein targets and visualization of the outputs, including HTDocking, TargetHunter, BBB permeation predictor, NGL viewer, Spider Plot, etc. The Pain-CKB server is accessible at https://www.cbligand.org/g/pain-ckb.