Prediction of Anti-Alzheimer's Activity of Flavonoids Targeting Acetylcholinesterase in silico

R software for QSAR analysis in phytopharmacological studies

INTRODUCTION

In recent decades, quantitative structure-activity relationship (QSAR) analysis has become an important method for drug design and natural product research. With the availability of bioinformatic and cheminformatic tools, a vast number of descriptors have been generated, making it challenging to select potential independent variables that are accurately related to the dependent response variable.

OBJECTIVE

The objective of this study is to demonstrate various descriptor selection procedures, such as the Boruta approach, all subsets regression, the ANOVA approach, the AIC method, stepwise regression, and genetic algorithm, that can be used in QSAR studies. Additionally, we performed regression diagnostics using R software to test parameters such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity.

RESULTS

The workflow designed in this study highlights the different descriptor selection procedures and regression diagnostics that can be used in QSAR studies. The results showed that the Boruta approach and genetic algorithm performed better than other methods in selecting potential independent variables. The regression diagnostics parameters tested using R software, such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity, helped in identifying and diagnosing model errors, ensuring the reliability of the QSAR model.

CONCLUSION

QSAR analysis is vital in drug design and natural product research. To develop a reliable QSAR model, it is essential to choose suitable descriptors and perform regression diagnostics. This study offers an accessible, customizable approach for researchers to select appropriate descriptors and diagnose errors in QSAR studies.

Effect of acetone fraction of Ottelia alismoides on the G2/M cell cycle arrest and apoptosis in the human carcinoma cell lines

ETHNOPHARMACOLOGICAL RELEVANCE

The North-eastern parts of India have immense therapeutic floras, Ottelia alismoides is an aquatic plant that has been in use for a long time in traditional medicine for treating diseases like cancer, tuberculosis, diabetes, febrifuge, hemorrhoids, and rubefacient. In lung and skin carcinoma cells with a high rate of proliferation and metastasis including drug resistance and non-specific target activity, generates important challenges towards their treatment strategy. Thus, finding novel therapeutic targets to treat lung and skin cancer progression is essential to enhance the patients' survival with treatment.

AIM OF THE STUDY

The purpose of this study was to evaluate the apoptotic potential of acetone extract of O. alismoides (L.) Pers. (OA-AC) and to identify the compounds responsible for this effect, HRLC-MS-QTOF analysis of the extract has been undertaken along with in-silico molecular docking analysis of the identified compounds.

MATERIALS AND METHODS

A549 and A431 cells were treated with acetone extract of O. alismoides (OA-AC) at 24 h and 48 h exposure and cell cycle phase distribution was evaluated and also apoptosis induction activity was evaluated by OA-EtBr staining and Mitochondrial outer membrane potential assay. Western blotting was performed for the evaluation of apoptotic protein expression. At last, the HR-LCMS of OA-AC was analyzed to identify the compounds responsible for the apoptotic activity of the extract.

RESULTS

The cell cycle phase distribution analysis in A549 and A431 cells at 24hrs exposure with 10 μg/mL and 25 μg/mL of OA-AC showed a potent arrest or blockage at the G2/M phase of the cell cycle with reduced expression of cyclin B and p-Cdc2. At 48 h exposure, apoptosis was observed in these cancer cells with elevated expression of Bax, p21 and cleaved caspase 3 and reduced expression of the Bcl2.

CONCLUSION

AO-EtBr staining of these cancer cells reveals that the death induced by OA-AC was apoptotic in nature with depolarization of mitochondrial membrane due to loss or damage of the mitochondrial membrane. The HRLC-MS-QTOF analysis of OA-AC depicted 14 major isolable compounds and molecular docking analysis displayed 4 compounds that might act as an inhibitor of cyclin B for G2/M phase arrest that leads to apoptotic induction in the cells.

Flavonoids in vegetables: improvement of dietary flavonoids by metabolic engineering to promote health

Flavonoids are the most abundant polyphenols in plants, and have antioxidant effects as well as other bioactivities (e.g., anti-inflammatory, anti-cancer, anti-allergic, and neuroprotective effects). Vegetables are rich in flavonoids and are indispensable in our daily diet. Moreover, the vegetables as chassis for producing natural products would emerge as a promising means for cost-effective and sustainable production of flavonoids. Understanding the metabolic engineering of flavonoids in vegetables allows us to improve their nutrient composition. In this review, a comprehensive overview of flavonoids in vegetables, including the characterized types and distribution, health-promoting effects, associated metabolic pathways, and applied metabolic engineering are provided. We also introduce breakthroughs in multi-omics approaches that pertain to the elucidation of flavonoids metabolism in vegetables, as well as prospective and potential genome-editing technologies. Based on the varied composition and content of flavonoids among vegetables, dietary suggestions are further provided for human health.

Protective Effects of Flavonoids against Alzheimer's Disease: Pathological Hypothesis, Potential Targets, and Structure-Activity Relationship

Alzheimer’s disease (AD) is a neurodegenerative disease with high morbidity and mortality, for which there is no available cure. Currently, it is generally believed that AD is a disease caused by multiple factors, such as amyloid-beta accumulation, tau protein hyperphosphorylation, oxidative stress, and inflammation. Multitarget prevention and treatment strategies for AD are recommended. Interestingly, naturally occurring dietary flavonoids, a class of polyphenols, have been reported to have multiple biological activities and anti-AD effects in several AD models owing to their antioxidative, anti-inflammatory, and anti-amyloidogenic properties. In this review, we summarize and discuss the existing multiple pathogenic factors of AD. Moreover, we further elaborate on the biological activities of natural flavonoids and their potential mode of action and targets in managing AD by presenting a wide range of experimental evidence. The gathered data indicate that flavonoids can be regarded as prophylactics to slow the advancement of AD or avert its onset. Different flavonoids have different activities and varying levels of activity. Further, this review summarizes the structure–activity relationship of flavonoids based on the existing literature and can provide guidance on the design and selection of flavonoids as anti-AD drugs.

Metabolomic Analysis Reveals Nutritional Diversity among Three Staple Crops and Three Fruits

More than 2 billion people worldwide are under threat of nutritional deficiency. Thus, an in-depth comprehension of the nutritional composition of staple crops and popular fruits is essential for health. Herein, we performed LC-MS-based non-targeted and targeted metabolome analyses with crops (including wheat, rice, and corn) and fruits (including grape, banana, and mango). We detected a total of 2631 compounds by using non-targeted strategy and identified more than 260 nutrients. Our work discovered species-dependent accumulation of common present nutrients in crops and fruits. Although rice and wheat lack vitamins and amino acids, sweet corn was rich in most amino acids and vitamins. Among the three fruits, mango had more vitamins and amino acids than grape and banana. Grape and banana provided sufficient 5-methyltetrahydrofolate and vitamin B6, respectively. Moreover, rice and grape had a high content of flavonoids. In addition, the three crops contained more lipids than fruits. Furthermore, we also identified species-specific metabolites. The crops yielded 11 specific metabolites, including flavonoids, lipids, and others. Meanwhile, most fruit-specific nutrients were flavonoids. Our work discovered the complementary pattern of essential nutrients in crops and fruits, which provides metabolomic evidence for a healthy diet.

Molecular docking analysis of flupenthixol and desmethylastemizole with the apoptotic regulator proteins CFLAR and TRAF2 linked to lung carcinoma

It is known that molecular changes in apoptotic genes due to mutation may cause disruption of apoptotic pathway resulting in an abrupt increase in cell proliferation. Therefore, it is of interest to identify compounds that could potentially replenish the changes in the apoptotic pathway, resulted from mutation. The gene network analysis using the Network Analyzer Plugin of Cytoscape (3.5.1) shows CFLAR and TRAF2 as influential genes in the apoptotic pathway. Mutation in these genes brings loss in apoptotic property of a cell and thus increases the cell proliferating activity. Thus, data on the molecular docking analysis of four natural compounds from Ottelia alismoides (L.) Pers with the two target proteins were reported. Flupenthixol and desmethylastemizole was found to be two efficient ligand molecules based on ligand-target interaction. In stereochemical quality assessment, the Ramachandran plot analysis of receptors indicates the better stereochemical characteristics for receptor-ligand interaction.

Eugenol and capsaicin exhibit anti-metastatic activity via modulating TGF-β signaling in gastric carcinoma

The transforming growth factor-β (TGF-β) signaling is considered to be a key player in gastric cancer metastasis, and the inhibition of the TGF-β/SMAD4 signaling pathway may be a novel strategy for therapeutic interventions in cancer. Here, the anti-metastatic activity of two phytochemicals, eugenol and capsaicin, has been studied, and their potential to antagonize TGF-β has been investigated in gastric cancer cells. Both the phytochemicals exhibited anti-metastatic activity by inhibiting the TGF-β signaling pathway independent of P21 or P53, with capsaicin proving to be more potent than eugenol. However, unlike eugenol, the inhibitory effect of capsaicin on the TGF-β signaling pathway and metastasis was found to be dependent on SMAD4, which was validated in SMAD4-knocked down AGS cell and SMAD4-null SW620 cell line. Furthermore, the use of recombinant TGF-β and TGF-β receptor inhibitor LY2109761 confirmed that the anti-metastatic activity of eugenol is partially and that of capsaicin is principally mediated through the TGF-β signaling pathway. Identifying phytochemicals with the potential to inhibit cancer metastasis by targeting the TGF-β signaling pathway has immense scope for developing a therapeutic strategy against cancer metastasis.

LC-MS/MS Screening of Phenolic Compounds in Wild and Cultivated Grapes Vitis amurensis Rupr

This work represents a comparative metabolomic study of extracts of wild grapes obtained from six different places in the Primorsky and Khabarovsk territories (Far East Russia) and extracts of grapes obtained from the collection of N.I. Vavilov All-Russian Institute of Plant Genetic Resources (St. Petersburg). The metabolome analysis was performed by liquid chromatography in combination with ion trap mass spectrometry. The results showed the presence of 118 compounds in ethanolic extracts of V. amurensis grapes. In addition, several metabolites were newly annotated in V. amurensis. The highest diversity of phenolic compounds was identified in the samples of the V. amurensis grape collected in the vicinity of Vyazemsky (Khabarovsk Territory) and the floodplain of the Arsenyevka River (Primorsky Territory), compared to the other wild samples and cultural grapes obtained in the collection of N.I. Vavilov All-Russian Institute of Plant Genetic Resources.

Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer's Diseases via Pharmacophore Modeling, Auto-QSAR, and Molecular Docking Approaches

Neurodegenerative diseases, for example Alzheimer’s, are perceived as driven by hereditary, cellular, and multifaceted biochemical actions. Numerous plant products, for example flavonoids, are documented in studies for having the ability to pass the blood-brain barrier and moderate the development of such illnesses. Computer-aided drug design (CADD) has achieved importance in the drug discovery world; innovative developments in the aspects of structure identification and characterization, bio-computational science, and molecular biology have added to the preparation of new medications towards these ailments. In this study we evaluated nine flavonoid compounds identified from three medicinal plants, namely T. diversifolia, B. sapida, and I. gabonensis for their inhibitory role on acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and monoamine oxidase (MAO) activity, using pharmacophore modeling, auto-QSAR prediction, and molecular studies, in comparison with standard drugs. The results indicated that the pharmacophore models produced from structures of AChE, BChE and MAO could identify the active compounds, with a recuperation rate of the actives found near 100% in the complete ranked decoy database. Moreso, the robustness of the virtual screening method was accessed by well-established methods including enrichment factor (EF), receiver operating characteristic curve (ROC), Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), and area under accumulation curve (AUAC). Most notably, the compounds’ pIC₅₀ values were predicted by a machine learning-based model generated by the AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best models achieved for AChE, BChE and MAO were models kpls_radial_17 (R² = 0.86 and Q² = 0.73), pls_38 (R² = 0.77 and Q² = 0.72), kpls_desc_44 (R² = 0.81 and Q² = 0.81) and these externally validated models were utilized to predict the bioactivities of the lead compounds. The binding affinity results of the ligands against the three selected targets revealed that luteolin displayed the highest affinity score of −9.60 kcal/mol, closely followed by apigenin and ellagic acid with docking scores of −9.60 and −9.53 kcal/mol, respectively. The least binding affinity was attained by gallic acid (−6.30 kcal/mol). The docking scores of our standards were −10.40 and −7.93 kcal/mol for donepezil and galanthamine, respectively. The toxicity prediction revealed that none of the flavonoids presented toxicity and they all had good absorption parameters for the analyzed targets. Hence, these compounds can be considered as likely leads for drug improvement against the same.

Recent Research on Flavonoids and their Biomedical Applications

Flavonoids, commonly found in various plants, are a class of polyphenolic compounds having a basic structural unit of 2-phenylchromone. Flavonoid compounds have attracted much attention due to their wide biological applications. In order to facilitate further research on the biomedical application of flavonoids, we surveyed the literature published on the use of flavonoids in medicine during the past decade, documented the commonly found structures in natural flavonoids, and summarized their pharmacological activities as well as associated mechanisms of action against a variety of health disorders including chronic inflammation, cancer, cardiovascular complications and hypoglycemia. In this mini-review, we provide suggestions for further research on the biomedical applications of flavonoids.

Synthesis of novel 4-methylthiocoumarin and comparison with conventional coumarin derivative as a multi-target-directed ligand in Alzheimer's disease

Alzheimer's disease (AD) is a multifactorial disorder characterized by cognitive deficit and memory loss. The pathological feature of the disease involves β-amyloid senile plaques, reduced levels of acetylcholine neurotransmitter, oxidative stress and neurofibrillary tangles formation within the brain of AD patients. The present study aims to screen the inhibitory activity of newly synthesized and existing novel 4-methylthiocoumarin derivative against acetylcholinesterase, butyrylcholinesterase, BACE1, β-amyloid aggregation and oxidative stress involved in the AD pathogenesis. The in vitro assays used in this study were Ellman's assay, FRET assays, Thioflavin T, transmission electron microscopy, circular dichroism, FRAP, and TEAC. Molecular docking and dynamics studies were performed to correlate the results. C3 and C7 (thiocoumarin derivatives) were found to be the most potent inhibitors of acetylcholinesterase (IC50-5.63 µM) and butyrylcholinesterase (IC50-3.40 µM) using Ellman's assays. Enzyme kinetic studies showed that C3 and C7 compounds followed by the mixed mode of inhibition using LB plot. C3 also moderately inhibited the BACE1 using FRET assay. C3 inhibited the fibrillization of β-amyloid peptides in a concentration-dependent manner as observed by Thioflavin T, TEM studies and Circular dichroism data. Molecular modeling studies were performed to understand the probable mode of binding of C3 and C7 in the binding pocket of acetylcholinesterase, butyrylcholinesterase, BACE1 and amyloid β peptides. This indicates the important role of hydrophobic interactions between C3 and acetylcholinesterase. C3 also exhibited significant antioxidant potential by FRAP and TEAC assays. Hence, C3 might serve as a promising lead for developing novel multi target-directed ligand for the treatment of AD.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Deciphering inhibitory activity of flavonoids against tau protein kinases: a coupled molecular docking and quantum chemical study

Today, Alzheimer's disease (AD) is one of the most important neurodegenerative disorders that affected millions of people worldwide. Hundreds of academic investigations highlighted the potential roles of natural metabolites in the cornerstone of AD prevention. Nevertheless, alkaloids are only metabolites that successfully showed promising clinical therapeutic effects on the prevention of AD. In this regard, other plant metabolites such as flavonoids are also considered as promising substances in the improvement of AD complications. The lack of data on molecular mode of action of flavonoids inside brain tissues, and their potential to transport across the blood-brain barrier, a physical hindrance between bloodstream and brain tissues, limited the large-scale application of these compounds for AD therapy programs. Herein, a coupled docking and quantum study was applied to determine the binding mode of flavonoids and three protein kinases involved in the pathogenesis of AD. The results suggested that all docked metabolites showed considerable binding affinity to interact with target receptors, but some compounds possessed higher binding energy values. Because docking simulation cannot entirely reveal the potential roles of ligand substructures in the interaction with target residues, quantum chemical analyses (QCAs) were performed to cover this drawback. Accordingly, QCAs determined that distribution of molecular orbitals have a pivotal function in the determination of the type of reaction between ligands and receptors; therefore, using such quantum chemical descriptors may correct the results of virtual docking outcomes to highlight promising backbones for further developments.Communicated by Ramaswamy H. Sarma.

Recent research progress on natural small molecule bibenzyls and its derivatives in Dendrobium species

Orchidaceous plant Dendrobium genus is often used as a tonic, and its phenolic components have attracted attention for its anti-tumor and anti-diabetic complications. Bibenzyls is one of the essential phenolic active ingredients in the Dendrobium genus. At present, 89 bibenzyl derivatives have been extracted and identified from 46 Dendrobium species. The activity studies have shown that 42 compounds have pharmaceutical activity. Among them, 23 compounds showed antitumor activity; 7 compounds showed anti-diabetes and its complications activity; 10 compounds exhibited neuroprotective effects; 18 compounds showed antioxidant effects; 11 compounds had anti-inflammatory activity; 3 compounds had Antiplatelet aggregation effects; 3 compounds had antibacterial and antiviral effects. The Bibenzyls is small-molecular compounds of natural origin and widely sourced. Previous studies showed that the bibenzyls has good anti-tumor, anti-diabetes and its complications, and neuroprotective effects, and it has great potential for treating tumors, diabetes and its complications, Alzheimer's disease (AD) and Parkinson's disease (PD). Additionally, compounds such as moscatilin (1), gigantol (2) and chrysotoxine (3) have been further studied as lead compounds, and compounds exhibited therapeutical effects had been synthesized. Enough pieces of evidences have shown that the Bibenzyls have good development prospects. This article reviews the pharmacological effects of bibenzyls in Dendrobium species and provides an idea for its further development.

Rationalization of a traditional liver medicine using systems biology approach and its evaluation in preclinical trial

'Bottom-up', i.e., molecule to medicine strategy for the discovery of new drugs takes enormous time and cost. In most of the cases, inherent toxicity and undesired side effects of the developed drug hinder its way beyond the early stages of development. In this regard, the systems pharmacology can play an excellent role by reducing the cost and time of drug development through rationalization and/or repurposing of traditional drugs with known side effects. In the present study, our aim was to develop an integrated systems biology method for the prediction of active ingredients of a traditional medicine and their potential targets inside the body. Further, we evaluated the predictive capacity of the developed method in a preclinical animal model. Here, we have prepared a formulation (SKP17LIV01) from an extract of eight medicinal plants traditionally used as liver medicine and identified the constituents using UHPLC-MS technique. Using systems biology approach, we have rationalized the components of the formulation for potential use in the treatment of heavy metal-induced hepatotoxicity. The active ingredients and potential therapeutic targets were also predicted. A detailed biochemical, histopathological and molecular study on the mice model of lead toxicity confirms the efficacy of the formulation as per prediction by the systems pharmacology approach. The study may open a new frontier for re-discovery of drugs that are already used in traditional medicine.

Computational Studies Applied to Flavonoids against Alzheimer's and Parkinson's Diseases

Neurodegenerative diseases, such as Parkinson's and Alzheimer's, are understood as occurring through genetic, cellular, and multifactor pathophysiological mechanisms. Several natural products such as flavonoids have been reported in the literature for having the capacity to cross the blood-brain barrier and slow the progression of such diseases. The present article reports on in silico enzymatic target studies and natural products as inhibitors for the treatment of Parkinson's and Alzheimer's diseases. In this study we evaluated 39 flavonoids using prediction of molecular properties and in silico docking studies, while comparing against 7 standard reference compounds: 4 for Parkinson's and 3 for Alzheimer's. Osiris analysis revealed that most of the flavonoids presented no toxicity and good absorption parameters. The Parkinson's docking results using selected flavonoids as compared to the standards with four proteins revealed similar binding energies, indicating that the compounds 8-prenylnaringenin, europinidin, epicatechin gallate, homoeriodictyol, capensinidin, and rosinidin are potential leads with the necessary pharmacological and structural properties to be drug candidates. The Alzheimer's docking results suggested that seven of the 39 flavonoids studied, being those with the best molecular docking results, presenting no toxicity risks, and having good absorption rates (8-prenylnaringenin, europinidin, epicatechin gallate, homoeriodictyol, aspalathin, butin, and norartocarpetin) for the targets analyzed, are the flavonoids which possess the most adequate pharmacological profiles.

Current Therapeutic Molecules and Targets in Neurodegenerative Diseases Based on in silico Drug Design

Abstract: Background

As the number of elderly persons increases, neurodegenerative diseases are becoming ubiquitous. There is currently a great need for knowledge concerning management of old-age neurodegenerative diseases; the most important of which are: Alzheimer’s disease, Parkinson’s disease, Amyotrophic Lateral Sclerosis, and Huntington’s disease.

Objective

To summarize the potential of computationally predicted molecules and targets against neurodegenerative diseases.

Method

Review of literature published since 1997 against neurodegenerative diseases, utilizing as keywords: in silico, Alzheimer’s disease, Parkinson’s disease, Amyotrophic Lateral Sclerosis ALS, and Huntington’s disease was conducted.

Results and Conclusion

Due to the costs associated with experimentation and current ethical law, performing experiments directly on living organisms has become much more difficult. In this scenario, in silico techniques have been successful and have become powerful tools in the search to cure disease. Researchers use the Computer Aided Drug Design pipeline which: 1) generates 3-dimensional structures of target proteins through homology modeling 2) achieves stabilization through molecular dynamics simulation, and 3) exploits molecular docking through large compound libraries. Next generation sequencing is continually producing enormous amounts of raw sequence data while neuroimaging is producing a multitude of raw image data. To solve such pressing problems, these new tools and algorithms are required. This review elaborates precise in silico tools and techniques for drug targets, active molecules, and molecular docking studies, together with future prospects and challenges concerning possible breakthroughs in Alzheimer’s, Parkinson’s, Amyotrophic Lateral Sclerosis, and Huntington’s disease.

Anti-Alzheimers activity and molecular mechanism of albumin-derived peptides against AChE and BChE

Alzheimer's disease (AD) is a global health issue affecting millions of elderly people worldwide. The aim of the present study was to identify novel anti-AD peptides isolated from albumin. Anti-AD activities of the peptides were evaluated via inhibitory activities on acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). Furthermore, the potential molecular mechanisms of the KLPGF/AChE were investigated by CDOCKER of Discovery studio 2017. The results revealed that peptide KLPGF could effectively inhibit AChE with an inhibition rate of 61.23% at a concentration of 50 μg mL^-1. In addition, the peptide KLPGF came in contact with acylation sites and peripheral anion sites of AChE. The present study demonstrates that the peptide KLPGF could become a potential functional food intervention in AD.

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.