3D‐e‐Chem: Structural Cheminformatics Workflows for Computer‐Aided Drug Discovery

Kooistra, Albert J.; Vass, Márton; McGuire, Ross; Leurs, Rob; Esch, Iwan J. P. de; Vriend, Gert; Verhoeven, Stefan; Graaf, Chris de

doi:10.1002/cmdc.201700754

Cited by 17 publications

(16 citation statements)

References 93 publications

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“…It was one of the earliest freely available offerings to generate metabolite | 383 TYZACK And KIRCHMAIR structures but remains a highly effective and useable tool. More recently, in addition to being made available as a software package, SyGMa has been released as a KNIME (Berthold et al, 2018) node as part of the 3D-e-Chem project (Kooistra et al, 2018). ToxTree (Patlewicz, Jeliazkova, Safford, Worth, & Aleksiev, 2008) includes a module for metabolite structure prediction which is based on SoMs predicted with SMARTCyp.…”

Section: Structure Predictionmentioning

confidence: 99%

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

2019

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In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, including CYP substrate and inhibitor predictors, site of metabolism predictors (i.e., metabolically labile sites within potential substrates) and metabolite structure predictors. We summarize the different approaches taken by these models, such as rule‐based methods, machine learning, data mining, quantum chemical methods, molecular interaction fields, and docking. We highlight the scope and limitations of each method and discuss future implications for the field of metabolism prediction in drug discovery.

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Section: Structure Predictionmentioning

confidence: 99%

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

2019

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“…The open-source tool Swagger CodeGen, for example, can be used to automatically generate a client SDK based on the KLIFS specifications for more than 50 different frameworks in 40 different programming languages. Moreover, the KLIFS nodes ( 30 , 31 ) for KNIME ( 32 ) also make use of the web services to obtain their data. KNIME ( 32 ) is an easy-to-use open-source data analytics platform that amongst others enables cheminformatics analyses.…”

Section: Klifs: New Look New Logo New Featuresmentioning

confidence: 99%

“…These nodes make it possible for any user, also non-programmers, to create advanced structural bio- and cheminformatics pipelines ( 33 ). Examples of such pipelines include structure-based kinase bioactivity data mapping, structure-based identification of scaffold replacements for kinase inhibitor optimization, and structure-based pharmacophore comparison for ligand repurposing ( 31 ).…”

Section: Klifs: New Look New Logo New Featuresmentioning

confidence: 99%

KLIFS: an overhaul after the first 5 years of supporting kinase research

Kanev

Graaf

Westerman

et al. 2020

Nucleic Acids Research

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136

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Kinases are a prime target of drug development efforts with >60 drug approvals in the past two decades. Due to the research into this protein family, a wealth of data has been accumulated that keeps on growing. KLIFS—Kinase–Ligand Interaction Fingerprints and Structures—is a structural database focusing on how kinase inhibitors interact with their targets. The aim of KLIFS is to support (structure-based) kinase research through the systematic collection, annotation, and processing of kinase structures. Now, 5 years after releasing the initial KLIFS website, the database has undergone a complete overhaul with a new website, new logo, and new functionalities. In this article, we start by looking back at how KLIFS has been used by the research community, followed by a description of the renewed KLIFS, and conclude with showcasing the functionalities of KLIFS. Major changes include the integration of approved drugs and inhibitors in clinical trials, extension of the coverage to atypical kinases, and a RESTful API for programmatic access. KLIFS is available at the new domain https://klifs.net.

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“…For VS, the chemical structures of 50000 compound include antiviral drugs and related compounds that are structurally similar to known antivirals (CAS COVID-19 Antiviral Compound Dataset) were downloaded as 3D sdf files from the Chemical Abstract Service database (https://www.cas.org/ covid-19-antiviral-compounds-dataset) and prepared with an in-house KNIME workflow (using Rdkit and 3D-e-Chem nodes) (Kooistra et al, 2018;Landrum, n.d.), first duplicated molecules, compounds with Pan Interference Assay Structures (PAINS) (Dahlin et al, 2015) have been deleted and salts have been stripped out, then best tautomer for every molecule has been generated and optimized, next a modified Lipinski's rule (Lipinski, 2004;Lipinski et al, 1997) (with 300 Mw 700 g/mol, 5 Number of rotatable bonds 12) has been applied to filter out non drug like molecules, which allowed us to narrow down the database to 5378 drug like compound.…”

Section: Collection and Curation Of The Chemical Librarymentioning

confidence: 99%

Identification of a novel dual-target scaffold for 3CLpro and RdRp proteins of SARS-CoV-2 using 3D-similarity search, molecular docking, molecular dynamics and ADMET evaluation

Aouidate

Ghaleb

Chtita

et al. 2020

Journal of Biomolecular Structure and Dynamics

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The new SARS-CoV-2 coronavirus is the causative agent of the COVID-19 pandemic outbreak that affected whole the world with more than 6 million confirmed cases and over 370,000 deaths. At present, there are no effective treatments or vaccine for this disease, which constitutes a serious global health crisis. As the pandemic still spreading around the globe, it is of interest to use computational methods to identify potential inhibitors for the virus. The crystallographic structures of 3CLpro (PDB: 6LU7) and RdRp (PDB 6ML7) were used in virtual screening of 50000 chemical compounds obtained from the CAS Antiviral COVID19 database using 3D-similarity search and standard molecular docking followed by ranking and selection of compounds based on their binding affinity, computational techniques for the sake of details on the binding interactions, absorption, distribution, metabolism, excretion, and toxicity prediction; we report three 4-(morpholin-4-yl)-1,3,5-triazin-2-amine derivatives; two compounds (2001083-68-5 and 2001083-69-6) with optimal binding features to the active site of the main protease and one compound (833463-19-7) with optimal binding features to the active site of the polymerase for further consideration to fight COVID-19. The structural stability and dynamics of lead compounds at the active site of 3CLpro and RdRp were examined using molecular dynamics (MD) simulation. Essential dynamics demonstrated that the three complexes remain stable during simulation of 20 ns, which may be suitable candidates for further experimental analysis. As the identified leads share the same scaffold, they may serve as promising leads in the development of dual 3CLpro and RdRp inhibitors against SARS-CoV-2.

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3D‐e‐Chem: Structural Cheminformatics Workflows for Computer‐Aided Drug Discovery

Cited by 17 publications

References 93 publications

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

KLIFS: an overhaul after the first 5 years of supporting kinase research

Identification of a novel dual-target scaffold for 3CLpro and RdRp proteins of SARS-CoV-2 using 3D-similarity search, molecular docking, molecular dynamics and ADMET evaluation

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