Exploring the cause of the inhibitor 4AX attaching to binding site disrupting protein tyrosine phosphatase 4A1 trimerization by molecular dynamic simulation

In silico exploration of the potential inhibitory activities of in-house and ZINC database lead compounds against alpha-glucosidase using structure-based virtual screening and molecular dynamics simulation approach

Inhibitors of α-glucosidase have been used to treat type-2 diabetes (T2DM) by preventing the breakdown of carbohydrates into glucose and prevent enhancing glucose conversion. Structure-based virtual screening (SBVS) was used to generate novel chemical scaffold-ligand α-glucosidase inhibitors. The databases were screened against the receptor α-glucosidase using SBVS and molecular dynamics simulation (MDS) techniques in this study. Based on molecular docking studies, three and two compounds of α-glucosidase inhibitors were chosen from a commercial database (ZINC) and an In-house database for this study respectively. The mode of binding interactions of the selected compounds later predicted their α-glucosidase inhibitory potential. Finally, one out of three lead compound from ZINC and one out of two lead compound from In-house database were shortlisted based on interactions. Furthermore, MDS and post-MDS strategies were used to refine and validate the shortlisted leads along with the reference acarbose/α-glucosidase. The Hits' ability to inhibit α-glucosidase was predicted by SBVS, indicating that these compounds have good inhibitory activities. The lead inhibitor's structure may serve as templates for the design of novel inhibitors, and in vitro testing to confirm their anti-diabetic potential is necessary. These insights can help rationally design new effective anti-diabetic drugs.

Salt-bridge mediated conformational dynamics in the figure-of-eight knotted ketol acid reductoisomerase (KARI)

The utility of knotted proteins in biological activities has been ambiguous since their discovery. From their evolutionary significance to their functionality in stabilizing the native protein structure, a unilateral conclusion hasn't been achieved yet. While most studies have been performed to understand the stabilizing effect of the knotted fold on the protein chain, more ideas are yet to emerge regarding the interactions in stabilizing the knot. Using classical molecular dynamics (MD) simulations, we have explored the dynamics of the figure-of-eight knotted domain present in ketol acid reductoisomerase (KARI). Our main focus was on the presence of a salt bridge network evident within the knotted region and its role in shaping the conformational dynamics of the knotted chain. Through the potential of mean forces (PMFs) calculation, we have also marked the specific salt bridges that are pivotal in stabilizing the knotted structure. The correlated motions have been further monitored with the help of principal component analysis (PCA) and dynamic cross-correlation maps (DCCM). Furthermore, mutation of the specific salt bridges led to a change in their conformational stability, vindicating their importance.

Machine learning-based drug design for identification of thymidylate kinase inhibitors as a potential anti-Mycobacterium tuberculosis

The rise of antibiotic-resistant Mycobacterium tuberculosis (Mtb) has reduced the availability of medications for tuberculosis therapy, resulting in increased morbidity and mortality globally. Tuberculosis spreads from the lungs to other parts of the body, including the brain and spine. Developing a single drug can take several decades, making drug discovery costly and time-consuming. Machine learning algorithms like support vector machines (SVM), k-nearest neighbor (k-NN), random forest (RF) and Gaussian naive base (GNB) are fast and effective and are commonly used in drug discovery. These algorithms are ideal for the virtual screening of large compound libraries to classify molecules as active or inactive. For the training of the models, a dataset of 307 was downloaded from BindingDB. Among 307 compounds, 85 compounds were labeled as active, having an IC50 below 58 mM, while 222 compounds were labeled inactive against thymidylate kinase, with 87.2% accuracy. The developed models were subjected to an external ZINC dataset of 136,564 compounds. Furthermore, we performed the 100-ns dynamic simulation and post trajectories analysis of compounds having good interaction and score in molecular docking. As compared to the standard reference compound, the top three hits revealed greater stability and compactness. In conclusion, our predicted hits can inhibit thymidylate kinase overexpression to combat Mycobacterium tuberculosis.Communicated by Ramaswamy H. Sarma.

Can de-phosphorylation of serine-5 in the C-terminal domain of human polymerase II affect its interaction with the PA C-terminal domain of bat Flu A polymerase?

Influenza viruses depend on the host transcription machinery to create their new progeny viral mRNA. They need the host transcription machinery to hijack the 5'-capped RNA from the host RNAs in order to utilize them to activate their viral transcription. In this study, we computationally regenerated the interaction between 3 heptad repeats, phosphorylated at the fifth serine residue in each repeat, from human polymerase and the CT D of the PA subunit of viral RNA polymerase (Holo 3SEP). We also studied the effect of the de-phosphorylation of the Serine-5 in the middle heptad repeat on the stability of the interaction (Holo 2SEP). The dynamics of the protein association and the heptad repeat in both cases are studied using appropriate in silico tools. This is followed by applying the MM-GBSA method based on relative binding estimation to show the effect of the de-phosphorylation of the middle Serine-5. Results indicate a clear change in total relative binding energy in Holo 2SEP, compared to Holo 3SEP, with no shift in occupied amino acids involved in the interaction in both cases. Knowing that de-phosphorylation of one serine-5 has no significant contribution to the investigated interactions opens the door for further studies to understand the role of the middle heptad serine-5 in these interactions, as its dephosphorylation caused a decrease by ≈13% in the binding affinity values obtained using MM-GBSA. The current in silico study represents a one-step-ahead insight into the RNA-dependent RNA polymerase (RdRP) mechanism that is yet to be verified in the lab.Communicated by Ramaswamy H. Sarma.

New lead compounds identification against KRas mediated cancers through pharmacophore-based virtual screening and in vitro assays

Cancer remains the leading cause of mortality and morbidity in the world, with 19.3 million new diagnoses and 10.1 million deaths in 2020. Cancer is caused due to mutations in proto-oncogenes and tumor-suppressor genes. Genetic analyses found that Ras (Rat sarcoma) is one of the most deregulated oncogenes in human cancers. The Ras oncogene family members including NRas (Neuroblastoma ras viral oncogene homolog), HRas (Harvey rat sarcoma) and KRas are involved in different types of human cancers. The mutant KRas is considered as the most frequent oncogene implicated in the development of lung, pancreatic and colon cancers. However, there is no efficient clinical drug even though it has been identified as an oncogene for 30 years. Therefore there is an emerging need to develop potent, new anticancer drugs. In this study, computer-aided drug designing approaches as well as experimental methods were employed to find new and potential anti-cancer drugs. The pharmacophore model was developed from an already known FDA approved anti-cancer drug Bortezomib using the software MOE. The validated pharmacophore model was then used to screen the in-house and commercially available databases. The pharmacophore-based virtual screening resulted in 26 and 86 hits from in-house and commercial databases respectively. Finally, 6/13 (in-house database) and 24/64 hits (commercial databases) were selected with different scaffolds having good interactions with the significant active residues of KRasG12D protein that were predicted as potent lead compounds. Finally, the results of pharmacophore-based virtual screening were further validated by molecular dynamics simulation analysis. The 6 hits of the in-house database were further evaluated experimentally. The experimental results showed that these compounds have good anti-cancer activity which validate the protocol of our in silico studies. KRasG12D protein is a very important anti-cancer target and potent inhibitors for this target are still not available, so small lead compound inhibitors were identified to inhibit the activity of this protein by blocking the GTP-binding pocket.Communicated by Ramaswamy H. Sarma.

Identification of Drug Targets and Their Inhibitors in Yersinia pestis Strain 91001 through Subtractive Genomics, Machine Learning, and MD Simulation Approaches

Yersinia pestis, the causative agent of plague, is a Gram-negative bacterium. If the plague is not properly treated it can cause rapid death of the host. Bubonic, pneumonic, and septicemic are the three types of plague described. Bubonic plague can progress to septicemic plague, if not diagnosed and treated on time. The mortality rate of pneumonic and septicemic plague is quite high. The symptom-defining disease is the bubo, which is a painful lymph node swelling. Almost 50% of bubonic plague leads to sepsis and death if not treated immediately with antibiotics. The host immune response is slow as compared to other bacterial infections. Clinical isolates of Yersinia pestis revealed resistance to many antibiotics such as tetracycline, spectinomycin, kanamycin, streptomycin, minocycline, chloramphenicol, and sulfonamides. Drug discovery is a time-consuming process. It always takes ten to fifteen years to bring a single drug to the market. In this regard, in silico subtractive proteomics is an accurate, rapid, and cost-effective approach for the discovery of drug targets. An ideal drug target must be essential to the pathogen's survival and must be absent in the host. Machine learning approaches are more accurate as compared to traditional virtual screening. In this study, k-nearest neighbor (kNN) and support vector machine (SVM) were used to predict the active hits against the beta-ketoacyl-ACP synthase III drug target predicted by the subtractive genomics approach. Among the 1012 compounds of the South African Natural Products database, 11 hits were predicted as active. Further, the active hits were docked against the active site of beta-ketoacyl-ACP synthase III. Out of the total 11 active hits, the 3 lowest docking score hits that showed strong interaction with the drug target were shortlisted along with the standard drug and were simulated for 100 ns. The MD simulation revealed that all the shortlisted compounds display stable behavior and the compounds formed stable complexes with the drug target. These compounds may have the potential to inhibit the beta-ketoacyl-ACP synthase III drug target and can help to combat Yersinia pestis-related infections. The dataset and the source codes are freely available on GitHub.

New drug target identification in Vibrio vulnificus by subtractive genome analysis and their inhibitors through molecular docking and molecular dynamics simulations

Vibrio vulnificus is a rod shape, Gram-negative bacterium that causes sepsis (with a greater than 50% mortality rate), necrotizing fasciitis, gastroenteritis, skin, and soft tissue infection, wound infection, peritonitis, meningitis, pneumonia, keratitis, and arthritis. Based on pathogenicity V. vulnificus is categorized into three biotypes. Type 1 and type 3 cause diseases in humans while biotype 2 causes diseases in eel and fish. Due to indiscriminate use of antibiotics V. vulnificus has developed resistance to many antibiotics so curing is dramatically a challenge. V. vulnificus is resistant to cefazolin, streptomycin, tetracycline, aztreonam, tobramycin, cefepime, and gentamycin. Subtractive genome analysis is the most effective method for drug target identification. The method is based on the subtraction of homologous proteins from both pathogen and host. By this process set of proteins present only in the pathogen and perform essential functions in the pathogen can be identified. The entire proteome of Vibrio vulnificus strain ATCC 27562 was reduced step by step to a single protein predicted as the drug target. AlphaFold2 is one of the applications of deep learning algorithms in biomedicine and is correctly considered the game changer in the field of structural biology. Accuracy and speed are the major strength of AlphaFold2. In the PDB database, the crystal structure of the predicted drug target was not present, therefore the Colab notebook was used to predict the 3D structure by the AlphaFold2, and subsequently, the predicted model was validated. Potent inhibitors against the new target were predicted by virtual screening and molecular docking study. The most stable compound ZINC01318774 tightly attaches to the binding pocket of bisphosphoglycerate-independent phosphoglycerate mutase. The time-dependent molecular dynamics simulation revealed compound ZINC01318774 was superior as compared to the standard drug tetracycline in terms of stability. The availability of V. vulnificus strain ATCC 27562 has allowed in silico identification of drug target which will provide a base for the discovery of specific therapeutic targets against Vibrio vulnificus.

In Silico Identification of Lead Compounds for Pseudomonas Aeruginosa PqsA Enzyme: Computational Study to Block Biofilm Formation

Pseudomonas aeruginosa is an opportunistic Gram-negative bacterium implicated in acute and chronic nosocomial infections and a leading cause of patient mortality. Pseudomonas aeruginosa infections are frequently associated with the development of biofilms, which give the bacteria additional drug resistance and increase their virulence. The goal of this study was to find strong compounds that block the Anthranilate-CoA ligase enzyme made by the pqsA gene. This would stop the P. aeruginosa quorum signaling system. This enzyme plays a crucial role in the pathogenicity of P. aeruginosa by producing autoinducers for cell-to-cell communication that lead to the production of biofilms. Pharmacophore-based virtual screening was carried out utilizing a library of commercially accessible enzyme inhibitors. The most promising hits obtained during virtual screening were put through molecular docking with the help of MOE. The virtual screening yielded 7/160 and 10/249 hits (ZINC and Chembridge). Finally, 2/7 ZINC hits and 2/10 ChemBridge hits were selected as potent lead compounds employing diverse scaffolds due to their high pqsA enzyme binding affinity. The results of the pharmacophore-based virtual screening were subsequently verified using a molecular dynamic simulation-based study (MDS). Using MDS and post-MDS, the stability of the complexes was evaluated. The most promising lead compounds exhibited a high binding affinity towards protein-binding pocket and interacted with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation.

In-Silico Lead Druggable Compounds Identification against SARS COVID-19 Main Protease Target from In-House, Chembridge and Zinc Databases by Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations

Pharmacological strategies to lower the viral load among patients suffering from severe diseases were researched in great detail during the SARS-CoV-2 outbreak. The viral protease M^pro (3CLpro) is necessary for viral replication and is among the main therapeutic targets proposed, thus far. To stop the pandemic from spreading, researchers are working to find more effective M^pro inhibitors against SARS-CoV-2. The 33.8 kDa M^pro protease of SARS-CoV-2, being a nonhuman homologue, has the possibility of being utilized as a therapeutic target against coronaviruses. To develop drug-like compounds capable of preventing the replication of SARS-main CoV-2's protease (M^pro), a computer-aided drug design (CADD) approach is extremely viable. Using MOE, structure-based virtual screening (SBVS) of in-house and commercial databases was carried out using SARS-CoV-2 proteins. The most promising hits obtained during virtual screening (VS) were put through molecular docking with the help of MOE. The virtual screening yielded 3/5 hits (in-house database) and 56/66 hits (commercial databases). Finally, 3/5 hits (in-house database), 3/5 hits (ZINC database), and 2/7 hits (ChemBridge database) were chosen as potent lead compounds using various scaffolds due to their considerable binding affinity with M^pro protein. The outcomes of SBVS were then validated using an analysis based on molecular dynamics simulation (MDS). The complexes' stability was tested using MDS and post-MDS. The most promising candidates were found to exhibit a high capacity for fitting into the protein-binding pocket and interacting with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation.

Discovery of novel natural products as dual MNK/PIM inhibitors for acute myeloid leukemia treatment: Pharmacophore modeling, molecular docking, and molecular dynamics studies

MNK-2 and PIM-2 kinases play an indispensable role in cell proliferation signaling pathways linked to tyrosine kinase inhibitors resistance. In this study, pharmacophore modeling studies have been conducted on the co-crystalized ligands of MNK-2 and PIM-2 enzyme crystal structures to determine the essential features required for the identification of potential dual inhibitors. The obtained pharmacophore features were then screened against a library of 270,540 natural products from the ZINC database. The matched natural molecules were docked into the binding sites of MNK-2 and PIM-2 enzymes. The compounds with high docking scores with the two enzymes were further subjected to MM-GBSA calculations and ADME prediction. This led to the identification of compound 1 (ZINC000085569211), compound 2 (ZINC000085569178), and compound 3 (ZINC000085569190), with better docking scores compared to the reference co-crystallized ligands of MNK-2 and PIM-2. Moreover, compounds 1‒3 displayed better MM-GBSA binding free energies compared to the reference ligands. Finally, molecular dynamics (MD) study was used to assess the interaction stability of the compounds with MNK-2. To this end, compounds 1 and 3 bound strongly to the target during the whole period of MD simulation. The findings of the current study may further help the researchers in the discovery of novel molecules against MNK-2 and PIM-2.

Investigation of the structural dynamics of a knotted protein and its unknotted analog using molecular dynamics

The role of knots in proteins remains elusive. Some studies suggest an impact on stability; the difficulty in comparing systems to assess this effect, however, has been a significant challenge. In this study, we produced and analyzed molecular dynamic trajectories considering three different temperatures of two variants of ornithine transcarbamylase (OTC), only one of which has a 3₁ knot, in order to evaluate the relative stability of the two molecules. RMSD showed equilibrated structures for the produced trajectories, and RMSF showed subtle differences in flexibility. In the knot moiety, the knotted protein did not show a great deal of fluctuation at any temperature. For the unknotted protein, the residue GLY243 showed a high fluctuation in the corresponding moiety. The fraction of native contacts (Q) showed a similar profile at all temperatures, with the greatest decrease by 436 K. The investigation of conformational behavior with principal component analysis (PCA) and dynamic cross-correlation map (DCCM) showed that knotted protein is less likely to undergo changes in its conformation under the conditions employed compared to unknotted. PCA data showed that the unknotted protein had greater dispersion in its conformations, which suggests that it has a greater capacity for conformation transitions in response to thermal changes. DCCM graphs comparing the 310 K and 436 K temperatures showed that the knotted protein had less change in its correlation and anti-correlation movements, indicating stability compared to the unknotted.

Multiple-level copy number variations in cell-free DNA for prognostic prediction of HCC with radical treatments

Copy number variations (CNVs) in cell-free DNA (cfDNA) are emerging as noninvasive biomarkers for various cancers. However, multiple-level analysis of cfDNA CNVs for hepatocellular carcinoma (HCC) patients with radical treatments remains uninvestigated. Here, CNVs at genome-wide, chromosomal-arm, and bin levels were analyzed in cfDNA from 117 HCC patients receiving radical treatments. Then, the relationship between cfDNA CNVs and clinical outcomes was explored. Our results showed that a concordant profile of CNVs was observed between cfDNA and tumor tissue DNA. Three genome-wide CNV indicators including tumor fraction (TFx), prediction score (P-score), and stability score (S-score) were calculated and demonstrated to exhibit significant correlation with poorer overall survival (OS) and recurrence-free survival (RFS). Furthermore, the high-frequency cfDNA CNVs at chromosomal-arm level including the loss of 4q, 17p, and 19p and the gain of 8q and 1q clearly predicted HCC prognosis. Finally, a bin-level risk score was constructed to improve the ability of CNVs in predicting prognosis. Altogether, our study indicates that the multiple-level cfDNA CNVs are significantly associated with OS and RFS in HCC patients with radical treatments, suggesting that cfDNA CNVs detected by low-coverage whole-genome sequencing (WGS) may be used as potential prognostic biomarkers of HCC patients.

Evaluation of flavonoids as 2019-nCoV cell entry inhibitor through molecular docking and pharmacological analysis

The outbreak of Coronavirus Disease 2019 (COVID-19) has been declared as a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO), which is being rapidly spread by the extremely spreadable and pathogenic 2019 novel coronavirus (2019-nCoV), also known as SARS-CoV-2. Pandemic incidence of COVID-19 has created a severe threat to global public health, necessitating the development of effective drugs or inhibitors or therapeutics agents against SARS-CoV-2. Spike protein (S) of the SARS-CoV-2 plays a crucial role in entering viruses into the host cell by binding to angiotensin-converting enzyme 2 (ACE-2), and this specific interaction represents a promising drug target for the identification of potential drugs. This study aimed at the receptor-binding domain of S protein (RBD of nCoV-SP) and the ACE-2 receptor as a promising target for developing drugs against SARS-CoV-2. Over 100 different flavonoids with antioxidant, anti-inflammatory, and antiviral properties from different literatures were taken as a ligand or inhibitor for molecular docking against target protein RBD of nCoV-SP and ACE-2 using PyRX and iGEMDOCK. Top flavonoids based on docking scores were selected for the pharmacokinetic study. Selected flavonoids (hesperidin, naringin, ECGC, and quercetin) showed excellent pharmacokinetics with proper absorption, solubility, permeability, distribution, metabolism, minimal toxicity, and excellent bioavailability. Molecular dynamics simulation studies up to 100 ns exhibited strong binding affinity of selected flavonoids to RBD of nCoV-SP and ACE-2, and the protein-ligand complexes were structurally stable. These identified lead flavonoids may act as potential compounds for developing effective drugs against SARS-CoV-2 by potentially inhibiting virus entry into the host cell.

Identification of potential inhibitors against SARS-CoV-2 by targeting proteins responsible for envelope formation and virion assembly using docking based virtual screening, and pharmacokinetics approaches

WHO has declared the outbreak of COVID-19 as a public health emergency of international concern. The ever-growing new cases have called for an urgent emergency for specific anti-COVID-19 drugs. Three structural proteins (Membrane, Envelope and Nucleocapsid protein) play an essential role in the assembly and formation of the infectious virion particles. Thus, the present study was designed to identify potential drug candidates from the unique collection of 548 anti-viral compounds (natural and synthetic anti-viral), which target SARS-CoV-2 structural proteins. High-end molecular docking analysis was performed to characterize the binding affinity of the selected drugs-the ligand, with the SARS-CoV-2 structural proteins, while high-level Simulation studies analyzed the stability of drug-protein interactions. The present study identified rutin, a bioflavonoid and the antibiotic, doxycycline, as the most potent inhibitor of SARS-CoV-2 envelope protein. Caffeic acid and ferulic acid were found to inhibit SARS-CoV-2 membrane protein while the anti-viral agent's simeprevir and grazoprevir showed a high binding affinity for nucleocapsid protein. All these compounds not only showed excellent pharmacokinetic properties, absorption, metabolism, minimal toxicity and bioavailability but were also remain stabilized at the active site of proteins during the MD simulation. Thus, the identified lead compounds may act as potential molecules for the development of effective drugs against SARS-CoV-2 by inhibiting the envelope formation, virion assembly and viral pathogenesis.

Evaluation of potential drugs against leishmaniasis targeting catalytic subunit of Leishmania donovani nuclear DNA primase using ligand based virtual screening, docking and molecular dynamics approaches

Leishmania donovani, causes leishmaniasis, a global health trouble with around 89 different countries and its population under its risk. Replication initiation events have been instrumental in regulating the DNA duplication and as the small subunit of L. donovani nuclear DNA primase (Ld-PriS) inherits the catalytic site, it plays a vital role in DNA replication. In this study we have aimed Ld-PriS for the first time as a prospective target for the application of drug against Leishmania parasite. 3-D structures of Ld-PriS were built and ligand-based virtual screening was performed using hybrid similarity recognition techniques. Ligands from the ZINC database were used for the screening purposes based on known DNA primase inhibitor Sphingosine as a query. Top 150 ligands were taken into consideration for molecular docking against the query protein (Ld-PriS) using PyRx and iGEMDOCK softwares. Top five compounds with the best docking score were selected for pharmacokinetic investigation and molecular dynamic simulation. These top five screened inhibitors showed very poor binding affinity toward the catalytic subunit of human primase indicating their safety toward the host normal replication mechanism. The top five compounds showed good pharmacokinetic profiles and ADMET predictions revealed good absorption, solubility, permeability, uniform distribution, proper metabolism, minimal toxicity and good bioavailability. Simulation studies upto 50 ns revealed the three leads ZINC000009219046, ZINC000025998119 and ZINC000004677901 bind with Ld-PriS throughout the simulation and there were no huge variations in their backbone suggesting that these three may play as potential lead compounds for developing new drug against leishmaniasis.Communicated by Ramaswamy H. Sarma.

Identification of the potential dual inhibitor of protein tyrosine phosphatase sigma and leukocyte common antigen-related phosphatase by virtual screen, molecular dynamic simulations and post-analysis

Owing to their inhibitory role in regulating oligodendrocyte differentiation and apoptosis, protein tyrosine phosphatase sigma (PTPσ) and leukocyte common antigen-related phosphatase (LAR) play a crucial potential role in treating spinal cord injury (SCI) disease. In this research, the computer aided drug design (CADD) methods were applied to discover the potential dual-target drug involving virtual screen, molecular docking and molecular dynamic simulation. Initially, the top 20 compounds with higher docking score than the positive controls (ZINC13749892, ZINC14516161) were virtually screened out from NCI and ZINC databases, and then were submitted in ADMET to predict their drug properties. Among these potential compounds, ZINC72417086 showed a higher docking score and satisfied Lipinski's rule of five. In addition, the post-analysis demonstrated that when ZINC72417086 bound to PTPσ and LAR, it could stable proteins conformations and destroy the residues interactions between P-loop and other loop regions in active pocket. Meanwhile, residue ARG1595 and ARG1528 could play a crucial role in in the inhibition of PTPσ and LAR, respectively. This research offered a novel approach for rapid discovery of dual-target leads compounds to treat SCI.Communicated by Ramaswamy H. Sarma.

Identification of potential leukocyte antigen-related protein (PTP-LAR) inhibitors through 3D QSAR pharmacophore-based virtual screening and molecular dynamics simulation

Owing to its negative regulatory role in insulin signaling, protein tyrosine phosphatase of leukocyte antigen-related protein (PTP-LAR) was widely thought as a potential drug target for diabetes. Now, it was urgent to search for potential LAR inhibitors targeting diabetes. Initially, the pharmacophore models of LAR inhibitors were established with the application of the HypoGen module. The cost analysis, test set validation, as well as Fischer's test was used to verify the efficiency of pharmacophore model. Then, the best pharmacophore model (Hypo-1-LAR) was applied for the virtual screening of the ZINC database. And 30 compounds met the Lipinski's rule of five. Among them, 10 compounds with better binding affinity than the known LAR inhibitor (BDBM50296375) were discovered by docking studies. Finally, molecular dynamics simulations and post-analysis experiments (RMSD, RMSF, PCA, DCCM and RIN) were conducted to explore the effect of ligands (ZINC97018474 and Compound 1) on LAR and preliminary understand why ZINC97018474 had better inhibitory activity than Compound 1 (BDBM50296375). Communicated by Ramaswamy H. Sarma.