Discovery of Anticancer Hybrid Molecules by Supervised Machine Learning Models and in Vitro Validation in Drug Resistant Chronic Myeloid Leukemia Cells.

The concept of hybrid drugs for targeting multiple aberrant pathways of cancer, by combining the key pharmacophores of clinically approved single-targeted drugs, has emerged as a promising approach for overcoming drug-resistance. Here, we report the design of unique hybrid molecules by combining the two pharmacophores of clinically approved BCR-ABL inhibitor (ponatinib) and HDAC inhibitor (vorinostat) and results of in vitro studies in drug-resistant CML cells. Robust 2D-QSAR and 3D-pharmacophore machine learning supervised models were developed for virtual screening of the hybrid molecules based on their predicted BCR-ABL and HDAC inhibitory activity. The developed 2D-QSAR model showed five information rich molecular descriptors while the 3D-pharmacophore model of BCR-ABL showed five different chemical features (hydrogen bond acceptor, donor, hydrophobic group, positive ion group, and aromatic rings) and the HDAC model showed four different chemical features (hydrogen bond acceptor, donor, positive ion group, and aromatic rings) for potent BCR-ABL and HDAC inhibition. Virtual screening of the 16 designed hybrid molecules identified FP7 and FP10 with better potential of inhibitory activity. FP7 was the most effective molecule with predicted IC50 using the BCR-ABL based 2D-QSAR model of 0.005 µM and that of the HDAC model of 0.153 µM, and that using the BCR-ABL based 3D-pharmacophore model was 0.02 µM and that with HDAC model was 0.014 µM. In vitro study (dose-response relationship) of FP7 in wild type and imatinib-resistant CML cell lines harboring Thr315Ile or Tyr253His mutations showed growth inhibitory IC50 values of 0.000 16, 0.0039, and 0.01 µM, respectively. This molecule also showed better biocompatibility when tested in whole blood and in PBMCs as compared to ponatinib or vorinostat.

Identification of New KRAS G12D Inhibitors through Computer-Aided Drug Discovery Methods.

Owing to several mutations, the oncogene Kirsten rat sarcoma 2 viral oncogene homolog (KRAS) is activated in the majority of cancers, and targeting it has been pharmacologically challenging. In this study, using an in silico approach comprised of pharmacophore modeling, molecular docking, and molecular dynamics simulations, potential KRAS G12D inhibitors were investigated. A ligand-based common feature pharmacophore model was generated to identify the framework necessary for effective KRAS inhibition. The chemical features in the selected pharmacophore model comprised two hydrogen bond donors, one hydrogen bond acceptor, two aromatic rings and one hydrophobic feature. This model was used for screening in excess of 214,000 compounds from InterBioScreen (IBS) and ZINC databases. Eighteen compounds from the IBS and ten from the ZINC database mapped onto the pharmacophore model and were subjected to molecular docking. Molecular docking results highlighted a higher affinity of four hit compounds towards KRAS G12D in comparison to the reference inhibitor, BI-2852. Sequential molecular dynamics (MD) simulation studies revealed all four hit compounds them possess higher KRAS G12D binding free energy and demonstrate stable polar interaction with key residues. Further, Principal Component Analysis (PCA) analysis of the hit compounds in complex with KRAS G12D also indicated stability. Overall, the research undertaken provides strong support for further in vitro testing of these newly identified KRAS G12D inhibitors, particularly Hit1 and Hit2.

Computational Simulations Highlight the IL2Rα Binding Potential of Polyphenol Stilbenes from Fenugreek.

Widely used in global households, fenugreek is well known for its culinary and medicinal uses. The various reported medicinal properties of fenugreek are by virtue of the different natural phytochemicals present in it. Regarded as a promising target, interleukin 2 receptor subunit alpha (IL2Rα) has been shown to influence immune responses. In the present research, using in silico techniques, we have demonstrated the potential IL2Rα binding properties of three polyphenol stilbenes (desoxyrhaponticin, rhaponticin, rhapontigenin) from fenugreek. As the first step, molecular docking was performed to assess the binding potential of the fenugreek phytochemicals with IL2Rα. All three phytochemicals demonstrated interactions with active site residues. To confirm the reliability of our molecular docking results, 100 ns molecular dynamics simulations studies were undertaken. As discerned by the RMSD and RMSF analyses, IL2Rα in complex with the desoxyrhaponticin, rhaponticin, and rhapontigenin indicated stability. The RMSD analysis of the phytochemicals alone also demonstrated no significant structural changes. Based on the stable molecular interactions and comparatively slightly better MM/PBSA binding free energy, rhaponticin seems promising. Additionally, ADMET analysis performed for the stilbenes indicated that all of them obey the ADMET rules. Our computational study thus supports further in vitro IL2Rα binding studies on these stilbenes, especially rhaponticin.

Investigation of Marine-Derived Natural Products as Raf Kinase Inhibitory Protein (RKIP)-Binding Ligands.

Raf kinase inhibitory protein (RKIP) is an essential regulator of the Ras/Raf-1/MEK/ERK signaling cascade and functions by directly interacting with the Raf-1 kinase. The abnormal expression of RKIP is linked with numerous diseases including cancers, Alzheimer's and diabetic nephropathy. Interestingly, RKIP also plays an indispensable role as a tumor suppressor, thus making it an attractive therapeutic target. To date, only a few small molecules have been reported to modulate the activity of RKIP, and there is a need to explore additional scaffolds. In order to achieve this objective, a pharmacophore model was generated that explores the features of locostatin, the most potent RKIP modulator. Correspondingly, the developed model was subjected to screening, and the mapped compounds from Marine Natural Products (MNP) library were retrieved. The mapped MNPs after ensuing drug-likeness filtration were escalated for molecular docking, where locostatin was regarded as a reference. The MNPs exhibiting higher docking scores than locostatin were considered for molecular dynamics simulations, and their binding affinity towards RKIP was computed via MM/PBSA. A total of five molecules revealed significantly better binding free energy scores than compared to locostatin and, therefore, were reckoned as hits. The hits from the present in silico investigation could act as potent RKIP modulators and disrupt interactions of RKIP with its binding proteins. Furthermore, the identification of potent modulators from marine natural habitat can act as a future drug-discovery source.

Computational Investigation Identified Potential Chemical Scaffolds for Heparanase as Anticancer Therapeutics.

Heparanase (Hpse) is an endo-ß-D-glucuronidase capable of cleaving heparan sulfate side chains. Its upregulated expression is implicated in tumor growth, metastasis and angiogenesis, thus making it an attractive target in cancer therapeutics. Currently, a few small molecule inhibitors have been reported to inhibit Hpse, with promising oral administration and pharmacokinetic (PK) properties. In the present study, a ligand-based pharmacophore model was generated from a dataset of well-known active small molecule Hpse inhibitors which were observed to display favorable PK properties. The compounds from the InterBioScreen database of natural (69,034) and synthetic (195,469) molecules were first filtered for their drug-likeness and the pharmacophore model was used to screen the drug-like database. The compounds acquired from screening were subjected to molecular docking with Heparanase, where two molecules used in pharmacophore generation were used as reference. From the docking analysis, 33 compounds displayed higher docking scores than the reference and favorable interactions with the catalytic residues. Complex interactions were further evaluated by molecular dynamics simulations to assess their stability over a period of 50 ns. Furthermore, the binding free energies of the 33 compounds revealed 2 natural and 2 synthetic compounds, with better binding affinities than reference molecules, and were, therefore, deemed as hits. The hit compounds presented from this in silico investigation could act as potent Heparanase inhibitors and further serve as lead scaffolds to develop compounds targeting Heparanase upregulation in cancer.

Identification of Flavonoids as Putative ROS-1 Kinase Inhibitors Using Pharmacophore Modeling for NSCLC Therapeutics.

Non-small cell lung cancer (NSCLC) is a lethal non-immunogenic malignancy and proto-oncogene ROS-1 tyrosine kinase is one of its clinically relevant oncogenic markers. The ROS-1 inhibitor, crizotinib, demonstrated resistance due to the Gly2032Arg mutation. To curtail this resistance, researchers developed lorlatinib against the mutated kinase. In the present study, a receptor-ligand pharmacophore model exploiting the key features of lorlatinib binding with ROS-1 was exploited to identify inhibitors against the wild-type (WT) and the mutant (MT) kinase domain. The developed model was utilized to virtually screen the TimTec flavonoids database and the retrieved drug-like hits were subjected for docking with the WT and MT ROS-1 kinase. A total of 10 flavonoids displayed higher docking scores than lorlatinib. Subsequent molecular dynamics simulations of the acquired flavonoids with WT and MT ROS-1 revealed no steric clashes with the Arg2032 (MT ROS-1). The binding free energy calculations computed via molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) demonstrated one flavonoid (Hit) with better energy than lorlatinib in binding with WT and MT ROS-1. The Hit compound was observed to bind in the ROS-1 selectivity pocket comprised of residues from the ß-3 sheet and DFG-motif. The identified Hit from this investigation could act as a potent WT and MT ROS-1 inhibitor.

In Silico Study Identified Methotrexate Analog as Potential Inhibitor of Drug Resistant Human Dihydrofolate Reductase for Cancer Therapeutics.

Drug resistance is a core issue in cancer chemotherapy. A known folate antagonist, methotrexate (MTX) inhibits human dihydrofolate reductase (hDHFR), the enzyme responsible for the catalysis of 7,8-dihydrofolate reduction to 5,6,7,8-tetrahydrofolate, in biosynthesis and cell proliferation. Structural change in the DHFR enzyme is a significant cause of resistance and the subsequent loss of MTX. In the current study, wild type hDHFR and double mutant (engineered variant) F31R/Q35E (PDB ID: 3EIG) were subject to computational study. Structure-based pharmacophore modeling was carried out for wild type (WT) and mutant (MT) (variant F31R/Q35E) hDHFR structures by generating ten models for each. Two pharmacophore models, WT-pharma and MT-pharma, were selected for further computations, and showed excellent ROC curve quality. Additionally, the selected pharmacophore models were validated by the Guner-Henry decoy test method, which yielded high goodness of fit for WT-hDHFR and MT-hDHFR. Using a SMILES string of MTX in ZINC15 with the selections of 'clean', in vitro and in vivo options, 32 MTX-analogs were obtained. Eight analogs were filtered out due to their drug-like properties by applying absorption, distribution, metabolism, excretion, and toxicity (ADMET) assessment tests and Lipinski's Rule of five. WT-pharma and MT-pharma were further employed as a 3D query in virtual screening with drug-like MTX analogs. Subsequently, seven screening hits along with a reference compound (MTX) were subjected to molecular docking in the active site of WT- and MT-hDHFR. Through a clustering analysis and examination of protein-ligand interactions, one compound was found with a ChemPLP fitness score greater than that of MTX (reference compound). Finally, a simulation of molecular dynamics (MD) identified an MTX analog which exhibited strong affinity for WT- and MT-hDHFR, with stable RMSD, hydrogen bonds (H-bonds) in the binding site and the lowest MM/PBSA binding free energy. In conclusion, we report on an MTX analog which is capable of inhibiting hDHFR in wild type form, as well as in cases where the enzyme acquires resistance to drugs during chemotherapy treatment.

Computational insights into allosteric inhibition of focal adhesion kinase: A combined pharmacophore modeling and molecular dynamics approach.

Focal adhesion kinase (FAK) is a non-receptor tyrosine kinase that modulates integrin and growth factor signaling pathways and is implicated in cancer cell migration, proliferation, and survival. Over the past decade various, FAK kinase, FERM, and FAT domain inhibitors have been reported and a few kinase domain inhibitors are under clinical consideration. However, few of them were identified as multikinase inhibitors. In kinase drug design selectivity is always a point of concern, to improve selectivity allosteric inhibitor development is the best choice. The current research utilized a pharmacophore modeling (PM) approach to identify novel allosteric inhibitors of FAK. The all-available allosteric inhibitor bound 3D structures with PDB ids 4EBV, 4EBW, and 4I4F were utilized for the pharmacophore modeling. The validated PM models were utilized to map a database of 770,550 compounds prepared from ZINC, EXIMED, SPECS, ASINEX, and InterBioScreen, aiming to identify potential allosteric inhibitors. The obtained compounds from screening step were forwarded to molecular docking (MD) for the prediction of binding orientation inside the allosteric site and the results were evaluated with the known FAK allosteric inhibitor (REF). Finally, 14 FAK-inhibitor complexes were selected from the docking study and were studied under molecular dynamics simulations (MDS) for 500 ns. The complexes were ranked according to binding free energy (BFE) and those demonstrated higher affinity for allosteric site of FAK than REF inhibitors were selected. The selected complexes were further analyzed for intermolecular interactions and finally, three potential allosteric inhibitor candidates for the inhibition of FAK protein were identified. We believe that identified scaffolds may help in drug development against FAK as an anticancer agent.

Investigation of Macrocyclic mTOR Modulators of Rapamycin Binding Site via Pharmacoinformatics Approaches.

The PI3K/Akt/mTOR is an essential intracellular signaling pathway in which the serine/threonine mTOR kinase portrays a major role in cell growth, proliferation and survival. The mTOR kinase is frequently dysregulated in a broad spectrum of cancers, thus making it a potential target. Rapamycin and its analogs (rapalogs) allosterically inhibit mTOR, thereby dodging the deleterious effects prompted by ATP-competitive mTOR inhibitors. However, the available mTOR allosteric site inhibitors exhibit low oral bioavailability and suboptimal solubility. Bearing in mind this narrow therapeutic window of the current allosteric mTOR inhibitors, an in silico study was designed in search of new macrocyclic inhibitors. The macrocycles from the ChemBridge database (12,677 molecules) were filtered for their drug-likeness properties and the procured compounds were subjected for molecular docking within the binding cleft between FKBP25 and FRB domains of mTOR. The docking analysis resulted with 15 macrocycles displaying higher scores than the selective mTOR allosteric site inhibitor, DL001. The docked complexes were refined by subsequent molecular dynamics simulations for a period of 100 ns. Successive binding free energy computation revealed a total of 7 macrocyclic compounds (HITS) demonstrating better binding affinity than DL001, towards mTOR. The consequent assessment of pharmacokinetic properties resulted in HITS with similar or better properties than the selective inhibitor, DL001. The HITS from this investigation could act as effective mTOR allosteric site inhibitors and serve as macrocyclic scaffolds for developing compounds targeting the dysregulated mTOR.

Identification of Activated Cdc42-Associated Kinase Inhibitors as Potential Anticancer Agents Using Pharmacoinformatic Approaches.

BACKGROUND: Activated Cdc42-associated kinase (ACK1) is essential for numerous cellular functions, such as growth, proliferation, and migration. ACK1 signaling occurs through multiple receptor tyrosine kinases; therefore, its inhibition can provide effective antiproliferative effects against multiple human cancers. A number of ACK1-specific inhibitors were designed and discovered in the previous decade, but none have reached the clinic. Potent and selective ACK1 inhibitors are urgently needed. METHODS: In the present investigation, the pharmacophore model (PM) was rationally built utilizing two distinct inhibitors coupled with ACK1 crystal structures. The generated PM was utilized to screen the drug-like database generated from the four chemical databases. The binding mode of pharmacophore-mapped compounds was predicted using a molecular docking (MD) study. The selected hit-protein complexes from MD were studied under all-atom molecular dynamics simulations (MDS) for 500 ns. The obtained trajectories were ranked using binding free energy calculations (ΔG kJ/mol) and Gibb's free energy landscape. RESULTS: Our results indicate that the three hit compounds displayed higher binding affinity toward ACK1 when compared with the known multi-kinase inhibitor dasatinib. The inter-molecular interactions of Hit1 and Hit3 reveal that compounds form desirable hydrogen bond interactions with gatekeeper T205, hinge region A208, and DFG motif D270. As a result, we anticipate that the proposed scaffolds might help in the design of promising selective ACK1 inhibitors.

3D-QSAR-Based Pharmacophore Modeling, Virtual Screening, and Molecular Dynamics Simulations for the Identification of Spleen Tyrosine Kinase Inhibitors.

Spleen tyrosine kinase (SYK) is an essential mediator of immune cell signaling and has been anticipated as a therapeutic target for autoimmune diseases, notably rheumatoid arthritis, allergic rhinitis, asthma, and cancers. Significant attempts have been undertaken in recent years to develop SYK inhibitors; however, limited success has been achieved due to poor pharmacokinetics and adverse effects of inhibitors. The primary goal of this research was to identify potential inhibitors having high affinity, selectivity based on key molecular interactions, and good drug-like properties than the available inhibitor, fostamatinib. In this study, a 3D-QSAR model was built for SYK based on known inhibitor IC50 values. The best pharmacophore model was then used as a 3D query to screen a drug-like database to retrieve hits with novel chemical scaffolds. The obtained compounds were subjected to binding affinity prediction using the molecular docking approach, and the results were subsequently validated using molecular dynamics (MD) simulations. The simulated compounds were ranked according to binding free energy (ΔG), and the binding affinity was compared with fostamatinib. The binding mode analysis of selected compounds revealed that the hit compounds form hydrogen bond interactions with hinge region residue Ala451, glycine-rich loop residue Lys375, Ser379, and DFG motif Asp512. Identified hits were also observed to form a desirable interaction with Pro455 and Asn457, the rare feature observed in SYK inhibitors. Therefore, we argue that identified hit compounds ZINC98363745, ZINC98365358, ZINC98364133, and ZINC08789982 may help in drug design against SYK.

Investigating natural compounds against oncogenic RET tyrosine kinase using pharmacoinformatic approaches for cancer therapeutics.

Rearranged during transfection (RET) tyrosine kinase is a transmembrane receptor tyrosine kinase regulating vital aspects of cellular proliferation, differentiation, and survival. An outstanding challenge in designing protein kinase inhibitors is due to the development of drug resistance. The "gain of function" mutations in the RET gate-keeper residue, Val804, confers resistance to the majority of known RET inhibitors, including vandetanib. To curtail this resistance, researchers developed selpercatinib (LOXO-292) against the RET gate-keeper mutant forms - V804M and V804L. In the present in silico investigation, a receptor-ligand pharmacophore model was generated to identify small molecule inhibitors effective for wild-type (WT) as well as mutant RET kinase variants. The generated model was employed to screen 144 766 natural products (NPs) available in the ZINC database and the retrieved NPs were filtered for their drug-likeness. The resulting 2696 drug-like NPs were subjected to molecular docking with the RET WT kinase domain and a total of 27 molecules displayed better dock scores than the reference inhibitors - vandetanib and selpercatinib. From 27 NPs, an aggregate of 12 compounds demonstrated better binding free energy (BFE) scores than the reference inhibitors, towards RET. Thus, the 12 NPs were also subjected to docking, simulation, and BFE estimation towards the constructed gate-keeper RET mutant structures. The BFE calculations revealed 3 hits with better BFE scores than the reference inhibitors towards WT, V804M, and V804L RET variants. Thus, the scaffolds of hit compounds presented in this study could act as potent RET inhibitors and further provide insights for drug optimization targeting aberrant activation of RET signaling, specifically the mutation of gate-keeper residue - Val804.

Computational Simulations Identified Marine-Derived Natural Bioactive Compounds as Replication Inhibitors of SARS-CoV-2.

The rapid spread of COVID-19, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a worldwide health emergency. Unfortunately, to date, a very small number of remedies have been to be found effective against SARS-CoV-2 infection. Therefore, further research is required to achieve a lasting solution against this deadly disease. Repurposing available drugs and evaluating natural product inhibitors against target proteins of SARS-CoV-2 could be an effective approach to accelerate drug discovery and development. With this strategy in mind, we derived Marine Natural Products (MNP)-based drug-like small molecules and evaluated them against three major target proteins of the SARS-CoV-2 virus replication cycle. A drug-like database from MNP library was generated using Lipinski's rule of five and ADMET descriptors. A total of 2,033 compounds were obtained and were subsequently subjected to molecular docking with 3CLpro, PLpro, and RdRp. The docking analyses revealed that a total of 14 compounds displayed better docking scores than the reference compounds and have significant molecular interactions with the active site residues of SARS-CoV-2 virus targeted proteins. Furthermore, the stability of docking-derived complexes was analyzed using molecular dynamics simulations and binding free energy calculations. The analyses revealed two hit compounds against each targeted protein displaying stable behavior, binding affinity, and molecular interactions. Our investigation identified two hit compounds against each targeted proteins displaying stable behavior, higher binding affinity and key residual molecular interactions, with good in silico pharmacokinetic properties, therefore can be considered for further in vitro studies.

Marine-Derived Natural Products as ATP-Competitive mTOR Kinase Inhibitors for Cancer Therapeutics.

The mammalian target of rapamycin (mTOR) is a serine/threonine kinase portraying a quintessential role in cellular proliferation and survival. Aberrations in the mTOR signaling pathway have been reported in numerous cancers including thyroid, lung, gastric and ovarian cancer, thus making it a therapeutic target. To attain this objective, an in silico investigation was designed, employing a pharmacophore modeling approach. A structure-based pharmacophore (SBP) model exploiting the key features of a selective mTOR inhibitor, Torkinib directed at the ATP-binding pocket was generated. A Marine Natural Products (MNP) library was screened using SBP model as a query. The retrieved compounds after consequent drug-likeness filtration were subjected to molecular docking with mTOR, thus revealing four MNPs with better scores than Torkinib. Successive refinement via molecular dynamics simulations demonstrated that the hits formed crucial interactions with key residues of the pocket. Furthermore, the four identified hits exhibited good binding free energy scores through MM-PBSA calculations and the subsequent in silico toxicity assessments displayed three hits deemed essentially non-carcinogenic and non-mutagenic. The hits presented in this investigation could act as potent ATP-competitive mTOR inhibitors, representing a platform for the future discovery of drugs from marine natural origin.

Exploring the Binding Interaction of Raf Kinase Inhibitory Protein With the N-Terminal of C-Raf Through Molecular Docking and Molecular Dynamics Simulation.

Protein-protein interactions are indispensable physiological processes regulating several biological functions. Despite the availability of structural information on protein-protein complexes, deciphering their complex topology remains an outstanding challenge. Raf kinase inhibitory protein (RKIP) has gained substantial attention as a favorable molecular target for numerous pathologies including cancer and Alzheimer's disease. RKIP interferes with the RAF/MEK/ERK signaling cascade by endogenously binding with C-Raf (Raf-1 kinase) and preventing its activation. In the current investigation, the binding of RKIP with C-Raf was explored by knowledge-based protein-protein docking web-servers including HADDOCK and ZDOCK and a consensus binding mode of C-Raf/RKIP structural complex was obtained. Molecular dynamics (MD) simulations were further performed in an explicit solvent to sample the conformations for when RKIP binds to C-Raf. Some of the conserved interface residues were mutated to alanine, phenylalanine and leucine and the impact of mutations was estimated by additional MD simulations and MM/PBSA analysis for the wild-type (WT) and constructed mutant complexes. Substantial decrease in binding free energy was observed for the mutant complexes as compared to the binding free energy of WT C-Raf/RKIP structural complex. Furthermore, a considerable increase in average backbone root mean square deviation and fluctuation was perceived for the mutant complexes. Moreover, per-residue energy contribution analysis of the equilibrated simulation trajectory by HawkDock and ANCHOR web-servers was conducted to characterize the key residues for the complex formation. One residue each from C-Raf (Arg398) and RKIP (Lys80) were identified as the druggable "hot spots" constituting the core of the binding interface and corroborated by additional long-time scale (300 ns) MD simulation of Arg398Ala mutant complex. A notable conformational change in Arg398Ala mutant occurred near the mutation site as compared to the equilibrated C-Raf/RKIP native state conformation and an essential hydrogen bonding interaction was lost. The thirteen binding sites assimilated from the overall analysis were mapped onto the complex as surface and divided into active and allosteric binding sites, depending on their location at the interface. The acquired information on the predicted 3D structural complex and the detected sites aid as promising targets in designing novel inhibitors to block the C-Raf/RKIP interaction.

Development of Machine Learning Models for Accurately Predicting and Ranking the Activity of Lead Molecules to Inhibit PRC2 Dependent Cancer.

Disruption of epigenetic processes to eradicate tumor cells is among the most promising interventions for cancer control. EZH2 (Enhancer of zeste homolog 2), a catalytic component of polycomb repressive complex 2 (PRC2), methylates lysine 27 of histone H3 to promote transcriptional silencing and is an important drug target for controlling cancer via epigenetic processes. In the present study, we have developed various predictive models for modeling the inhibitory activity of EZH2. Binary and multiclass models were built using SVM, random forest and XGBoost methods. Rigorous validation approaches including predictiveness curve, Y-randomization and applicability domain (AD) were employed for evaluation of the developed models. Eighteen descriptors selected from Boruta methods have been used for modeling. For binary classification, random forest and XGBoost achieved an accuracy of 0.80 and 0.82, respectively, on external test set. Contrastingly, for multiclass models, random forest and XGBoost achieved an accuracy of 0.73 and 0.75, respectively. 500 Y-randomization runs demonstrate that the models were robust and the correlations were not by chance. Evaluation metrics from predictiveness curve show that the selected eighteen descriptors predict active compounds with total gain (TG) of 0.79 and 0.59 for XGBoost and random forest, respectively. Validated models were further used for virtual screening and molecular docking in search of potential hits. A total of 221 compounds were commonly predicted as active with above the set probability threshold and also under the AD of training set. Molecular docking revealed that three compounds have reasonable binding energy and favorable interactions with critical residues in the active site of EZH2. In conclusion, we highlighted the potential of rigorously validated models for accurately predicting and ranking the activities of lead molecules against cancer epigenetic targets. The models presented in this study represent the platform for development of EZH2 inhibitors.

Insilico drug repurposing using FDA approved drugs against Membrane protein of SARS-CoV-2.

The novel coronavirus (SARS-CoV-2) outbreak has started taking away the millions of lives worldwide. Identification of known and approved drugs against novel coronavirus disease (COVID-19) seems to be an urgent need for the repurposing of the existing drugs. So, here we examined a safe strategy of using approved drugs of SuperDRUG2 database against modeled membrane protein (M-protein) of SARS-CoV-2 which is essential for virus assembly by using molecular docking-based virtual screening. A total of 3639 drugs from SuperDRUG2 database and additionally 14 potential drugs reported against COVID-19 proteins were selected. Molecular docking analyses revealed that nine drugs can bind the active site of M-protein with desirable molecular interactions. We therefore applied molecular dynamics simulations and binding free energy calculation using MM-PBSA to analyze the stability of the compounds. The complexes of M-protein with the selected drugs were simulated for 50 ns and ranked according to their binding free energies. The binding mode of the drugs with M-protein was analyzed and it was observed that Colchicine, Remdesivir, Bafilomycin A1 from COVID-19 suggested drugs and Temozolomide from SuperDRUG2 database displayed desirable molecular interactions and higher binding affinity towards M-protein. Interestingly, Colchicine was found as the top most binder among tested drugs against M-protein. We therefore additionally identified four Colchicine derivatives which can bind efficiently with M-protein and have better pharmacokinetic properties. We recommend that these drugs can be tested further through in vitro studies against SARS-CoV-2 M-protein.

Identification of CDK7 Inhibitors from Natural Sources Using Pharmacoinformatics and Molecular Dynamics Simulations.

The cyclin-dependent kinase 7 (CDK7) plays a crucial role in regulating the cell cycle and RNA polymerase-based transcription. Overexpression of this kinase is linked with various cancers in humans due to its dual involvement in cell development. Furthermore, emerging evidence has revealed that inhibiting CDK7 has anti-cancer effects, driving the development of novel and more cost-effective inhibitors with enhanced selectivity for CDK7 over other CDKs. In the present investigation, a pharmacophore-based approach was utilized to identify potential hit compounds against CDK7. The generated pharmacophore models were validated and used as 3D queries to screen 55,578 natural drug-like compounds. The obtained compounds were then subjected to molecular docking and molecular dynamics simulations to predict their binding mode with CDK7. The molecular dynamics simulation trajectories were subsequently used to calculate binding affinity, revealing four hits-ZINC20392430, SN00112175, SN00004718, and SN00262261-having a better binding affinity towards CDK7 than the reference inhibitors (CT7001 and THZ1). The binding mode analysis displayed hydrogen bond interactions with the hinge region residues Met94 and Glu95, DFG motif residue Asp155, ATP-binding site residues Thr96, Asp97, and Gln141, and quintessential residue outside the kinase domain, Cys312 of CDK7. The in silico selectivity of the hits was further checked by docking with CDK2, the close homolog structure of CDK7. Additionally, the detailed pharmacokinetic properties were predicted, revealing that our hits have better properties than established CDK7 inhibitors CT7001 and THZ1. Hence, we argue that proposed hits may be crucial against CDK7-related malignancies.

Discovery of Lonafarnib-Like Compounds: Pharmacophore Modeling and Molecular Dynamics Studies.

Progeria is a globally noticed rare genetic disorder manifested by premature aging with no effective treatment. Under these circumstances, farnesyltransferase inhibitors (FTIs) are marked as promising drug candidates. Correspondingly, a pharmacophore model was generated exploiting the features of lonafarnib. The selected pharmacophore model was allowed to screen the InterBioScreen natural compound database to retrieve the potential lead candidates. A series of filtering steps were applied to assess the drug-likeness of the compounds. The obtained compounds were advanced to molecular docking employing the CDOCKER module available with Discovery Studio (DS). Subsequently, three compounds (Hits) have displayed a higher dock score and demonstrated key residue interactions with stable molecular dynamics simulation results compared to the reference compound. Taken together, we therefore put forth three identified Hits as FTIs that may further serve as chemical spaces in designing new compounds.

Natural compounds as potential Hsp90 inhibitors for breast cancer-Pharmacophore guided molecular modelling studies.

Breast cancer is one of the major impediments affecting women globally. The ATP-dependant heat shock protein 90 (Hsp90) forms the central component of molecular chaperone machinery that predominantly governs the folding of newly synthesized peptides and their conformational maturation. It regulates the stability and function of numerous client proteins that are frequently upregulated and/or mutated in cancer cells, therefore, making Hsp90 inhibition a promising therapeutic strategy for the development of new efficacious drugs to treat breast cancer. In the present in silico investigation, a structure-based pharmacophore model was generated with hydrogen bond donor, hydrogen bond acceptor and hydrophobic features complementary to crucial residues Ala55, Lys58, Asp93, Ile96, Met98 and Thr184 directed at inhibiting the ATP-binding activity of Hsp90. Subsequently, the phytochemical dataset of 3210 natural compounds was screened to retrieve the prospective inhibitors after rigorous validation of the model pharmacophore. The retrieved 135 phytocompounds were further filtered by drug-likeness parameters including Lipinski's rule of five and ADMET properties, then investigated via molecular docking-based scoring. Molecular interactions were assessed using Genetic Optimisation for Ligand Docking program for 95 drug-like natural compounds against Hsp90 along with two clinical drugs as reference compounds - Geldanamycin and Radicicol. Docking studies revealed three phytochemicals are better than the investigated clinical drugs. The reference and hit compounds with dock scores of 48.27 (Geldanamycin), 40.90 (Radicicol), 73.04 (Hit1), 72.92 (Hit2) and 68.12 (Hit3) were further validated for their binding stability through molecular dynamics simulations. We propose that the non-macrocyclic scaffolds of three identified phytochemicals might aid in the development of novel therapeutic candidates against Hsp90-driven cancers.