Discovery of Potent ALK Inhibitors Using Pharmacophore-Informatics Strategy

James, Nicola; Ramanathan, K.

doi:10.1007/s12013-017-0800-y

Cited by 19 publications

(13 citation statements)

References 47 publications

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“…PASS (Filimonov et al, 2014 ) is a computer program for analysis of structure-activity relationships (SAR) that allows users to perform ligand-based virtual screening for ligands of multiple targets and/or compounds with desired biological activities (Abdou et al, 2017 ; James and Ramanathan, 2017 ; Stasevych et al, 2017 ; Yildirim et al, 2017 ). Structures of chemical compounds are represented in PASS as a set of 2D atom-centric substructural descriptors called MNA (Multilevel Neighborhoods of Atoms).…”

Section: Methodsmentioning

confidence: 99%

How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors

et al. 2018

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Discovery of new pharmaceutical substances is currently boosted by the possibility of utilization of the Synthetically Accessible Virtual Inventory (SAVI) library, which includes about 283 million molecules, each annotated with a proposed synthetic one-step route from commercially available starting materials. The SAVI database is well-suited for ligand-based methods of virtual screening to select molecules for experimental testing. In this study, we compare the performance of three approaches for the analysis of structure-activity relationships that differ in their criteria for selecting of “active” and “inactive” compounds included in the training sets. PASS (Prediction of Activity Spectra for Substances), which is based on a modified Naïve Bayes algorithm, was applied since it had been shown to be robust and to provide good predictions of many biological activities based on just the structural formula of a compound even if the information in the training set is incomplete. We used different subsets of kinase inhibitors for this case study because many data are currently available on this important class of drug-like molecules. Based on the subsets of kinase inhibitors extracted from the ChEMBL 20 database we performed the PASS training, and then applied the model to ChEMBL 23 compounds not yet present in ChEMBL 20 to identify novel kinase inhibitors. As one may expect, the best prediction accuracy was obtained if only the experimentally confirmed active and inactive compounds for distinct kinases in the training procedure were used. However, for some kinases, reasonable results were obtained even if we used merged training sets, in which we designated as inactives the compounds not tested against the particular kinase. Thus, depending on the availability of data for a particular biological activity, one may choose the first or the second approach for creating ligand-based computational tools to achieve the best possible results in virtual screening.

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Section: Methodsmentioning

confidence: 99%

How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors

et al. 2018

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“…The use of such hierarchical filters and associated parameters was highlighted in our earlier articles. 19,34 Initially, HTVS was carried out and 500 (50%) of the high scoring compounds were selected for the SP docking. Finally, a total of 250 (50%) molecules from SP docking were subjected to XP docking.…”

Section: Virtual Screening Using the Docking Algorithm And Prime Mmgbsa Analysismentioning

confidence: 99%

Discovery of potent Covid‐19 main protease inhibitors using integrated drug‐repurposing strategy

Rohini

James

et al. 2021

Biotech and App Biochem

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The emergence and rapid spreading of novel SARS-CoV-2 across the globe represent an imminent threat to public health. Novel antiviral therapies are urgently needed to overcome this pandemic. Given the significant role of the main protease of Covid-19 for virus replication, we performed a drug-repurposing study using the recently deposited main protease structure, 6LU7. For instance, pharmacophore-and e-pharmacophore-based hypotheses such as AARRH and AARR, respectively, were developed using available small molecule inhibitors and utilized in the screening of the DrugBank repository. Further, a hierarchical docking protocol was implemented with the support of the Glide algorithm. The resultant compounds were then examined for their binding free energy against the main protease of Covid-19 by means of the Prime-MM/GBSA algorithm. Most importantly, the machine learning-based AutoQSAR algorithm was used to predict the antiviral activities of resultant compounds. The hit molecules were also examined for their drug-likeness and toxicity parameters through the QikProp algorithm. Finally, the hit compounds activity against the main protease was validated using molecular dynamics simulation studies. Overall, the present analysis yielded two potential inhibitors (DB02986 and DB08573) that are predicted to bind with the main protease of Covid-19 better than currently used drug molecules such as N3 (cocrystallized native ligand), lopinavir, and ritonavir.

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“…In this process the hits were ranked using a three step hierarchical process, viz HTVS, SP and XP. The use of such hierarchical filters and associated parameters were highlighted in our earlier articles [18,29,30]. Initially, HTVS was carried out and 500 (50%) of the high scoring compounds were selected for the SP docking.…”

Section: Virtual Screening Using Docking Algorithm and Prime Mmgbsa Amentioning

confidence: 99%

Discovery of Potent Covid-19 Main Protease Inhibitors Using Machine Learning Based Virtual Screening Strategy

Kumar¹,

Rohini

James³

et al. 2020

Preprint

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<p>The emergence and rapid spreading of novel SARS-CoV-2 across the globe represent an imminent threat to public health. Novel antiviral therapies are urgently needed to overcome this pandemic. Given the great role of main protease of Covid-19 for virus replication, we performed drug repurposing study using recently deposited main protease structure, 6LU7. For instance, pharmacophore- and e-pharmacophore-based hypotheses such as AARRH and AARR respectively were developed using available small molecule inhibitors and utilized in the screening of DrugBank repository. Further, hierarchical docking protocol was implemented with the support of Glide algorithm. The resultant compounds were then examined for its binding free energy against main protease of Covid-19 by means of Prime-MM/GBSA algorithm. Most importantly, the resultant compounds antiviral activities were further predicted by machine learning-based model generated by AutoQSAR algorithm. Finally, the hit molecules were examined for its drug likeness and its toxicity parameters through QikProp algorithm. Overall, the present analysis yielded four potential inhibitors (DB07800, DB08573, DB03744 and DB02986) that are predicted to bind with main protease of Covid-19 better than currently used drug molecules such as N3 (co-crystallized native ligand), Lopinavir and Ritonavir. </p>

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Discovery of Potent ALK Inhibitors Using Pharmacophore-Informatics Strategy

Cited by 19 publications

References 47 publications

How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors

How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors

Discovery of potent Covid‐19 main protease inhibitors using integrated drug‐repurposing strategy

Discovery of Potent Covid-19 Main Protease Inhibitors Using Machine Learning Based Virtual Screening Strategy

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