Web services as applications' integration tool: QikProp case study

Laoui, Abdel; Polyakov, Valery

doi:10.1002/jcc.21778

Cited by 31 publications

(13 citation statements)

References 7 publications

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“…QikProp is an ADME program offered by Schrodinger that predicts pharmaceutically relevant and physically significant descriptors for small drug-like molecules [232]. The VolSurf package can be used to calculate ADME properties and generate ADME models [233].…”

Section: Reviewmentioning

confidence: 99%

Computational methods in drug discovery

Leelananda

Lindert

2016

Beilstein J. Org. Chem.

501

309

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The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

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Section: Reviewmentioning

confidence: 99%

Computational methods in drug discovery

Leelananda

Lindert

2016

Beilstein J. Org. Chem.

501

309

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“…The remaining six hundred structures (600) were then evaluated for their pharmacokinetic properties (the absorption, distribution, metabolism) using the QikProp software [ 37 , 38 , 39 ]. Structures submitted for pharmacokinetic study, resulted in two hundred and thirty-three (233) hits that presented satisfactory pharmacokinetic properties, especially electronic affinity, Lipinski’s rule, and the central nervous system (CNS) parameter, when compared with the properties of Rofecoxib.…”

Section: Resultsmentioning

confidence: 99%

Identification of New Rofecoxib-Based Cyclooxygenase-2 Inhibitors: A Bioinformatics Approach

et al. 2020

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The cyclooxygenase-2 receptor is a therapeutic target for planning potential drugs with anti-inflammatory activity. The selective cyclooxygenase-2 (COX-2) inhibitor rofecoxib was selected as a pivot molecule to perform virtual ligand-based screening from six commercial databases. We performed the search for similarly shaped Rapid Overlay of Chemical Structures (ROCS) and electrostatic (EON) compounds. After, we used pharmacokinetic and toxicological parameters to determine the best potential compounds, obtained through the softwares QikProp and Derek, respectively. Then, the compounds proceeded to the molecular anchorage study, which showed promising results of binding affinity with the hCOX-2 receptor: LMQC72 (∆G = −11.0 kcal/mol), LMQC36 (∆G = −10.6 kcal/mol), and LMQC50 (∆G = −10.2 kcal/mol). LMQC72 and LMQC36 showed higher binding affinity compared to rofecoxib (∆G = −10.4 kcal/mol). Finally, molecular dynamics (MD) simulations were used to evaluate the interaction of the compounds with the target hCOX-2 during 150 ns. In all MD simulation trajectories, the ligands remained interacting with the protein until the end of the simulation. The compounds were also complexing with hCOX-2 favorably. The compounds obtained the following affinity energy values: rofecoxib: ΔGbind = −45.31 kcal/mol; LMQC72: ΔGbind = −38.58 kcal/mol; LMQC36: ΔGbind = −36.10 kcal/mol; and LMQC50: ΔGbind = −39.40 kcal/mol. The selected LMQC72, LMQC50, and LMQC36 structures showed satisfactory pharmacokinetic results related to absorption and distribution. The toxicological predictions of these compounds did not display alerts for possible toxic groups and lower risk of cardiotoxicity compared to rofecoxib. Therefore, future in vitro and in vivo studies are needed to confirm the anti-inflammatory potential of the compounds selected here with bioinformatics approaches based on rofecoxib ligand.

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“…Several reviews have focused on previously reported resources, specifically, publicly available databases with ADMET‐related content and software and tools for ADMET‐related property prediction . As the data and models have aggregated, many prediction tools have been integrated into comprehensive commercial software, such as QikProp (http://www.schrodinger.com/QikProp/) and VolSurf+ (http://www.moldiscovery.com/software/vsplas/). In addition, some academic institutions have also developed free comprehensive ADMET software or have provided online services, such as PK/DB, admetSAR, pkCSM and FAF‐Drugs4 .…”

Section: Computational Methods For Cbddmentioning

confidence: 99%

Computational chemical biology and drug design: Facilitating protein structure, function, and modulation studies

Zheng

Zhao

Chen

et al. 2018

Medicinal Research Reviews

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Over the past quarter of a century, there has been rapid development in structural biology, which now can provide solid evidence for understanding the functions of proteins. Concurrently, computational approaches with particular relevance to the chemical biology and drug design (CBDD) field have also incrementally and steadily improved. Today, these methods help elucidate detailed working mechanisms and accelerate the discovery of new chemical modulators of proteins. In recent years, integrating computational simulations and predictions with experimental validation has allowed for more effective explorations of the structure, function and modulation of important therapeutic targets. In this review, we summarize the main advancements in computational methodology development, which are then illustrated by several successful applications in CBDD. Finally, we conclude with a discussion of the current major challenges and future directions in the field.

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Web services as applications' integration tool: QikProp case study

Cited by 31 publications

References 7 publications

Computational methods in drug discovery

Computational methods in drug discovery

Identification of New Rofecoxib-Based Cyclooxygenase-2 Inhibitors: A Bioinformatics Approach

Computational chemical biology and drug design: Facilitating protein structure, function, and modulation studies

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