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CIP is an open-source high-level function library for (non-linear) curve fitting and data smoothing (with cubic splines), clustering (k-medoids, ART-2a) and machine learning (multiple linear/polynomial regression, feed-forward perceptron-type shallow and deep neural networks and support vector machines). In addition it provides several heuristics for the selection of training and test data or methods to estimate the relevance of data input components. CIP is built on top of the computing platform Mathematica to exploit its algorithmic and graphical capabilities.
CIP is an open-source high-level function library for (non-linear) curve fitting and data smoothing (with cubic splines), clustering (k-medoids, ART-2a) and machine learning (multiple linear/polynomial regression, feed-forward perceptron-type shallow and deep neural networks and support vector machines). In addition it provides several heuristics for the selection of training and test data or methods to estimate the relevance of data input components. CIP is built on top of the computing platform Mathematica to exploit its algorithmic and graphical capabilities.
Creating chemo- & bioinformatics workflows, further developments within the CDK-Taverna Project
(2008)
The translation of images of chemical structures into machine-readable representations of the depicted molecules is known as optical chemical structure recognition (OCSR). There has been a lot of progress over the last three decades in this field, but the development of systems for the recognition of complex hand-drawn structure depictions is still at the beginning. Currently, there is no data for the systematic evaluation of OCSR methods on hand-drawn structures available. Here we present DECIMER - Hand-drawn molecule images, a standardised, openly available benchmark dataset of 5088 hand-drawn depictions of diversely picked chemical structures. Every structure depiction in the dataset is mapped to a machine-readable representation of the underlying molecule. The dataset is openly available and published under the CC-BY 4.0 licence which applies very few limitations. We hope that it will contribute to the further development of the field.
The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical IMagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai.
The translation of images of chemical structures into machine-readable representations of the depicted molecules is known as optical chemical structure recognition (OCSR). There has been a lot of progress over the last three decades in this field, but the development of systems for the recognition of complex hand-drawn structure depictions is still at the beginning. Currently, there is no data for the systematic evaluation of OCSR methods on hand-drawn structures available. Here we present DECIMER — Hand-drawn molecule images, a standardised, openly available benchmark dataset of 5088 hand-drawn depictions of diversely picked chemical structures. Every structure depiction in the dataset is mapped to a machine-readable representation of the underlying molecule. The dataset is openly available and published under the CC-BY 4.0 licence which applies very few limitations. We hope that it will contribute to the further development of the field.
Description and Analysis of Glycosidic Residues in the Largest Open Natural Products Database
(2021)
The algorithm for automated functional groups detection and extraction of organic molecules developed by Peter Ertl is implemented on the basis of the Chemistry Development Kit (CDK).
Folder Basic contains the basic ErtlFunctionalGroupsFinder code and test code for integration in Java projects.
Folder CDK contains CDK library jar file cdk-2.2.jar that ErtlFunctionalGroupsFinder works with.
Folder Evaluation contains sample code for evaluation of functional groups with ErtlFunctionalGroupsFinder.
Folder JUnit 4 contains library jar files for unit testing.
Folder Performance contains a jar library for performance tests.
ErtlFunctionalGroupsFinder is described in the scientific literature
Improved Plasma Membrane Models as Test Systems for the Membrane
Disrupting Activity of Kalata B1
(2017)
Jdpd is an open Java simulation kernel for Molecular Fragment Dissipative Particle Dynamics with parallelizable force calculation, efficient caching options and fast property calculations. It is characterized by an interface and factory-pattern driven design for simple code changes and may help to avoid problems of polyglot programming. Detailed input/output communication, parallelization and process control as well as internal logging capabilities for debugging purposes are supported. The new kernel may be utilized in different simulation environments ranging from flexible scripting solutions up to fully integrated “all-in-one” simulation systems.
Jdpd - An open Java Simulation Kernel for Molecular Fragment Dissipative Particle Dynamics (DPD)
Jdpd is an open Java simulation kernel for Molecular Fragment Dissipative Particle Dynamics (DPD) with parallelizable force calculation, efficient caching options and fast property calculations. It is characterized by an interface and factory-pattern driven design for simple code changes and may help to avoid problems of polyglot programming. Detailed input/output communication, parallelization and process control as well as internal logging capabilities for debugging purposes are supported. The kernel may be utilized in different simulation environments ranging from flexible scripting solutions up to fully integrated “all-in-one” simulation systems like MFsim.
Since Jdpd version 1.6.1.0 Jdpd is available in a (basic) double-precision version and a (derived) single-precision version (= JdpdSP) for all numerical calculations, where the single precision version needs about half the memory of the double precision version.
Jdpd uses the Apache Commons Math and Apache Commons RNG libraries and is published as open source under the GNU General Public License version 3. This repository comprises the Java bytecode libraries (including the Apache Commons Math and RNG libraries), the Javadoc HTML documentation and the Netbeans source code packages including Unit tests.
Jdpd has been described in the scientific literature (the final manuscript 2018 - van den Broek - Jdpd - Final Manucsript.pdf is added to the repository) and used for DPD studies (see references below).
See text file JdpdVersionHistory.txt for a version history with more detailed information.