Drug Design Studies Research Articles

Structure-based drug design and molecular dynamics studies of an allosteric modulator targeting the protein-protein interaction site of PDK1.

Protein-protein interaction interfaces play a major role in cell signaling pathways. There is always agreat interest in developing protein-protein interaction (PPI) inhibitors of kinases, as they are challenging due to their hydrophobicity, flat surface, specificity, potency, etc. 3 Phosphoinositide-dependent kinase-1 (PDK1), which is involved in the PI3K/PDK1/AKT pathway, is a cancer target that has gained insight for the past two decades. PDK1 possesses a protein interaction fragment (PIF) pocket, which is a well-known PPI that targets allosteric modulators. This work focusses on energy-based pharmacophore model development which on virtual screening could yield novel scaffolds towards the drug designing objective for the kind of PDK1 modulators. A novel pyrazolo pyridine molecule was identified as an allosteric modulator that binds to the PPI site. The metadynamics simulations with free energy profiles further revealed the conformational allosteric changes stimulated on the protein structure upon ligand binding. The cytotoxic activity (IC50-20 μM) of the identified compound against five different cancer cell lines and cell cycle analysis supported the anticancer activity of the identified compound. All the computational works were carried out by the most commonly used Schrodinger Suite software. The pharmacophore was validated by Receiver Operation Characteristics (ROC) and screening against allosteric Enamine database library. The Optimized Potential Liquid Simulations (OPLS-2005) was used to minimize the structures for molecular docking calculations, and inbuilt scoring method of ranking the compounds based on docking score and Glide energy was used. Molecular dynamics simulations were conducted by Desmond implemented in Maestro. The hit compound was purchased from Enamine and tested against different cancer cell lines by MTT assay, apoptosis by western blotting technique, and by flow cytometry analysis.

Targeted Protein Degradation Systems: Controlling Protein Stability Using E3 Ubiquitin Ligases in Eukaryotic Species.

This review explores various methods for modulating protein stability to achieve target protein degradation, which is a crucial aspect in the study of biological processes and drug design. Thirty years have passed since the introduction of heat-inducible degron cells utilizing the N-end rule, and methods for controlling protein stability using the ubiquitin-proteasome system have moved from academia to industry. This review covers protein stability control methods, from the early days to recent advancements, and discusses the evolution of techniques in this field. This review also addresses the challenges and future directions of protein stability control techniques by tracing their development from the inception of protein stability control methods to the present day.

Discovery, Optimization, and Evaluation of Novel Pyridin-2(1H)-one Analogues as Potent TRK Inhibitors for Cancer Treatment.

Tropomyosin receptor kinase (TRK) fusion, an oncogenic form of kinase with pan-tumor occurrence, is a clinically validated important antitumor target. In this study, we screened our in-house kinase inhibitor library against TRK and identified a promising hit compound 4 with a novel pyridin-2(1H)-one scaffold. Through a combination of structure-based drug design and structure-activity relationship (SAR) study, compound 14q was identified as a potent TRK inhibitor with good kinase selectivity. It also blocked cellular TRK signaling, thereby inhibiting TRK-dependent cell viability. Additionally, 14q displayed acceptable pharmacokinetic properties with 37.8% oral bioavailability in mice. Strong in vivo tumor growth inhibition of 14q was observed in subcutaneous M091 and KM12 tumor xenograft models with TRK fusion, causing significant tumor inhibition or even complete tumor regression.

In Silico Therapeutic Study: The Next Frontier in the Fight against SARS-CoV-2 and Its Variants

COVID-19 has claimed around 7 million lives (from December 2019–November 2023) worldwide and continues to impact global health. SARS-CoV-2, the virus causing COVID-19 disease, is characterized by a high rate of mutations, which contributes to its rapid spread, virulence, and vaccine escape. While several vaccines have been produced to minimize the severity of the coronavirus, and diverse treatment regimens have been approved by the US FDA under Emergency Use Authorization (EUA), SARS-CoV-2 viral mutations continue to derail the efforts of scientists as the emerging variants evade the recommended therapies. Nonetheless, diverse computational models exist that offer an opportunity for the swift development of new drugs or the repurposing of old drugs. In this review, we focus on the use of various virtual screening techniques like homology modeling, molecular docking, molecular dynamics simulations, QSAR, pharmacophore modeling, etc., in repurposing SARS-CoV-2 therapeutics against major variants of SARS-CoV-2 (Alpha, Beta, Gamma, Delta, and Omicron). The results have been promising from the computer-aided drug design (CADD) studies in suggesting potential compounds for the treatment of COVID-19 variants. Hence, in silico therapeutic studies represent a transformative approach that holds great promise in advancing our fight against the ever-evolving landscape of SARS-CoV-2 and its variants.

AlphaFold2 for Protein Structure Prediction: Best Practices and Critical Analyses.

AlphaFold2 (AF2) has emerged in recent years as a groundbreaking innovation that has revolutionized several scientific fields, in particular structural biology, drug design, and the elucidation of disease mechanisms. Many scientists now use AF2 on a daily basis, including non-specialist users. This chapter is aimed at the latter. Tips and tricks for getting the most out of AF2 to produce a high-quality biological model are discussed here. We suggest to non-specialist users how to maintain a critical perspective when working with AF2 models and provide guidelines on how to properly evaluate them. After showing how to perform our own structure prediction using ColabFold, we list several ways to improve AF2 models by adding information that is missing from the original AF2 model. By using software such as AlphaFill to add cofactors and ligands to the models, or MODELLER to add disulfide bridges between cysteines, we guide users to build a high-quality biological model suitable for applications such as drug design, protein interaction, or molecular dynamics studies.

Biological Activity of Late Transition Metal‐Based Compounds: From Computational and Theoretical Studies to Laboratory Exploration and Beyond

The use of metal compounds such as cisplatin and its derivatives as therapeutic and diagnostic agents against various diseases is well established. Although metallodrugs have been very successful clinically, low therapeutic selectivity and the potential toxicity of the metal compounds mandate the need for a continuous search for more efficient and specific treatments. Hence, the use of computer‐aided drug design (CADD), molecular modelling and theoretical studies in designing, selecting and applying potential drug candidates have become ever more necessary. Among the many computational and theoretical techniques, molecular docking and density functional theory play significant roles in predicting drug activity. Recent methodologies, within the past 5 years, that rely on these techniques to advance the adoption of potential metallodrugs are highlighted and thoroughly discussed. This is because advancements in computing power have led to the wide use of CADD approaches for drug discovery, development and analysis to shorten the time and reduce the costs associated with drug discovery.

Innovative tree-based method for sampling molecular conformations: exploring the ATP-binding cassette subfamily D member 1 (ABCD1) transporter as a case study.

We introduce a novel tree-based method for visualizing molecular conformation sampling. Our method offers enhanced precision in highlighting conformational differences and facilitates the observation of local minimas within proteins fold space. The projection of empirical laboratory data on the tree allows us to create a link between protein conformations and disease relevant data. To demonstrate the efficacy of our approach, we applied it to the ATP-binding cassette subfamily D member 1 (ABCD1) transporter responsible for very long-chain fatty acids (VLCFAs) import into peroxisomes. The genetic disorder called X-linked adrenoleukodystrophy (XALD) is characterized by the accumulation of VLCFA due to pathogenic variants in the ABCD1 gene. Using in silico molecular simulation, we examined the behavior of 16 prevalent mutations alongside the wild-type protein, exploring both inward and outward open forms of the transporter through molecular simulations. We evaluated from resulting trajectories the energy potential related to the ABCD1 interactions with ATP molecules. We categorized XALD patients based on the severity and progression of their disease, providing a unique clinical perspective. By integrating this data into our numerical framework, our study aimed to uncover the molecular underpinnings of XALD, offering new insights into disease progression. As we explored molecular trajectories and conformations resulting from our study, the tree-based method not only contributes valuable insights into XALD but also lays a solid foundation for forthcoming drug design studies. We advocate for the broader adoption of our innovative approach, proposing it as a valuable tool for researchers engaged in molecular simulation studies.

Activity prediction, structure-based drug design, molecular docking, and pharmacokinetic studies of 1,4-dihydropyridines derivatives as α-amylase inhibitors

ObjectivesDiabetes places a substantial economic burden on countries worldwide. The costs associated with diabetes management, including healthcare services, medications, monitoring equipment, and productivity losses, are substantial. The International Diabetes Federation has estimated that global healthcare expenditures associated with diabetes and its complications exceed hundreds of billions of dollars annually. Therefore, a critical need exists to develop drugs that are highly effective, affordable, and easily accessible to society. MethodsThis study explored the structural modification of 1,4-DHP derivatives to identify specific α-amylase inhibitors, with the aim of developing more effective and accessible drugs for diabetes. We evaluated the activity and binding ability of the designed compounds. In addition, we performed drug-likeness and pharmacokinetic studies on the modified compounds. ResultsEquation (1) had the highest accuracy, on the basis of internal and external assessment parameters, including R2int = 0.852, R2adj = 0.803, Q2cv = 0.731, and R2ext = 0.884. Moreover, the five potent analogs identified through structure-based drug design demonstrated a more favorable interaction than observed for the template or acarbose. Additionally, comprehensive studies on the drug-like properties and pharmacokinetics of the designed compounds supported their oral safety and favorable pharmacokinetic profiles. ConclusionsThe designed analogs show promise for developing new hypoglycemic agents. Their positive attributes and performance suggest that they may potentially serve as candidates for further research in improving treatments for high blood sugar-associated conditions.

Computational drug discovery of an inhibitor of APOBEC3B as a treatment for epithelial cancers

Cancer is one of the leading causes of death in the U.S., and tumorous cancers such as cervical, lung, breast, and ovarian cancers are the most common types. APOBEC3B is a nonessential cytidine deaminase found in humans and theorized to defend against viral infection. However, overexpression of APOBEC3B is linked to cancer in humans, which makes APOBEC3B a potential cancer treatment target through competitive inhibition for several tumorous cancers. Computational studies can help reveal a small molecule inhibitor using high-throughput virtual screening of millions of candidates with relatively little cost. This study aims to narrow the field of potential APOBEC3B inhibition candidates for future in vitro assays and provide an effective scaffold for drug design studies. Another goal of this project is to provide critical amino acid targets in the active site for future drug design studies. This study simulated 7.8 million drug candidates using high-throughput virtual screening and further processed the top scoring 241 molecules from AutoDock Vina, DOCK 6, and de novo design. Using virtual screening, de novo design, and molecular dynamics simulations, a competitive inhibitor candidate was discovered with an average binding free energy score of −46.03 kcal/mol, more than 10 kcal/mol better than the substrate control (dCMP). These results indicate that this molecule (or a structural derivative) may be an effective inhibitor of APOBEC3B and prevent host genome mutagenesis resulting from protein overexpression. Another important finding is the confirmation of essential amino acid targets, such as Tyr250 and Gln213 within the active site of APOBEC3B. Therefore, study used novel computational methods to provide a theoretical scaffold for future drug design studies that may prove useful as a treatment for epithelial cancers. Communicated by Ramaswamy H. Sarma

3,3',5,5'-Tetramethoxybiphenyl-4,4'diol triggers oxidative stress, metabolic changes, and apoptosis-like process by reducing the PI3K/AKT/NF-κB pathway in the NCI-H460 lung cancer cell line

Lung cancer is one of the leading causes of cancer-related deaths in men and women worldwide. Current treatments have limited efficacy, cause significant side effects, and cells can develop drug resistance. New therapeutic strategies are needed to discover alternative anticancer agents with high efficacy and low-toxicity. TMBP, a biphenyl obtained by laccase-biotransformation of 2,6-dimethoxyphenol, possesses antitumor activity against A549 adenocarcinoma cells. Without causing damage to sheep erythrocytes and mouse peritoneal macrophages of BALB/c mice. In addition to being classified as a good oral drug according to in-silico studies. This study evaluated the in-vitro cytotoxic effect of TMBP on lung-cancer cell-line NCI-H460 and reports mechanisms on immunomodulation and cell death. TMBP treatment (12.5–200 μM) inhibited cell proliferation at 24, 48, and 72 h. After 24-h treatment, TMBP at IC50 (154 μM) induced various morphological and ultrastructural changes in NCI-H460, reduced migration and immunofluorescence staining of N-cadherin and β-catenin, induced increased reactive oxygen species and nitric oxide with reduced superoxide radical-anion, increased superoxide dismutase activity and reduced glutathione reductase. Treatment also caused metabolic stress, reduced glucose-uptake, intracellular lactate dehydrogenase and lactate levels, mitochondrial depolarization, increased lipid droplets, and autophagic vacuoles. TMBP induced cell-cycle arrest in the G2/M phase, death by apoptosis, increased caspase-3/7, and reduced STAT-3 immunofluorescence staining. The anticancer effect was accompanied by decreasing PI3K, AKT, ARG-1, and NF-κB levels, and increasing iNOS. These results suggest its potential as a candidate for use in future lung anticancer drug design studies.

IDP-LM: Prediction of protein intrinsic disorder and disorder functions based on language models.

Intrinsically disordered proteins (IDPs) and regions (IDRs) are a class of functionally important proteins and regions that lack stable three-dimensional structures under the native physiologic conditions. They participate in critical biological processes and thus are associated with the pathogenesis of many severe human diseases. Identifying the IDPs/IDRs and their functions will be helpful for a comprehensive understanding of protein structures and functions, and inform studies of rational drug design. Over the past decades, the exponential growth in the number of proteins with sequence information has deepened the gap between uncharacterized and annotated disordered sequences. Protein language models have recently demonstrated their powerful abilities to capture complex structural and functional information from the enormous quantity of unlabelled protein sequences, providing opportunities to apply protein language models to uncover the intrinsic disorders and their biological properties from the amino acid sequences. In this study, we proposed a computational predictor called IDP-LM for predicting intrinsic disorder and disorder functions by leveraging the pre-trained protein language models. IDP-LM takes the embeddings extracted from three pre-trained protein language models as the exclusive inputs, including ProtBERT, ProtT5 and a disorder specific language model (IDP-BERT). The ablation analysis shown that the IDP-BERT provided fine-grained feature representations of disorder, and the combination of three language models is the key to the performance improvement of IDP-LM. The evaluation results on independent test datasets demonstrated that the IDP-LM provided high-quality prediction results for intrinsic disorder and four common disordered functions.

Drug design and in-silico study of 2-alkoxylatedquinoline-3-carbaldehyde compounds: Inhibitors of Mycobacterium tuberculosis

Tuberculosis (TB) caused by Mycobacterium tuberculosis (MTB) is a deadly communicable disease that frequently affects the lungs. Current treatment protocols are bedeviled by extensive drug-resistant (XDR) and the evolution of multidrug-resistant (MDR-TB) strains. Virtual in-silico drug discovery tools were used to investigate thirty-two hypothetical 2-alkoxylatedquinoline-3-carbaldehyde compounds for screening against ten different diseases proteins based on drug-likeness, oral bioavailability, pharmacokinetics, global chemical reactivity and their theoretical binding affinities. Their chemical structures were optimized at the density functional theory (DFT) using Becke's three-parameter exchange functional with Lee–Yang–Parr correlation function (B3LYP) and the triple zeta basis set 6–311 in a vacuum using Gaussian 09 W software. Docking study using Pyrx and Discovery studio. Fourteen compounds; 4 - 6, 12 – 14, 19 – 22 and 27–30 complied with the established drug-likeness rules, however, five compounds 12, 13, 27, 28 and 29 exhibited no significant toxicity. Structural activity relationship revealed that shorter (n < 3) or longer (n > 5) alkyloxyl substituents at position-2 of the quinoline moiety reduces drug-likeness and increases toxicity. Individually, the binding energies obtained were (-8.9 kcal/ mole) against malaria for compound 12 and (-8.2 kcal/mole) against the diabetes for compound 29, both highest for the ten diseases investigated. Mycobacterium Tuberculosis proteins investigated. Molecular dynamics also confirms that 12 and 27 binds very well in the active pocket of Mycobacterium tuberculosis and calculated total free binding energy from MMPBSA is -97.53 ± 2.47 and -58.62 ± 2.94 kJ/mol respectively. The five lead compounds all had binding energies higher than the reference tuberculosis drugs; Isoniazid and Ethambutanol.

Molecular Dynamics Simulations of HDAC-Ligand Complexes Towards the Design of New Anticancer Compounds±.

Quantitative Structure-Activity Relationship (QSAR) studies gained a foothold in the mid-1960s to rationalise the biological activity of medicinally important compounds. Since then, the advancements in computer hardware and software added many new techniques and areas to this field of study. Molecular dynamics (MD) simulations are one such technique in direct drug design approaches. MD simulations have a special place in drug design studies because they decode the dynamics of intermolecular interactions between a biological target and its potential ligands/inhibitors. The trajectories from MD simulations provide different non-bonding interaction parameters to assess the compatibility of the protein-ligand complex and thereby facilitate the design of prospective compounds prior to their wet-lab exploration. Histone deacetylases (HDACs) play a key role in epigenetics and they are promising drug targets for cancer and various other diseases. This review attempts to shed some light on the modelling studies of HDAC inhibitors as anticancer agents. In view of the advantages of MD simulations in direct drug design, this review also discusses the fragment-based approach in designing new inhibitors of HDAC8 and HDAC2, starting from the interaction energies of ligand fragments obtained from the MD simulations of respective protein-ligand complexes. Here, the design of new anticancer compounds from largazole thiol, trichostatin A, vorinostat, and several other prototype compounds are reviewed. These studies may stimulate the interest of medicinal chemists in MD simulations as a direct drug design approach for new drug development.

Anti-Neurodegenerating Activity: Structure-Activity Relationship Analysis of Flavonoids.

An anti-neurodegeneration activity study was carried out for 80 flavonoid compounds. The structure-activity analysis of the structures was carried out by performing three different anti-neurodegeneration screening tests, showing that in these structures, the presence of a hydroxy substituent group at position C3' as well as C5' of ring B and a methoxy substituent group at the C7 position of ring A play a vital role in neuroprotective and antioxidant as well as anti-inflammatory activity. Further, we found structure (5) was the top-performing active structure out of 80 structures. Subsequently, a molecular docking study was carried out for the 3 lead flavonoid compounds (4), (5), and (23) and 21 similar hypothetical proposed structures to estimate the binding strength between the tested compounds and proteins potentially involved in disease causation. Ligand-based pharmacophores were generated to guide future drug design studies.

VinaLigGen: a method to generate LigPlots and retrieval of hydrogen and hydrophobic interactions from protein-ligand complexes

Developments in the field of computational structural biology and with increasing computing speeds have encouraged researchers in studying large compound libraries during the virtual screening. After performing molecular docking, the consideration of vina score in filtering the compounds without collecting the hydrogen bond or hydrophobic interaction between protein and ligand complex leads to missing multiple good lead molecules. The tools used for virtual screening in drug design and discovery studies were previously designed and developed for small datasets. LigPlots were used to generate 2-dimensional (2D) interaction maps of protein-ligand complexes. These maps depict diverse bonds like hydrogen and hydrophobic interactions in varied colors for all ligand conformations within the library. However, handling large numbers of protein-ligand complexes can make this process quite laborious. The development of a tool is strongly required or an implementation of automation to generate all the interaction details has a strong demand. This paper describes an implementation of an automation technique on the executable programs like ligplot.exe, hbplus.exe and hbadd.exe to obtain the 2D interaction map (LigPlots) of the protein and ligand complex (*.ps) and hydrogen bonds and hydrophobic interactions in *.csv format for molecules to be considered for virtual screening by using some sorting & searching algorithms and python’s file handling functions, and it also mentions the program’s limitations and availability of the program. The program can be found on github . Communicated by Ramaswamy H. Sarma

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies.

Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

Head-to-Tail Cyclization of α-Conotoxin TxID Leads to Enhanced Stability in Serum

Peptide biomolecules have important bioactivities and hence their use in drug design and development studies has increased in recent years. Conotoxins are natural peptides that obtained from cone snail venoms and have the potential to be used for chronic pain treatment, Parkinson's disease, schizophrenia, obesity and cancer due to their effects on the nervous system. However, their use as medicines has been limited as they can be easily broken down by many proteolytic enzymes in the body. Several modification methods are used to overcome these disadvantages. Cyclization of the peptide backbone is one such method and has been used to stabilize various linear peptides. In this study, the N- and C-termini of α-conotoxin TxID with two disulfide bridges were joined using a six amino acid long GGAAGG linker peptide chain to cyclize the peptide backbone and the serum stability of the cyclized peptide was examined. The cyclic TxID peptide remained intact about 50% in human serum after 24 hours.

Development and validation for bioanalysis of VK2809, its active metabolite VK2809A and glutathione-conjugated metabolite MB06588 in rat liver using LC-MS/MS

VK2809 is a promising drug candidate in Phase II clinical trials for the treatment of non-alcoholic steatohepatitis (NASH). It is a prodrug with a HepDirect strategy, which can achieve selective hepatic metabolic activation, generating an active metabolite VK2809A as a potent and selective agonist for thyroid hormone receptor beta (TRβ), a concomitant reactive metabolite VK2809B, and a glutathione (GSH) conjugate MB06588. Currently, there is no convenient and sensitive bioanalytical method for the simultaneous determination of the above three metabolites. Herein, we established an LC-MS/MS method to separate VK2809 and its metabolites on the XSelect HSS T3 column and quantified them in negative electrospray ionization mode. Subsequently, several factors were investigated such as the use of 60% acetonitrile for homogenization to stabilize the analytes, the addition of 20 mM glutathione for the derivation of VK2809B, and the protein precipitation with methanol containing Sobetirome as the internal standard (IS). The method exhibited good linearity for all compounds (19.4–388.4 nM for VK2809; 27.4–2744.4 nM for VK2809A and 10.6–211.0 nM for MB06588) with great correlation coefficients (r > 0.996). The method validation also demonstrated acceptable precision (RSD < 13.0% for VK2809, RSD < 7.9% for VK2809A, RSD < 14.4% for MB06588) and accuracy (92.7%−103% for VK2809, 91.2%−107.3% for VK2809A, 96%−106.7% for MB06588). The matrix effect, recovery, and stability were also suitable to determine all the analytes. This method is suitable for the bioanalysis of VK2809 and its metabolites and has been successfully applied to the study of intrahepatic exposure in rats. It is expected to be further practiced in drug design, optimization, and metabolism study in the following research.

Improving the Prediction Performance of Protein Protein Interaction Sites Using Xgboost and Optimization

Since proteins have important tasks in all processes within the cell they are vital elements for all living organisms. They are significant in regulating most of the biological processes which occur in a cell. They are widely researched to comprehend roles of them and to assist drug design studies. In these tasks, they usually work by interacting with other proteins, not alone. Thus, predicting protein-protein interactions and protein protein interaction sites is an important problem in bioinformatics. There are a number of computational methods developed for this prediction task. DeepPPISP-XGB is one of these methods and produces promising predicting results. In this study, we proposed an optimization process in order to improve prediction results of this method. This optimization process produced 1.5% better AUROC value and 3.3% better AUPRC value compared to DeepPPISP-XGB method.

In Silico Studies of Drug Discovery and Design Against COVID-19 Focusing on ACE2 and Spike Protein Virus Receptors: A Systematic Review

The emergence of COVID-19 has prompted researchers worldwide to focus on developing drugs that specifically target ACE2 receptors and SARS-CoV-2 Spike Protein receptors. They have embraced an in-silico approach that employs virtual screening, molecular docking, and molecular dynamics to achieve this. This innovative method harnesses existing chemical and natural product databases to identify the most suitable ACE2 receptor blockers and SARS-CoV-2 Spike Protein inhibitors. By following the PRISMA statement guidelines, a thorough literature search yielded 21 relevant articles, forming the basis of this systematic review. The review provides a comprehensive summary and detailed description of the methodologies, protocols, software tools, and noteworthy drug candidates identified in these studies. Additionally, it sheds light on the crucial molecular interactions by presenting an overview of the interacting residues elucidated in the reviewed articles, offering valuable insights for effective therapeutic interventions. Furthermore, the review presents thought-provoking suggestions for future research directions, aiming to inspire and guide advancements in drug development efforts.