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Gulf War Illness (GWI) is a chronic illness that affects upward of 32% of deployed Veterans to the 1991 Gulf War (GW). The symptoms are medically unexplained, ranging across cognitive deficits, fatigue, gastrointestinal problems, and musculoskeletal pain. Research indicates that chemical warfare agents play a key role in the onset and progression of GWI. The Khamisiyah ammunition storage that housed chemical warfare agents such as sarin, an acetylcholinesterase (AChE) inhibitor, was demolished during the GW, releasing toxicants into the atmosphere affecting deployed troops. Exposure to other chemical agents such as pyridostigmine bromide, N,N-diethyl-m-toluamide, permethrin and chlorpyrifos, were also prevalent during the war. These additional chemical agents have also been shown to inhibit AChE. AChE inhibition induces an acetylcholine build-up, disrupting signals between nerves and muscles, which in high doses leads to asphyxiation. Little is known about low dose exposure. As bioactive compounds tend to interact with multiple proteins with various physiological effect, we aimed to identify other potential shared targets to understand the extent in which these chemicals could lead to GWI. We followed a reverse screening approach where each chemical is computationally docked to a library of protein targets. The programs PharmMapper and TargetNet were used for this purpose, and further analyses were conducted to mark significant changes in participants with GWI. Previously published work on DNA methylation status in GWI was reanalyzed focusing specifically on the predicted shared targets indicating significant changes in DNA methylation of the associated genes. Our findings thus suggest that exposure to GWI-related agents may converge on similar targets with roles in inflammation, neurotransmitter and lipid metabolism, and detoxification which may have impacts on neurodegenerative-like disease and oxidative stress in Veterans with GWI.

The in silico study of medicinal plants is a rapidly growing field. Techniques such as reverse screening and network pharmacology are used to study the complex cellular action of medicinal plants against disease. However, it is difficult to produce a meaningful visualization of phytochemical-protein interactions (PCPIs) in the cell. This study introduces a novel workflow combining various tools to visualize a PCPI network for a medicinal plant against a disease. The five steps are 1) phytochemical compilation, 2) reverse screening, 3) network building, 4) network visualization, and 5) evaluation. The output is a PCPI network that encodes multiple dimensions of information, including subcellular location, phytochemical class, pharmacokinetic data, and prediction probability. As a proof of concept, we built a PCPI network for bitter gourd (Momordica charantia L.) against colorectal cancer. The network and workflow are available at https://yumibriones.github.io/network/. The PCPI network highlights high-confidence interactions for further in vitro or in vivo study. The overall workflow is broadly transferable and can be used to visualize the action of other medicinal plants or small molecules against other diseases.

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.

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

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.

Indicaxanthin is a bioactive and bioavailable betalain pigment extracted from Opuntia ficus indica fruits. Indicaxanthin has pharmacokinetic proprieties, rarely found in other phytochemicals, and it has been demonstrated that it provides a broad-spectrum of pharmaceutical activity, exerting anti-proliferative, anti-inflammatory, and neuromodulator effects. The discovery of the Indicaxanthin physiological targets plays an important role in understanding the biochemical mechanism. In this study, combined reverse pharmacophore mapping, reverse docking, and text-based database search identified Inositol Trisphosphate 3-Kinase (ITP3K-A), Glutamate carboxypeptidase II (GCPII), Leukotriene-A4 hydrolase (LTA4H), Phosphoserine phosphatase (HPSP), Phosphodiesterase 4D (PDE4D), AMPA receptor (GluA3 and GluA2 subunits) and Kainate receptor (GluK1 isoform) as potential targets for Indicaxanthin. These targets are implicated in neuromodulation, and inflammatory regulation, normally expressed mostly in the CNS, and expressed (or overexpressed) in cancer tissues ( i.e. breast, thyroid, and prostate cancer cells). Moreover, this study provides qualitative and quantitative information about dynamic interactions of Indicaxanthin at the binding site of target proteins, through molecular dynamics simulations and MM-GBSA. [ABSTRACT FROM AUTHOR]

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

The development of target-fishing approaches, aimed at identifying the possible protein targets of a small molecule, represents a hot topic in medicinal chemistry. A successful target-fishing approach would allow for the elucidation of the mechanism of action of all therapeutically interesting compounds for which the actual target is still unknown. Moreover, target-fishing would be essential for preventing adverse effects of drug candidates, by predicting their potential off-targets, and it would speed up drug repurposing campaigns. However, due to the huge number of possible protein targets that a small-molecule might interact with, experimental target-fishing approaches are out of reach. In silico target-fishing represents a valuable alternative, and examples of receptor-based approaches, exploiting the large number of crystallographic protein structures determined to date, have been reported in the literature. To the best of our knowledge, no proper evaluation of such approaches is, however, reported yet. In the present work, we extensively assessed the reliability of docking-based target-fishing strategies. For this purpose, a set of X-ray structures belonging to different targets was selected, and a dataset of compounds, including 10 experimentally active ligands for each target, was created. A target-fishing benchmark database was then obtained, and used to assess the performance of 13 different docking procedures, in identifying the correct target of the dataset ligands. Moreover, a consensus docking-based target-fishing strategy was developed and evaluated. The analysis highlighted that specific features of the target proteins could affect the reliability of the protocol, which however, proved to represent a valuable tool in the proper applicability domain. Our study represents the first extensive performance assessment of docking-based target-fishing approaches, paving the way for the development of novel efficient receptor-based target fishing strategies.

A screening pool consisting of 617710 drug-like query molecules properly filtered from the ChEMBL database was employed for a ligand-based reverse screening toward the type 2 cannabinoid receptor (CB 2 ) target. By using our recently developed PLATO polypharmacological web platform, 233 out of 617710 drug-like molecules were prioritized on the basis of the predicted bioactivity values, better than 0.2 μM with a probability of about 98%, toward the CB 2 target. Building on these results, the occurrence of putative CB 2 -related targets was also investigated for prospective repurposing studies., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Computational target fishing methods are designed to identify the most probable target of a query molecule. This process may allow the prediction of the bioactivity of a compound, the identification of the mode of action of known drugs, the detection of drug polypharmacology, drug repositioning or the prediction of the adverse effects of a compound. The large amount of information regarding the bioactivity of thousands of small molecules now allows the development of these types of methods. In recent years, we have witnessed the emergence of many methods for in silico target fishing. Most of these methods are based on the similarity principle, i.e., that similar molecules might bind to the same targets and have similar bioactivities. However, the difficult validation of target fishing methods hinders comparisons of the performance of each method. In this review, we describe the different methods developed for target prediction, the bioactivity databases most frequently used by these methods, and the publicly available programs and servers that enable non-specialist users to obtain these types of predictions. It is expected that target prediction will have a large impact on drug development and on the functional food industry. [ABSTRACT FROM AUTHOR]

Gulf War Illness (GWI) is a chronic illness that affects upward of 32% of deployed Veterans to the 1991 Gulf War (GW). The symptoms are medically unexplained, ranging across cognitive deficits, fatigue, gastrointestinal problems, and musculoskeletal pain. Research indicates that chemical warfare agents play a key role in the onset and progression of GWI. The Khamisiyah ammunition storage that housed chemical warfare agents such as sarin, an acetylcholinesterase (AChE) inhibitor, was demolished during the GW, releasing toxicants into the atmosphere affecting deployed troops. Exposure to other chemical agents such as pyridostigmine bromide, N,N-diethyl-m-toluamide, permethrin and chlorpyrifos, were also prevalent during the war. These additional chemical agents have also been shown to inhibit AChE. AChE inhibition induces an acetylcholine build-up, disrupting signals between nerves and muscles, which in high doses leads to asphyxiation. Little is known about low dose exposure. As bioactive compounds tend to interact with multiple proteins with various physiological effect, we aimed to identify other potential shared targets to understand the extent in which these chemicals could lead to GWI. We followed a reverse screening approach where each chemical is computationally docked to a library of protein targets. The programs PharmMapper and TargetNet were used for this purpose, and further analyses were conducted to mark significant changes in participants with GWI. Previously published work on DNA methylation status in GWI was reanalyzed focusing specifically on the predicted shared targets indicating significant changes in DNA methylation of the associated genes. Our findings thus suggest that exposure to GWI-related agents may converge on similar targets with roles in inflammation, neurotransmitter and lipid metabolism, and detoxification which may have impacts on neurodegenerative-like disease and oxidative stress in Veterans with GWI., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Abstract: A general approach for refolding recombinant proteins from inclusion bodies (IBs) is to screen conditions, that facilitate a conversion of unfolded to folded structure and minimize a conversion of unfolded to misfolded and aggregated structures. In this simplified model, such conditions may be those that stabilize the native protein and/or reduce aggregation. In this paper, a novel screening approach, termed reverse screening, was developed using a native activin. Activin-A, a member of transforming growth factor β superfamily, is a homodimeric protein with nine disulfide bonds. We examined partial unfolding process of native activin-A dissolved in a buffer containing moderate concentrations of denaturant and reducing reagent (i.e., 1.5M urea and 0.2mM dithiothreitol). The recovery of the protein was followed by reverse-phase high performance chromatography analysis. Without additives, activin-A showed about 60% loss of the protein due to aggregation after 12-h incubation in the above condition. We then tested various additives for their effects on the recovery after partial unfolding. One of these additives, sodium taurodeoxycholate (TDCA), greatly increased recovery and suppressed aggregation of the protein. These additives were then tested for refolding activin-A from IBs. TDCA among others is proved to be a highly effective refolding additive. These results strongly suggest that reverse screening using native proteins, if available, may be another approach to discovering effective refolding additives. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Introduction Worldwide two strategies are followed to diagnose syphilis. One is conventional method of testing the serum by non-specific treponemal test VDRL followed by specific Treponemal Pallidum Hemagglutination Assay (TPHA). The other method is vice-versa. We aim to study the percentage of syphilis case detection rate by both the methods. Material and methods A retrospective cross-sectional study was conducted amongst the high risk group patients attending the STD-OP of Institute of Venereology, Chennai for a period of 12 months. The study population was divided into 2 groups. Group A with conventional screening method (i.e. VDRL followed by TPHA) and Group B with TPHA followed by VDRL. Results A total of 1527 cases were recruited into the study, 767 in Group A and 760 in Group B. Group A Out of 767 cases, 90 (11.7%) were reactive by VDRL. 65 (72.2%) were TPHA positive among the 90 VDRL reactive. The case detection rate and correlation between the two tests were 11.7% and 72.2% respectively. Group B Out of 760 cases, 263 (34.6%) were TPHA positive 166 were VDRL reactive among the TPHA positive. The case detection rate and correlation between the two tests were 34.6% and 63.1% respectively. Conclusion The results of comparative data clearly demonstrate that the case detection rate was higher (three times) in reverse screening than that of conventional screening method. In conclusion, consistent with recommendations, laboratories should consider implementing the reverse algorithm for the diagnosis of syphiilis. [ABSTRACT FROM AUTHOR]

The in silico study of medicinal plants is a rapidly growing field. Techniques such as reverse screening and network pharmacology are used to study the complex cellular action of medicinal plants against disease. However, it is difficult to produce a meaningful visualization of phytochemical-protein interactions (PCPIs) in the cell. This study introduces a novel workflow combining various tools to visualize a PCPI network for a medicinal plant against a disease. The five steps are 1) phytochemical compilation, 2) reverse screening, 3) network building, 4) network visualization, and 5) evaluation. The output is a PCPI network that encodes multiple dimensions of information, including subcellular location, phytochemical class, pharmacokinetic data, and prediction probability. As a proof of concept, we built a PCPI network for bitter gourd ( Momordica charantia L.) against colorectal cancer. The network and workflow are available at https://yumibriones.github.io/network/. The PCPI network highlights high-confidence interactions for further in vitro or in vivo study. The overall workflow is broadly transferable and can be used to visualize the action of other medicinal plants or small molecules against other diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Briones, Young, Dayrit, De Jesus and Rojas.)

• Alginic acid, a natural carbohydrate is explored as a green bifunctional heterogeneous, biopolymeric organocatalyst for the expeditious synthesis of dihydropyrimidines (DHPMs). • A mild reaction protocol with high to quantitative yields (82–96%). • The catalyst was characterized through X-ray diffraction, thermogravimetric analysis, scanning electron microscopy and infrared spectroscopic techniques. • Reusability of the catalyst was assessed up to five consecutive cycles. Cancer remains considered as one of the leading global health problems either due to meagre and suboptimal therapeutic response of chemotherapeutic agents or due to the emergence of spontaneous complex multidrug resistance in cancer cells. This created a persistent need for the development of new anticancer agents. Enthralled by the high success rate for natural product-based drug discovery and current research scenario, we synthesized a new series of 3,4,5-trimethoxy phenyl ring pendant sulfur‐containing cyanopyrimidine derivatives clubbed with different amines intending to search an anticancer lead compound. To probe the anti-proliferative spectrum of the synthesized derivatives, an in-vitro evaluation was piloted against a panel of 60 cancer cell lines at the National Cancer Institute (NCI) representing major types of cancer diseases. Most of the derivatives showed good to moderate anti-proliferative activity. The results revealed that compound 4e displayed the most promising broad-spectrum anticancer activity with high growth inhibition of various cell lines representing multiple cancers diseases. Mechanistic investigation of compound 4e in human breast cancer MDA-MB-231 cells showed that compound 4e triggers cell death through the induction of apoptosis. ADMET studies and reverse screening were also performed to identify the potential targets of designed molecules. It was concluded that 3,4,5-trimethoxy phenyl ring pendant sulfur‐containing cyanopyrimidine derivative 4e could act as a promising hit molecule for further development of novel anticancer therapeutics. [ABSTRACT FROM AUTHOR]

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

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

The development of target-fishing approaches, aimed at identifying the possible protein targets of a small molecule, represents a hot topic in medicinal chemistry. A successful target-fishing approach would allow for the elucidation of the mechanism of action of all therapeutically interesting compounds for which the actual target is still unknown. Moreover, target-fishing would be essential for preventing adverse effects of drug candidates, by predicting their potential off-targets, and it would speed up drug repurposing campaigns. However, due to the huge number of possible protein targets that a small-molecule might interact with, experimental target-fishing approaches are out of reach. In silico target-fishing represents a valuable alternative, and examples of receptor-based approaches, exploiting the large number of crystallographic protein structures determined to date, have been reported in the literature. To the best of our knowledge, no proper evaluation of such approaches is, however, reported yet. In the present work, we extensively assessed the reliability of docking-based target-fishing strategies. For this purpose, a set of X-ray structures belonging to different targets was selected, and a dataset of compounds, including 10 experimentally active ligands for each target, was created. A target-fishing benchmark database was then obtained, and used to assess the performance of 13 different docking procedures, in identifying the correct target of the dataset ligands. Moreover, a consensus docking-based target-fishing strategy was developed and evaluated. The analysis highlighted that specific features of the target proteins could affect the reliability of the protocol, which however, proved to represent a valuable tool in the proper applicability domain. Our study represents the first extensive performance assessment of docking-based target-fishing approaches, paving the way for the development of novel efficient receptor-based target fishing strategies. [ABSTRACT FROM AUTHOR]

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.

Black cumin (Nigella sativa) is a spice having medicinal properties with pungent and bitter odour. It is used since thousands of years to treat various ailments, including cancer mainly in South Asia and Middle Eastern regions. Substantial evidence in multiple research studies emphasizes about the therapeutic importance of bioactive principles of N. sativa in cancer bioassays; however, the exact mechanism of their anti-tumour action is still to be fully comprehended. The current study makes an attempt in this direction by exploiting the advancements in the Insilico reverse screening technology. In this study, three different Insilico Reverse Screening approaches have been employed for identifying the putative molecular targets of the bioactive principles in Black cumin (thymoquinone, alpha-hederin, dithymoquinone and thymohydroquinone) relevant to its anti-tumour functionality. The identified set of putative targets is further compared with the existing set of experimentally validated targets, so as to estimate the performance of insilico platforms. Subsequently, molecular docking simulations studies were performed to elucidate the molecular interactions between the bioactive compounds & their respective identified targets. The molecular interactions of one such target identified i.e. VEGF2 along with thymoquinone depicted one H-bond formed at the catalytic site. The molecular targets identified in this study need further confirmatory tests on cancer bioassays, in order to justify the research findings from Insilico platforms. This study has brought to light the effectiveness of usage of Insilico Reverse Screening protocols to characterise the un-identified target-ome of poly pharmacological bioactive agents in spices.