Drug Design and Disease Diagnosis: The Potential of Deep Learning Models in Biology

Early prediction and detection enable reduced transmission of human diseases and provide healthcare professionals ample time to make subsequent diagnoses and treatment strategies. This, in turn, aids in saving more lives and results in lower medical costs. Designing small chemical molecules to treat fatal disorders is also urgently needed to address the high death rate of these diseases worldwide. A recent analysis of published literature suggested that deep learning (DL) based models apply more potential algorithms to hybrid databases of chemical data. Considering the above, we first discussed the concept of DL architectures and their applications in drug development and diagnostics in this review. Although DL-based approaches have applications in several fields, in the following sections of the arti-cle, we focus on recent developments of DL-based techniques in biology, notably in structure predic-tion, cancer drug development, COVID infection diagnostics, and drug repurposing strategies. Each review section summarizes several cutting-edge, recently developed DL-based techniques. Additionally, we introduced the approaches presented in our group, whose prediction accuracy is relatively compara-ble with current computational models. We concluded the review by discussing the benefits and draw-backs of DL techniques and outlining the future paths for data collecting and developing efficient computational models.Copyright © 2023 Bentham Science Publishers.

Drug–Target Interaction Deep Learning-Based Model Identifies the Flavonoid Troxerutin as a Candidate TRPV1 Antagonist

Based on the advances made by artificial intelligence (AI) technologies in drug discovery, including target identification, hit molecule identification, and lead optimization, this study investigated natural compounds that could act as transient receptor potential vanilloid 1 (TRPV1) channel protein antagonists. Using a molecular transformer drug–target interaction (MT-DTI) model, troxerutin was predicted to be a TRPV1 antagonist at IC50 582.73 nM. In a TRPV1-overexpressing HEK293T cell line, we found that troxerutin antagonized the calcium influx induced by the TRPV1 agonist capsaicin in vitro. A structural modeling and docking experiment of troxerutin and human TRPV1 confirmed that troxerutin could be a TRPV1 antagonist. A small-scale clinical trial consisting of 29 participants was performed to examine the efficacy of troxerutin in humans. Compared to a vehicle lotion, both 1% and 10% w/v troxerutin lotions reduced skin irritation, as measured by skin redness induced by capsaicin, suggesting that troxerutin could ameliorate skin sensitivity in clinical practice. We concluded that troxerutin is a potential TRPV1 antagonist based on the deep learning MT-DTI model prediction. The present study provides a useful reference for target-based drug discovery using AI technology and may provide useful information for the integrated research field of AI technology and biology.

Post-translational modifications: Host defence mechanism, pathogenic weapon, and emerged target of anti-infective drugs

Post-translational modifications are changes introduced to proteins after their translation. They are the means to generate molecular diversity, expand protein function, control catalytic activity and trigger quick responses to a wide range of stimuli. Moreover, they regulate numerous biological processes, including pathogen invasion and host defence mechanisms. It is well established that bacteria and viruses utilize post-translational modifications on their own or their host's proteins to advance their pathogenicity. Doing so, they evade immune responses, target signaling pathways and manipulate host cytoskeleton to achieve survival, replication and propagation. Many bacterial species secrete virulence factors into the host and mediate hostpathogen interactions by inducing post-translational modifications that subvert fundamental cellular processes. Viral pathogens also utilize post translational modifications in order to overcome the host defence mechanisms and hijack its cellular machinery for their replication and propagation. For example, many coronavirus proteins are modified to achieve host invasion, evasion of immune responses and utilization of the host translational machinery. PTMs are also considered potential targets for the development of novel therapeutics from natural products with antibiotic properties, like lasso peptides and lantibiotics. The last decade, significant progress was made in understanding the mechanisms that govern PTMs and mediate regulation of protein structure and function. This urges the identification of relevant molecular targets, the design of specific drugs and the discovery of PTM-based medicine. Therefore, PTMs emerge as a highly promising field for the investigation and discovery of new therapeutics for many infectious diseases.Copyright © 2021 Bentham Science Publishers.

Drug Repositioning for Coronavirus (COVID-19) using Different Deep Learning Architectures

In December 2019, the COVID-19 epidemic was found in Wuhan, China, and soon hundreds of millions were infected. Therefore, several efforts were made to identify commercially available drugs to repurpose them against COVID-19. Inferring potential drug indications through computational drug repositioning is an efficient method. The drug repositioning problem is a top-K recommendation function that presents the most likely drugs for specific diseases based on drug and disease-related data. The accurate prediction of drug-target interactions (DTI) is very important for drug repositioning. Deep learning (DL) models were recently exploited for promising DTI prediction performance. To build deep learning models for DTI prediction, encoder-decoder architectures can be utilized. In this paper, a deep learning-based drug repositioning approach is proposed, which is composed of two experimental phases. Firstly, training and evaluating different deep learning encoder-decoder architecture models using the benchmark DAVIS Dataset. The trained deep learning models have been evaluated using two evaluation metrics;mean square error and the concordance index. Secondly, predicting antiviral drugs for Covid-19 using the trained deep learning models created during the first phase. In this phase, these models have been experimented to predict different antiviral drug lists, which then have been compared with a recently published antiviral drug list for Covid-19 using the concordance index metric. The overall experimental results of both phases showed that the most accurate three deep learning compound-encoder/protein-encoder architectures are Morgan/AAC, CNN/AAC, and CNN/CNN with best values for the mean square error, the first phase concordance index, and the second phase concordance index. © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Prospects of Novel and Repurposed Immunomodulatory Drugs against Acute Respiratory Distress Syndrome (ARDS) Associated with COVID-19 Disease.

Acute respiratory distress syndrome (ARDS) is intricately linked with SARS-CoV-2-associated disease severity and mortality, especially in patients with co-morbidities. Lung tissue injury caused as a consequence of ARDS leads to fluid build-up in the alveolar sacs, which in turn affects oxygen supply from the capillaries. ARDS is a result of a hyperinflammatory, non-specific local immune response (cytokine storm), which is aggravated as the virus evades and meddles with protective anti-viral innate immune responses. Treatment and management of ARDS remain a major challenge, first, because the condition develops as the virus keeps replicating and, therefore, immunomodulatory drugs are required to be used with caution. Second, the hyperinflammatory responses observed during ARDS are quite heterogeneous and dependent on the stage of the disease and the clinical history of the patients. In this review, we present different anti-rheumatic drugs, natural compounds, monoclonal antibodies, and RNA therapeutics and discuss their application in the management of ARDS. We also discuss on the suitability of each of these drug classes at different stages of the disease. In the last section, we discuss the potential applications of advanced computational approaches in identifying reliable drug targets and in screening out credible lead compounds against ARDS.

Reverse tracking from drug-induced transcriptomes through multilayer molecular networks reveals hidden drug targets.

Identifying molecular targets of a drug is an essential process for drug discovery and development. The recent in-silico approaches are usually based on the structure information of chemicals and proteins. However, 3D structure information is hard to obtain and machine-learning methods using 2D structure suffer from data imbalance problem. Here, we present a reverse tracking method from genes to target proteins using drug-perturbed gene transcriptional profiles and multilayer molecular networks. We scored how well the protein explains gene expression changes perturbed by a drug. We validated the protein scores of our method in predicting known targets of drugs. Our method performs better than other methods using the gene transcriptional profiles and shows the ability to suggest the molecular mechanism of drugs. Furthermore, our method has the potential to predict targets for objects that do not have rigid structural information, such as coronavirus.

Virtual and In Vitro Screening of Natural Products Identifies Indole and Benzene Derivatives as Inhibitors of SARS-CoV-2 Main Protease (Mpro).

The rapid spread of the coronavirus disease 2019 (COVID-19) resulted in serious health, social, and economic consequences. While the development of effective vaccines substantially reduced the severity of symptoms and the associated deaths, we still urgently need effective drugs to further reduce the number of casualties associated with SARS-CoV-2 infections. Machine learning methods both improved and sped up all the different stages of the drug discovery processes by performing complex analyses with enormous datasets. Natural products (NPs) have been used for treating diseases and infections for thousands of years and represent a valuable resource for drug discovery when combined with the current computation advancements. Here, a dataset of 406,747 unique NPs was screened against the SARS-CoV-2 main protease (Mpro) crystal structure (6lu7) using a combination of ligand- and structural-based virtual screening. Based on 1) the predicted binding affinities of the NPs to the Mpro, 2) the types and number of interactions with the Mpro amino acids that are critical for its function, and 3) the desirable pharmacokinetic properties of the NPs, we identified the top 20 candidates that could potentially inhibit the Mpro protease function. A total of 7 of the 20 top candidates were subjected to in vitro protease inhibition assay and 4 of them (4/7; 57%), including two beta carbolines, one N-alkyl indole, and one Benzoic acid ester, had significant inhibitory activity against Mpro protease. These four NPs could be developed further for the treatment of COVID-19 symptoms.

The chamber of secretome in Mycobacterium tuberculosis as a potential therapeutic target.

Mycobacterium tuberculosis (MTB) causes one of the ancient diseases, Tuberculosis, affects people around the globe and its severity can be understood by its classification as a second infectious disease after COVID-19 and the 13th leading cause of death according to a WHO report. Despite having advanced diagnostic approaches and therapeutic strategies, unfortunately, TB is still spreading across the population due to the emergence of drug-resistance MTB and Latent TB infection (LTBI). We are seeking for effective approaches to overcome these hindrances and efficient treatment for this perilous disease. Therefore, there is an urgent need to develop drugs based on operative targeting of the bacterial system that could result in both efficient treatment and lesser emergence of MDR-TB. One such promising target could be the secretory systems and especially the Type 7 secretory system (T7SS-ESX) of Mycobacterium tuberculosis, which is crucial for the secretion of effector proteins as well as in establishing host-pathogen interactions of the tubercle bacilli. The five paralogous ESX systems (ESX-1 to EXS-5) have been observed by in silico genome analysis of MTB, among which ESX-1 and ESX-5 are substantial for virulence and mediating host cellular inflammasome. The bacterium growth and virulence can be modulated by targeting the T7SS. In the present review, we demonstrate the current status of therapeutics against MTB and focus on the function and cruciality of T7SS along with other secretory systems as a promising therapeutic target against Tuberculosis.

CNN-OSBO Encoder-Decoder Architecture for Drug-Target Interaction (DTI) Prediction of Covid-19 Targets

Drug Target Interaction (DTI) prediction is an important factor is drug discovery and repositioning (DDR) since it detects the response of a drug over a target protein. The Coronavirus disease 2019 (COVID-19) disease created groups of deadly pneumonia with clinical appearance mostly similar to SARS-CoV. The precise diagnosis of COVID-19 clinical outcome is more challenging, since the diseases has various forms with varying structures. So predicting the interactions between various drugs with the SARS-CoV target protein is very crucial need in these days, which may leads to discovery of new drugs for the deadly disease. Recently, Deep learning (DL) techniques have been applied by the researches for DTI prediction. Since CNN is one of the major DL models which has the ability to create predictive feature vectors or embeddings, CNN-OSBO encoder-decoder architecture for DTI prediction of Covid-19 targets has been designed Given the input drug and Covid-19 target pair of data, they are fed into the Convolution Neural Networks (CNN) with Opposition based Satin Bowerbird Optimizer (OSBO) encoder modules, separately. Here OSBO is utilized for regulating the hyper parameters (HPs) of CNN layers. Both the encoded data are then embedded to create a binding module. Finally the CNN Decoder module predicts the interaction of drugs over the Covid-19 targets by returning an affinity or interaction score. Experimental results state that DTI prediction using CNN+OSBO achieves better accuracy results when compared with the existing techniques. © 2022 IEEE.

Deep learning optimization for drug-target interaction prediction in COVID-19 using graphic processing unit

The exponentially increasing bioinformatics data raised a new problem: the computation time length. The amount of data that needs to be processed is not matched by an increase in hardware performance, so it burdens researchers on computation time, especially on drug-target interaction prediction, where the computational complexity is exponential. One of the focuses of high-performance computing research is the utilization of the graphics processing unit (GPU) to perform multiple computations in parallel. This study aims to see how well the GPU performs when used for deep learning problems to predict drug-target interactions. This study used the gold-standard data in drug-target interaction (DTI) and the coronavirus disease (COVID-19) dataset. The stages of this research are data acquisition, data preprocessing, model building, hyperparameter tuning, performance evaluation and COVID-19 dataset testing. The results of this study indicate that the use of GPU in deep learning models can speed up the training process by 100 times. In addition, the hyperparameter tuning process is also greatly helped by the presence of the GPU because it can make the process up to 55 times faster. When tested using the COVID-19 dataset, the model showed good performance with 76% accuracy, 74% F-measure and a speed-up value of 179. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

A new perspective on the potential application of RIPK1 in the treatment of sepsis.

RIPK1 is a global cellular sensor that can determine the survival of cells. Generally, RIPK1 can induce cell apoptosis and necroptosis through TNF, Fas and lipopolysaccharide stimulation, while its scaffold function can sense the fluctuation of cellular energy and promote cell survival. Sepsis is a nonspecific disease that seriously threatens human health. There is some dispute in the literature about the role of RIPK1 in sepsis. In this review, the authors attempt to comprehensively discuss the differential results for RIPK1 in sepsis by summarizing the underlying molecular mechanism and putting forward a tentative idea as to whether RIPK1 can serve as a biomarker for the monitoring of treatment and progression in sepsis.

Sepsis is a syndrome that poses a serious threat to human life and health and is classified as a medical emergency by the WHO. RIPK1 can regulate the onset of apoptosis and necrosis in several ways and is known as a sensor of cell survival status. A series of clinical trials of RIPK1 drugs has been conducted this year and have demonstrated promising efficacy in inflammatory diseases, in particular. In this paper, the authors summarize recent studies on the function and mechanism of RIPK1 in sepsis and combine them with the progress in RIPK1 drug development to provide information for the study of RIPK1 in sepsis.

Synthesis, characterization, molecular docking and network pharmacology of bioactive metallic sulfonamide-isatin ligands against promising drug targets

• Synthesis of new Sulfonamide-isatin based scaffolds to incorporate first row metals. • Molecular docking to find a best docking pocket for COVID-19 protein. • Online network pharmacology to find a best target for Alzheimer and carbonic anhydrase-II related gene targets. • Characterization with most promising analytical techniques and DFT based studies. • In vitro enzyme inhibition and antimicrobial profiling of new compounds. A series of sulfonamide and isatin based Schiff bases, (S1) and (S2), and their metal (Co2+, Ni2+, Cu2+ and Zn2+) complexes (1)-(8) were synthesized and characterized by spectroscopic (UV, IR, MS, 1H and 13C-NMR), elemental, magnetic and physical techniques. The non-electrolytic character of Co2+, Ni2+, and Zn2+ compounds and electrolytic nature of Cu2+ was established by their conductance studies. The energies of Frontier Molecular Orbitals (FMOs) were also used to explore various global and quantum chemical qualities. To find the activity and molecular targets in curing Alzheimer's Disease (AD) and Carbonic Anhydrase II (CA-II) inhibition, Network Pharmacology modeling was used. The prospective targets were predicted using the Swiss Target PredictionR online facility. The Gene CardsR database has been used to find genes linked to AD and CA-II. We also conducted Gene OntologyR (GO) analysis on the intersecting genes targets on active targets of synthesized compounds by DAVID (Database for Annotation, Visualization and Integrated Discovery) Bioinformatics Services using the CytoscapeR program. The in vitro enzyme inhibition assays were done against protease, amylase, acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) while their antimicrobial studies were performed against pathogenic bacterial and fungal species. The antioxidant values, evaluated as 2-diphenyl-1-picrylhydrazyl (DPPH) and ferric reducing assay power (FRAP) (%) ranged between 51.0±0.11-68.1±0.11% with IC 50 ranging 146.84-196.08 µL/mol. [Display omitted] [ FROM AUTHOR]

Towards De Novo Drug Design for the Coronavirus: A Drug-Target Interaction Prediction Approach using Atom-enhanced Graph Neural Network with Multi-hop Gating Mechanism

For humans, the COVID-19 pandemic and Coronavirus have undeniably been a nightmare. Although there are effective vaccines, specific drugs are still urgent. Normally, to identify potential drugs, one needs to design and then test interactions between the drug and the virus in an in silico manner for determining candidates. This Drug-Target Interaction (DTI) process, can be done by molecular docking, which is too complicated and time-consuming for manual works. Therefore, it opens room for applying Artificial Intelligence (AI) techniques. In particular, Graph Neural Network (GNN) attracts recent attention since its high suitability for the nature of drug compounds and virus proteins. However, to introduce such a representation well-reflecting biological structures of biological compounds is not a trivial task. Moreover, since available datasets of Coronavirus are still not highly popular, the recently developed GNNs have been suffering from overfitting on them. We then address those issues by proposing a novel model known as Atom-enhanced Graph Neural Network with Multi-hop Gating Mechanism. On one hand, our model can learn more precise features of compounds and proteins. On the other hand, we introduce a new gating mechanism to create better atom representation from non-neighbor information. Once applying transfer learning from very large databanks, our model enjoys promising performance, especially when experimenting with Coronavirus. © 2022 IEEE.

Development of Machine Learning Regression Model for COVID-19 Drug Target Prediction

There is a perennial need to identify novel, effective therapeutic agents to combat rising infections. Recently, prediction of therapeutic targets to decrease the impact of COVID-19 has posed an urgent challenge requiring innovative solutions. Successful identification of novel drug-target combinations may greatly facilitate drug development. To meet this need, we developed a COVID-19 drug target prediction model using machine learning approaches to quickly identify drug candidates for 18 COVID-19 protein targets. Specifically, we analyzed the performance of three prediction models to predict drug-target docking scores, which represents the strength of interactions between ligands and proteins. Docking scores were predicted for 300,457 molecules on 18 different COVID-19 related protein docking targets. Our proposed approach achieved a competitive performance with mathrm{R}-{2}=0.69,MAE=0.285, MSE=0.627. In addition, we identify chemical structures associated with stronger binding affinities across target binding sites. We believe our work could potentially save pharmaceutical companies significant resources, especially during the early stages of drug development. © 2022 IEEE.

Targeting viral proteins for restraining SARS-CoV-2: focusing lens on viral proteins beyond spike for discovering new drug targets.

INTRODUCTION: Emergence of highly infectious SARS-CoV-2 variants are reducing protection provided by current vaccines, requiring constant updates in antiviral approaches. The virus encodes four structural and sixteen nonstructural proteins which play important roles in viral genome replication and transcription, virion assembly, release , entry into cells, and compromising host cellular defenses. As alien proteins to host cells, many viral proteins represent potential targets for combating the SARS-CoV-2. AREAS COVERED: Based on literature from PubMed and Web of Science databases, the authors summarize the typical characteristics of SARS-CoV-2 from the whole viral particle to the individual viral proteins and their corresponding functions in virus life cycle. The authors also discuss the potential and emerging targeted interventions to curb virus replication and spread in detail to provide unique insights into SARS-CoV-2 infection and countermeasures against it. EXPERT OPINION: Our comprehensive analysis highlights the rationale to focus on non-spike viral proteins that are less mutated but have important functions. Examples of this include: structural proteins (e.g. nucleocapsid protein, envelope protein) and extensively-concerned nonstructural proteins (e.g. NSP3, NSP5, NSP12) along with the ones with relatively less attention (e.g. NSP1, NSP10, NSP14 and NSP16), for developing novel drugs to overcome resistance of SARS-CoV-2 variants to preexisting vaccines and antibody-based treatments.

FingerDTA: A Fingerprint-Embedding Framework for Drug-Target Binding Affinity Prediction

Many efforts have been exerted toward screening potential drugs for targets, and conducting wet experiments remains a laborious and time-consuming approach. Artificial intelligence methods, such as Convolutional Neural Network (CNN), are widely used to facilitate new drug discovery. Owing to the structural limitations of CNN, features extracted from this method are local patterns that lack global information. However, global information extracted from the whole sequence and local patterns extracted from the special domain can influence the drugtarget affinity. A fusion of global information and local patterns can construct neural network calculations closer to actual biological processes. This paper proposes a Fingerprint-embedding framework for Drug-Target binding Affinity prediction (FingerDTA), which uses CNN to extract local patterns and utilize fingerprints to characterize global information. These fingerprints are generated on the basis of the whole sequence of drugs or targets. Furthermore, FingerDTA achieves comparable performance on Davis and KIBA data sets. In the case study of screening potential drugs for the spike protein of the coronavirus disease 2019 (COVID-19), 7 of the top 10 drugs have been confirmed potential by literature. Ultimately, the docking experiment demonstrates that FingerDTA can find novel drug candidates for targets. All codes are available at http://lanproxy.biodwhu.cn:9099/mszjaas/FingerDTA.git. © 2018 Tsinghua University Press.

Reinforcement Learning and Graph Neural Networks for Designing Novel Drugs with Optimized Affinity: Application to SARS-CoV-2

Drugs are generally designed for a specific target protein. Recent studies have demonstrated the capability of deep learning-based models to accelerate and cheapen the drug development process. The proposed deep learning models can generate novel molecules with optimized drug-like properties. However, the properties addressed are often limited and may be misleading. This is because they do not reflect the complete information about the binding affinity of the designed drug and the target protein. In this work, we exploit the state-of-The-Art progress made in drug-Target-Affinity (DTA) prediction to assess the binding affinity of drugs generated by a developed molecular generator against the corona-virus main protease (SARS-CoV-2 Mpro). The molecular generator is a recurrent neural network-based model, while the DTA predictor is a graph neural network (GNN), famously known as GraphDTA. We train the molecular generator using reinforcement learning (RL) to optimize the GraphDTA-predicted score. As this is the first benchmark of this kind (to the best of our knowledge), we report our benchmarking results;of 1.79% desirability;with the hope of motivating future improvements in this regard. © 2022 IEEE.

dbSCI: A manually curated database of SARS-CoV-2 inhibitors for COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogen of the ongoing coronavirus disease 2019 (COVID-19) global pandemic. Here, by centralizing published cell-based experiments, clinical trials, and virtual drug screening data from the NCBI PubMed database, we developed a database of SARS-CoV-2 inhibitors for COVID-19, dbSCI, which includes 234 SARS-CoV-2 inhibitors collected from publications based on cell-based experiments, 81 drugs of COVID-19 in clinical trials and 1305 potential SARS-CoV-2 inhibitors from bioinformatics analyses. dbSCI provides four major functions: (1) search the drug target or its inhibitor for SARS-CoV-2, (2) browse target/inhibitor information collected from cell experiments, clinical trials, and virtual drug screenings, (3) download, and (4) submit data. Each entry in dbSCI contains 18 types of information, including inhibitor/drug name, targeting protein, mechanism of inhibition, experimental technique, experimental sample type, and reference information. In summary, dbSCI provides a relatively comprehensive, credible repository for inhibitors/drugs against SARS-CoV-2 and their potential targeting mechanisms and it will be valuable for further studies to control COVID-19. [ FROM AUTHOR]

Predicting drug-target binding affinity through molecule representation block based on multi-head attention and skip connection.

Exiting computational models for drug-target binding affinity prediction have much room for improvement in prediction accuracy, robustness and generalization ability. Most deep learning models lack interpretability analysis and few studies provide application examples. Based on these observations, we presented a novel model named Molecule Representation Block-based Drug-Target binding Affinity prediction (MRBDTA). MRBDTA is composed of embedding and positional encoding, molecule representation block and interaction learning module. The advantages of MRBDTA are reflected in three aspects: (i) developing Trans block to extract molecule features through improving the encoder of transformer, (ii) introducing skip connection at encoder level in Trans block and (iii) enhancing the ability to capture interaction sites between proteins and drugs. The test results on two benchmark datasets manifest that MRBDTA achieves the best performance compared with 11 state-of-the-art models. Besides, through replacing Trans block with single Trans encoder and removing skip connection in Trans block, we verified that Trans block and skip connection could effectively improve the prediction accuracy and reliability of MRBDTA. Then, relying on multi-head attention mechanism, we performed interpretability analysis to illustrate that MRBDTA can correctly capture part of interaction sites between proteins and drugs. In case studies, we firstly employed MRBDTA to predict binding affinities between Food and Drug Administration-approved drugs and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication-related proteins. Secondly, we compared true binding affinities between 3C-like proteinase and 185 drugs with those predicted by MRBDTA. The final results of case studies reveal reliable performance of MRBDTA in drug design for SARS-CoV-2.

Molecular insights on bioactive compounds againstCovid-19: A Network pharmacological and computational study.

BACKGROUND: Network pharmacology based identification of phytochemicals in the form of cocktails against off-targets can play a significant role in inhibition of SARS_CoV2 viral entry and its propagation. This study includes network pharmacology, virtual screening, docking and molecular dynamics to investigate the distinct antiviral mechanisms of effective phytochemicals against SARS_CoV2. METHODS: SARS_CoV2 human-protein interaction network was explored from the BioGRID database and analysed using Cytoscape. Further analysis was performed to explore biological function, protein-phytochemical/drugs network and up-down regulation of pathological host target proteins. This lead to understand the antiviral mechanism of phytochemicals against SARS_CoV2. The network was explored through g:Profiler, EnrichR, CTD, SwissTarget, STITCH, DrugBank, BindingDB, STRING and SuperPred. Virtual screening of phytochemicals against potential antiviral targets such as M-Pro, NSP1, Receptor binding domain, RNA binding domain, and ACE2 discloses the effective interaction between them. Further, the binding energy calculations through simulation of the docked complex explains the efficiency and stability of the interactions. RESULTS: The network analysis identified quercetin, genistein, luteolin, eugenol, berberine, isorhamnetin and cinnamaldehyde to be interacting with host proteins ACE2, DPP4, COMT, TUBGCP3, CENPF, BRD2 and HMOX1 which are involved in antiviral mechanisms such as viral entry, viral replication, host immune response, and antioxidant activity. Thus indicating that herbal cocktails can effectively tackle the viral hijacking of the crucial biological functions of human host. Further exploration through Virtual screening, docking and molecular dynamics recognizes the effective interaction of phytochemicals such as punicalagin, scutellarin, and solamargine with their respective potential targets. CONCLUSION: This work illustrates probable strategy for identification of phytochemical based cocktails and off-targets which are effective against SARS_CoV 2.