The Chemistry Development Kit (CDK):  An Open-Source Java Library for Chemo- and Bioinformatics

Steinbeck, Christoph; Han, Yongquan; Kühn, Stefan; Horlacher, Oliver; Luttmann, Edgar; Willighagen, Egon

doi:10.1021/ci025584y

Cited by 877 publications

(489 citation statements)

References 25 publications

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“…We have previously showed that models built with open source tools can produce validation statistics comparable to commercial modeling tools 49 . We recently made “function class fingerprints of maximum diameter 6” (FCFP6) and “extended connectivity (ECFP6) fingerprints,” open source and have described their implementation with the Chemistry Development Kit (CDK) 50 components 41 . In addition we described an open source Bayesian algorithm that can be used with these descriptors 39, 40 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning models identify molecules active against the Ebola virus in vitro

et al. 2016

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The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC 50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

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Section: Discussionmentioning

confidence: 99%

Machine learning models identify molecules active against the Ebola virus in vitro

et al. 2016

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“…Molecular properties such as atomic masses, number of polar (NPOL) and non-hydrogen atoms (NHA), and polar surface area are calculated using the Chemistry Development Toolkit (CDK) [11,12].…”

Section: Leis and Molecular Properties Calculationmentioning

confidence: 99%

“…Data for three affinity parameters (K i , IC 50 , K d ) are extracted and retained within the server connected to the appropriate target and organism. In the case of BindingDB, the SDF (Structure Data File) was converted to SMILES format using RDKit and then inserted into the database with the information provided for targets and organism in the property fields of the same SDF file, along with the calculated molecular properties by CDK [11,12]. For PDBBind the data were transformed directly into SMILES strings from the PDB files and the affinity information in the annotated set was used.…”

Section: Implementation Detailsmentioning

confidence: 99%

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AtlasCBS: a web server to map and explore chemico-biological space

Cortés-Cabrera

Morreale

Gago

et al. 2012

J Comput Aided Mol Des

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New approaches are needed that can help decrease the unsustainable failure in small-molecule drug discovery. Ligand Efficiency Indices (LEI) are making a great impact on early-stage compound selection and prioritization. Given a target-ligand database with chemical structures and associated biological affinities/activities for a target, the AtlasCBS server generates two-dimensional, dynamical, representations of its content in terms of LEI. These variables allow an effective decoupling of the chemical (angular) and biological (radial) components. BindingDB, PDBBind and ChEMBL databases are currently implemented. Proprietary datasets can also be uploaded and compared. The utility of this atlas-like representation in the future of drug design is highlighted with some examples. The web server can be accessed at

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“…[3] The IUPAC International Chemical Identifier (InChI  ) [2] application is run as native code on all the operating systems and is accessed either through a Java Native Interface (JNI) (Windows) or run as a separate process (UNIX/Linux).…”

Section: Softwarementioning

confidence: 99%

The development of an NMR chemical shift prediction application with the accuracy necessary to grade proton NMR spectra for identity

Spanton

Whittern

2009

Magnetic Reson in Chemistry

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We have developed an NMR chemical shift prediction system that enables high throughput automatic grading of NMR spectra. In support of high throughput synthetic efforts for our drug discovery program, a rapid and accurate analysis for identity was needed. The system was designed and implemented to take advantage of the NMR assignments that had been tabulated on internally generated research compounds. The system has been operational for four years and has been used in conjunction with an internally written grading program to successfully analyze several hundred thousand samples based only on their 1D 1 H spectrum. A focused test of the system's accuracy on 1006 molecules demonstrated the ability to estimate the proton chemical shift with an average error of +/−0.16 ppm. This level of chemical shift accuracy allows for reliable structure confirmation by automated analysis using only proton NMR.

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The Chemistry Development Kit (CDK): An Open-Source Java Library for Chemo- and Bioinformatics

Cited by 877 publications

References 25 publications

Machine learning models identify molecules active against the Ebola virus in vitro

Machine learning models identify molecules active against the Ebola virus in vitro

AtlasCBS: a web server to map and explore chemico-biological space

The development of an NMR chemical shift prediction application with the accuracy necessary to grade proton NMR spectra for identity

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