Discovery of Novel ROCK1 Inhibitors via Integrated Virtual Screening Strategy and Bioassays

Recent advances in the development of Rho kinase inhibitors (2015-2021)

Rho-associated coiled-coil kinases (ROCKs) are key downstream effectors of small GTPases. ROCK plays a central role in diverse cellular events with accumulating evidence supporting the concept that ROCK is important in tumor development and progression. Numerous ROCK inhibitors have been investigated for their therapeutic potential in the treatment of cancers. In this article, we review recent research progress on ROCK inhibitors, especially those with potential for the treatment of cancers, reported in the literature from 2015 to 2021. Most ROCK inhibitors show potent in vitro and in vivo antitumor activities and have potential in the treatment of cancers.

Discovery of Novel HPK1 Inhibitors Through Structure-Based Virtual Screening

Hematopoietic progenitor kinase (HPK1) is a negative regulator of T-cell receptor and B-cell signaling, which has been recognized as a novel antitumor target for immunotherapy. In this work, Glide docking-based virtual screening and kinase inhibition assay were performed to identify novel HPK1 inhibitors. The kinase inhibition assay results demonstrated five compounds with IC₅₀ values below 20 μM, and the most potent one (compound M074-2865) had an IC₅₀ value of 2.93 ± 0.09 μM. Molecular dynamics (MD) simulations were performed to delve into the interaction of sunitinib and the identified compound M074-2865 with the kinase domain of HPK1. The five compounds identified in this work could be considered promising hit compounds for further development of HPK1 inhibitors for immunotherapy.

A multi-conformational virtual screening approach based on machine learning targeting PI3Kγ

Nowadays, more and more attention has been attracted to develop selective PI3Kγ inhibitors, but the unique structural features of PI3Kγ protein make it a very big challenge. In the present study, a virtual screening strategy based on machine learning with multiple PI3Kγ protein structures was developed to screen novel PI3Kγ inhibitors. First, six mainstream docking programs were chosen to evaluate their scoring power and screening power; CDOCKER and Glide show satisfactory reliability and accuracy against the PI3Kγ system. Next, virtual screening integrating multiple PI3Kγ protein structures was demonstrated to significantly improve the screening enrichment rate comparing to that with an individual protein structure. Last, a multi-conformational Naïve Bayesian Classification model with the optimal docking programs was constructed, and it performed a true capability in the screening of PI3Kγ inhibitors. Taken together, the current study could provide some guidance for the docking-based virtual screening to discover novel PI3Kγ inhibitors.

Discovery of novel IDO1 inhibitors via structure-based virtual screening and biological assays

Indoleamine 2,3-dioxygenase 1 (IDO1) is a heme-containing enzyme that catalyzes the first and rate-limiting step in catabolism of tryptophan via the kynurenine pathway, which plays a pivotal role in the proliferation and differentiation of T cells. IDO1 has been proven to be an attractive target for many diseases, such as breast cancer, lung cancer, colon cancer, prostate cancer, etc. In this study, docking-based virtual screening and bioassays were conducted to identify novel inhibitors of IDO1. The cellular assay demonstrated that 24 compounds exhibited potent inhibitory activity against IDO1 at micromolar level, including 8 compounds with IC₅₀ values below 10 μM and the most potent one (compound 1) with IC₅₀ of 1.18 ± 0.04 μM. Further lead optimization based on similarity searching strategy led to the discovery of compound 28 as an excellent inhibitor with IC₅₀ of 0.27 ± 0.02 μM. Then, the structure-activity relationship of compounds 1, 2, 8 and 14 analogues is discussed. The interaction modes of two compounds against IDO1 were further explored through a Python Based Metal Center Parameter Builder (MCPB.py) molecular dynamics simulation, binding free energy calculation and electrostatic potential analysis. The novel IDO1 inhibitors of compound 1 and its analogues could be considered as promising scaffold for further development of IDO1 inhibitors.

Structure-based design of small molecule and peptide inhibitors for selective targeting of ROCK1: an integrative computational approach

Rho-associated, coiled-coil-containing protein kinase (ROCK1) regulates cell contraction, morphology, and motility by phosphorylating its downstream targets. ROCK1 is a proven target for many pathological conditions like cancer, atherosclerosis, glaucoma, neuro-degeneration, etc. Though many kinase inhibitors are available, there is a dearth of studies on repurposing approved drugs and novel peptide inhibitors that could potentially target ROCK1. Hence, in this study, an extensive integration of open-source pipelines was employed to probe the potential inhibitors (ligand/peptide) for targeting ROCK1. To start with, a systematic enrichment analysis was performed to delineate the most optimal ROCK1 crystal structure that can be harnessed for drug design. A comparative analysis of conformational flexibility between monomeric and dimeric forms was also performed to prioritize the optimal assembly for structural studies. Subsequently, Virtual screening of FDA-approved drugs in Drugbank was performed using POAP pipeline. Further, the top hits were probed for binding affinity, crucial interaction fingerprints, and complex stability during MD simulation. In parallel, a combinatorial tetrapeptide library was also virtually screened against ROCK1 using the PepVis pipeline. Following which, all these shortlisted inhibitors (compounds/peptides) were subjected to Kinomerun analysis to infer other potential kinase targets. Finally, Polydatin and conivaptan were prioritized as the most potential repurposable inhibitors, and WWWF, WWVW as potential inhibitory peptides for targeting ROCK1. The prioritized inhibitors are highly promising for use in therapeutics, as these are resultants of the multilevel stringent filtration process. The computational strategies implemented in this study could potentially serve as a scaffold towards selective inhibitor design for other kinases.Communicated by Ramaswamy H. Sarma.

Molecules against Covid-19: An in silico approach for drug development

A large number of deaths have been caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) worldwide, turning it into a serious and momentous threat to public health. This study tends to contribute to the development of effective treatment strategies through a computational approach, investigating the mechanisms in relation to the binding and subsequent inhibition of SARS-CoV-2 ribonucleic acid (RNA)-dependent RNA polymerase (RdRp). Molecular docking was performed to screen six naturally occurring molecules with antineoplastic properties (Ellipticine, Ecteinascidin, Homoharringtonine, Dolastatin 10, Halichondrin, and Plicamycin). Absorption, distribution, metabolism, and excretion (ADME) investigation was also conducted to analyze the drug-like properties of these compounds. The docked results have clearly shown binding of ligands to the SARS-CoV-2 RdRp protein. Interestingly, all ligands were found to obey Lipinski’s rule of five. These results provide a basis for repurposing and using molecules, derived from plants and animals, as a potential treatment for the coronavirus disease 2019 (COVID-19) infection as they could be effective therapeutics for the same.

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

Rho-kinase (ROCK) Inhibitors - A Neuroprotective Therapeutic Paradigm with a Focus on Ocular Utility

BACKGROUND

Glaucoma is a progressive optic neuropathy causing visual impairment and Retinal Ganglionic Cells (RGCs) death gradually posing a need for neuroprotective strategies to minimize the loss of RGCs and visual field. It is recognized as a multifactorial disease, Intraocular Pressure (IOP) being the foremost risk factor. ROCK inhibitors have been probed for various possible indications, such as myocardial ischemia, hypertension, kidney diseases. Their role in neuroprotection and neuronal regeneration has been suggested to be of value in the treatment of neurological diseases, like spinal-cord injury, Alzheimer's disease and multiple sclerosis but recently Rho-associated Kinase inhibitors have been recognized as potential antiglaucoma agents.

EVIDENCE SYNTHESIS

Rho-Kinase is a serine/threonine kinase with a kinase domain which is constitutively active and is involved in the regulation of smooth muscle contraction and stress fibre formation. Two isoforms of Rho-Kinase, ROCK-I (ROCK β) and ROCK-II (ROCK α) have been identified. ROCK II plays a pathophysiological role in glaucoma and hence the inhibitors of ROCK may be beneficial to ameliorate the vision loss. These inhibitors decrease the intraocular pressure in the glaucomatous eye by increasing the aqueous humour outflow through the trabecular meshwork pathway. They also act as anti-scarring agents and hence prevent post-operative scarring after the glaucoma filtration surgery. Their major role involves axon regeneration by increasing the optic nerve blood flow which may be useful in treating the damaged optic neurons. These drugs act directly on the neurons in the central visual pathway, interrupting the RGC apoptosis and therefore serve as a novel pharmacological approach for glaucoma neuroprotection.

CONCLUSION

Based on the results of high-throughput screening, several Rho kinase inhibitors have been designed and developed comprising of diverse scaffolds exhibiting Rho kinase inhibitory activity from micromolar to subnanomolar ranges. This diversity in the scaffolds with inhibitory potential against the kinase and their SAR development will be intricated in the present review. Ripasudil is the only Rho kinase inhibitor marketed to date for the treatment of glaucoma. Another ROCK inhibitor AR-13324 has recently passed the clinical trials whereas AMA0076, K115, PG324, Y39983 and RKI-983 are still under trials. In view of this, a detailed and updated account of ROCK II inhibitors as the next generation therapeutic agents for glaucoma will be discussed in this review.

Ensemble docking-based virtual screening toward identifying inhibitors against Wee1 kinase

Aim: Wee1 kinase plays a key role in the arrest of G2/M checkpoint that prevents mitotic entry in response to DNA damage. This work is to discover potent Wee1 inhibitors which can be considered valuable. Materials & Methods: Herein, Ensemble docking using multiple crystal structures was considered an effective strategy in the virtual screening. The performance of 17 scoring functions obtained from different docking software was evaluated for molecular docking. Results: Two novel compounds B1 and A2 were identified as Wee1 inhibitors with IC₅₀ values of 10.23 ± 0.505 and 8.72 ± 0.323 μM, respectively. Further cell viability assay demonstrated that the two active compounds exhibited good anticancer activities. Conclusion: This provides a meaningful starting point for further structure optimization to discover more potent Wee1 inhibitors.

Combined strategies in structure-based virtual screening

The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.

Neuroprotective Effects of BHDPC, a Novel Neuroprotectant, on Experimental Stroke by Modulating Microglia Polarization

This study mainly investigated the therapeutic effects of BHDPC on ischemic stroke and its underlying mechanisms. In vivo, the transient middle cerebral artery occlusion (MCAO) was used to induce ischemic model. In vitro, oxygen and glucose deprivation/reperfusion (OGD/R)-induced ischemic stroke in BV-2 microglia and primary neurons, and bEnd.3 mouse cerebral microvascular endothelial cells (ECs) were also used. First, we found that BHDPC exerts considerable neuroprotection against MCAO-induced ischemic injury to mice via alleviating neurological deficits and brain infarcts, inhibiting neuronal cell loss and apoptosis, and attenuating blood-brain barrier disruption and tight junction protein changes. Next, we observed that BHDPC significantly reduced microglial M1 activation but enhanced M2 polarization in MCAO-induced ischemic brain. Further experiments in vitro indicated that BHDPC suppressed microglial activation but promoted M2 microglial polarization in OGD/R-induced BV-2 microglia. In addition, conditioned medium (CM) experiments showed that CM from BHDPC-treated BV-2 microglia provided protections against OGD/R-induced ischemic damage in primary neurons and bEnd.3 ECs. Moreover, we found that BHDPC actions on microglial inflammation were associated with the inactivation of NF-κB signaling. Interestingly, we also found that BHDPC enhanced phosphorylation of protein kinase A (PKA) and cAMP-response element-binding protein (CREB). The pharmacological inhibition or gene knockdown of PKA/CREB signaling diminished BHDPC-promoted microglial M2 polarization. In summary, BHDPC conferred neuroprotection against ischemic injury in experimental stroke models. Modulating microglial activation and polarization contributes to BHDPC-mediated neuroprotective actions, which in part were mediated by nuclear factor kappa B and PKA/CREB signaling pathway.

Importance of Incorporating Protein Flexibility in Molecule Modeling: A Theoretical Study on Type I1/2 NIK Inhibitors

NF-κB inducing kinase (NIK), which is considered as the central component of the non-canonical NF-κB pathway, has been proved to be an important target for the regulation of the immune system. In the past few years, NIK inhibitors with various scaffolds have been successively reported, among which type I^1/2 inhibitors that can not only bind in the ATP-binding pocket at the DFG-in state but also extend into an additional back pocket, make up the largest proportion of the NIK inhibitors, and are worthy of more attention. In this study, an integration protocol that combines molecule docking, MD simulations, ensemble docking, MM/GB(PB)SA binding free energy calculations, and decomposition was employed to understand the binding mechanism of 21 tricyclic type I^1/2 NIK inhibitors. It is found that the docking accuracy is largely dependent on the selection of docking protocols as well as the crystal structures. The predictions given by the ensemble docking based on multiple receptor conformations (MRCs) and the MM/GB(PB)SA calculations based on MD simulations showed higher linear correlations with the experimental data than those given by conventional rigid receptor docking (RRD) methods (Glide, GOLD, and Autodock Vina), highlighting the importance of incorporating protein flexibility in predicting protein–ligand interactions. Further analysis based on MM/GBSA demonstrates that the hydrophobic interactions play the most essential role in the ligand binding to NIK, and the polar interactions also make an important contribution to the NIK-ligand recognition. A deeper comparison of several pairs of representative derivatives reveals that the hydrophobic interactions are vitally important in the structural optimization of analogs as well. Besides, the H-bond interactions with some key residues and the large desolvation effect in the back pocket devote to the affinity distinction. It is expected that our study could provide valuable insights into the design of novel and potent type I^1/2 NIK inhibitors.

Chemical biology approaches targeting the actin cytoskeleton through phenotypic screening

The actin cytoskeleton is dysregulated in cancer, yet this critical cellular machinery has not translated as a druggable clinical target due to cardio-toxic side-effects. Many actin regulators are also considered undruggable, being structural proteins lacking clear functional sites suitable for targeted drug design. In this review, we discuss opportunities and challenges associated with drugging the actin cytoskeleton through its structural regulators, taking tropomyosins as a target example. In particular, we highlight emerging data acquisition and analysis trends driving phenotypic, imaging-based compound screening. Finally, we consider how the confluence of these trends is now bringing functionally integral machineries such as the actin cytoskeleton, and associated structural regulatory proteins, into an expanded repertoire of druggable targets with previously unexploited clinical potential.

Design, synthesis, and biological evaluation of novel phenol ether derivatives as non-covalent proteasome inhibitors

A series of novel phenol ether derivatives were designed, synthesized, and evaluated as non-covalent proteasome inhibitors. Most compounds exhibited moderate to excellent proteasome inhibitory activity. In particular, compound 18x proved to be the most potent compound (chymotrypsin-like: IC₅₀ = 49 nM), exhibiting a 2-fold higher potency compared to the reported PI-1840. Besides, compound 18x exhibited excellent metabolic stability and selective anti-proliferative activity against solid cancer cell lines including HepG2 and HGC27, providing incentive for the further development as a potential anticancer agent against solid cancers.

BHDPC Is a Novel Neuroprotectant That Provides Anti-neuroinflammatory and Neuroprotective Effects by Inactivating NF-κB and Activating PKA/CREB

Microglia-mediated neuroinflammatory responses are inevitable and important pathological processes in several kinds of disorder of the central nervous system (CNS). Therefore, alleviating activated microglia-induced inflammatory process might be a valuable therapeutic approach to neuroinflammation-related diseases. In the present study, we investigated BHDPC, a novel neuroprotectant discovered in our previous study that had anti-inflammatory effects under neuroinflammatory conditions. First, we found that BHDPC could inhibit neuroinflammatory responses and promote microglial M2 phenotype polarization in both lipopolysaccharide (LPS)-activated BV-2 microglia l cells. Furthermore, BHDPC provided protective actions against neuroinflammation-induced neurotoxicity in HT22 mouse hippocampal cells co-cultured with activated BV-2 microglia. Further experiments demonstrated that BHDPC could suppress LPS-induced activation of transcription factor nuclear factor kappa B (NF-κB) via interfering with the degradation of the inhibitor of kappa B (IκB) and phosphorylation of IκB, the IκB kinase (IKK). Moreover, we also found that BHDPC could induce phosphorylation of cAMP-dependent protein kinase A (PKA) and cAMP-response element-binding protein (CREB) in BV-2 microglial cells. Also, using the PKA-specific inhibitor, we found that BHDPC-induced CREB phosphorylation was dependent on PKA, which also contributed to BHDPC-mediated anti-inflammation and neuroprotection.

Efficient iterative virtual screening with Apache Spark and conformal prediction

Background

Docking and scoring large libraries of ligands against target proteins forms the basis of structure-based virtual screening. The problem is trivially parallelizable, and calculations are generally carried out on computer clusters or on large workstations in a brute force manner, by docking and scoring all available ligands.

Contribution

In this study we propose a strategy that is based on iteratively docking a set of ligands to form a training set, training a ligand-based model on this set, and predicting the remainder of the ligands to exclude those predicted as ‘low-scoring’ ligands. Then, another set of ligands are docked, the model is retrained and the process is repeated until a certain model efficiency level is reached. Thereafter, the remaining ligands are docked or excluded based on this model. We use SVM and conformal prediction to deliver valid prediction intervals for ranking the predicted ligands, and Apache Spark to parallelize both the docking and the modeling.

Results

We show on 4 different targets that conformal prediction based virtual screening (CPVS) is able to reduce the number of docked molecules by 62.61% while retaining an accuracy for the top 30 hits of 94% on average and a speedup of 3.7. The implementation is available as open source via GitHub (https://github.com/laeeq80/spark-cpvs) and can be run on high-performance computers as well as on cloud resources.

ProSelection: A Novel Algorithm to Select Proper Protein Structure Subsets for in Silico Target Identification and Drug Discovery Research

Molecular docking is widely applied to computer-aided drug design and has become relatively mature in the recent decades. Application of docking in modeling varies from single lead compound optimization to large-scale virtual screening. The performance of molecular docking is highly dependent on the protein structures selected. It is especially challenging for large-scale target prediction research when multiple structures are available for a single target. Therefore, we have established ProSelection, a docking preferred-protein selection algorithm, in order to generate the proper structure subset(s). By the ProSelection algorithm, protein structures of "weak selectors" are filtered out whereas structures of "strong selectors" are kept. Specifically, the structure which has a good statistical performance of distinguishing active ligands from inactive ligands is defined as a strong selector. In this study, 249 protein structures of 14 autophagy-related targets are investigated. Surflex-dock was used as the docking engine to distinguish active and inactive compounds against these protein structures. Both t test and Mann-Whitney U test were used to distinguish the strong from the weak selectors based on the normality of the docking score distribution. The suggested docking score threshold for active ligands (SDA) was generated for each strong selector structure according to the receiver operating characteristic (ROC) curve. The performance of ProSelection was further validated by predicting the potential off-targets of 43 U.S. Federal Drug Administration approved small molecule antineoplastic drugs. Overall, ProSelection will accelerate the computational work in protein structure selection and could be a useful tool for molecular docking, target prediction, and protein-chemical database establishment research.

Discovery of a ROCK inhibitor, FPND, which prevents cerebral hemorrhage through maintaining vascular integrity by interference with VE-cadherin

Hemorrhagic stroke occurs when a weakened vessel ruptures and bleeds into the surrounding brain, leading to high rates of death and disability worldwide. A series of complex pathophysiological cascades contribute to the risk of hemorrhagic stroke, and no therapies have proven effective to prevent hemorrhagic stroke. Stabilization of vascular integrity has been considered as a potential therapeutic target for hemorrhagic stroke. ROCKs, which belong to the serine/threonine protein kinase family and participate in the organization of actin cytoskeleton, have become attractive targets for the treatment of strokes. In this study, in vitro enzyme-based assays revealed that a new compound (FPND) with a novel scaffold identified by docking-based virtual screening could inhibit ROCK1 specifically at low micromolar concentration. Molecular modeling showed that FPND preferentially interacted with ROCK1, and the difference between the binding affinity of FPND toward ROCK1 and ROCK2 primarily resulted from non-polar contributions. Furthermore, FPND significantly prevented statin-induced cerebral hemorrhage in a zebrafish model. In addition, in vitro studies using the xCELLigence RTCA system, immunofluorescence and western blotting revealed that FPND prevented statin-induced cerebral hemorrhage by enhancing endothelial cell–cell junctions through inhibiting the ROCK-mediated VE-cadherin signaling pathway. As indicated by the extremely low toxicity of FPND against mice, it is safe and can potentially prevent vascular integrity loss-related diseases, such as hemorrhagic stroke.

Discovery of Novel and Selective Adenosine A2A Receptor Antagonists for Treating Parkinson's Disease through Comparative Structure-Based Virtual Screening

Among non-dopaminergic strategies for combating Parkinson's disease (PD), antagonism of the A_2A adenosine receptor (AR) has emerged to show great potential. In this study, on the basis of two crystal structures of the A_2A AR with the best capability to distinguish known antagonists from decoys, docking-based virtual screening (VS) was conducted to identify novel A_2A AR antagonists. A total of 63 structurally diverse compounds identified by VS were submitted to experimental testing, and 11 of them exhibited substantial activity against the A_2A AR (K_i < 10 μM), including two compounds with K_i below 1 μM (compound 43, 0.42 μM; compound 51, 0.27 μM) and good A_2A/A₁ selectivity (fold < 0.1). Compounds 43 and 51 demonstrated antagonistic activity according to the results of cAMP measurements (cAMP IC₅₀ = 1.67 and 1.80 μM, respectively) and showed good efficacy in the haloperidol-induced catalepsy (HIC) rat model for PD at doses of up to 30 mg/kg. Further lead optimization based on a substructure searching strategy led to the discovery of compound 84 as an excellent A_2A AR antagonist (A_2A K_i = 54 nM, A_2A/A₁ fold < 0.1, cAMP IC₅₀ = 0.3 μM) that exhibited significant improvement in anti-PD efficacy in the HIC rat model.

Constructing and Validating High-Performance MIEC-SVM Models in Virtual Screening for Kinases: A Better Way for Actives Discovery

The MIEC-SVM approach, which combines molecular interaction energy components (MIEC) derived from free energy decomposition and support vector machine (SVM), has been found effective in capturing the energetic patterns of protein-peptide recognition. However, the performance of this approach in identifying small molecule inhibitors of drug targets has not been well assessed and validated by experiments. Thereafter, by combining different model construction protocols, the issues related to developing best MIEC-SVM models were firstly discussed upon three kinase targets (ABL, ALK, and BRAF). As for the investigated targets, the optimized MIEC-SVM models performed much better than the models based on the default SVM parameters and Autodock for the tested datasets. Then, the proposed strategy was utilized to screen the Specs database for discovering potential inhibitors of the ALK kinase. The experimental results showed that the optimized MIEC-SVM model, which identified 7 actives with IC50 < 10 μM from 50 purchased compounds (namely hit rate of 14%, and 4 in nM level) and performed much better than Autodock (3 actives with IC50 < 10 μM from 50 purchased compounds, namely hit rate of 6%, and 2 in nM level), suggesting that the proposed strategy is a powerful tool in structure-based virtual screening.

A review on ROCK-II inhibitors: From molecular modelling to synthesis

Rho kinase enzyme expressed in different disease conditions and involved in mediating vasoconstriction and vascular remodeling in the pathogenesis. There are two isoforms of Rho kinases, namely ROCK I and ROCK II, responsible for different physiological function due to difference in distribution, but almost similar in structure. The Rho kinase 2 belongs to AGC family and is widely distributed in brain, heart and muscles. It is responsible for contraction of vascular smooth muscles by calcium sensitization. Its defective and unwanted expression can lead to many medical conditions like multiple sclerosis, myocardial ischemia, inflammatory responses, etc. Many Rho kinase 1 and 2 inhibitors have been designed for Rho/Rho kinase pathway by use of molecular modeling studies. Most of the designed compounds have been modeled based on ROCK 1 enzyme. This article is focused on Rho kinase 2 inhibitors as there are many ways to improvise by use of Computer aided drug designing as very less quantum of research work carried out. Herein, the article highlights different stages of designing like docking, SAR and synthesis of ROCK inhibitors and recent advances. It also highlights future prospective to improve the activity.