This looks interesting 3D-e-Chem NLeSC project.
This project will develop technologies to improve the integration of ligand and protein data for structure-based prediction of protein-ligand selectivity and polypharmacology.
The project will use KNIME Analytics Platform to integrate the different technologies and datasets.
One of the most time-consuming parts of any data analysis is curating the input data prior to any model building. This Knime workflow is fully documented and described and as such is an invaluable starting point.
A semi-automated procedure is made available to support scientists in data preparation for modelling purposes. The procedure address:
- Automatic chemical data retrieval (i.e., SMILES) from different, orthogonal web based databases, by using two different identifiers, i.e. chemical name and CAS registration number. Records were scored based on the coherence of information retrieved from different web sources.
- Data curation procedure performed to top scored records. The procedure includes removal of inorganic and organometallic compounds and mixtures, neutralization of salts, removal of duplicates, checking of tautomeric forms.
- Standardization of chemical structures yielding to ready-to-use data for the development of QSARs.
Inte:Ligand have just announced the release of LigandScout 4.3.
The LigandScout software suite comprises the most user friendly molecular design tools available to chemists and modelers worldwide. The platform seamlessly integrates computational technology for designing, filtering, searching and prioritizing molecules for synthesis and biological assessment.
This is a significant update and expands LigandScout's molecular dynamics support. This update also now includes halogen binding as a new pharmacophoric element. In addition plotting has received an upgrade.
Furthermore, LigandScout 4.3 Expert introduces a completely new set of features summarized under the term Remote Execution. It is now possible to screen large compound libraries on remote High Performance Computing directly from within the graphical LigandScout user interface.
It can be downloaded here http://www.inteligand.com/ligandscout4/downloads/LigandScout43macos20181012.dmg
In addition there are now over 40 LigandScout nodes for KNIME.
KNIME Analytics Platform is the open source software for creating data science applications, workflows and services. Intuitive, open, and continuously integrating new developments, KNIME makes understanding data and designing data science workflows and reusable components accessible to everyone.
A timely tutorial and example workflow.
we have put together a more comprehensive workflow, serving as a blueprint for anyone to build her or his own version of a Guided Analytics application to combine just the right amount of automation and interaction for a specific set of problems.
What’s New in KNIME Analytics Platform 3.6.
- KNIME Deep Learning
- Constant Value Column Filter
- Numeric Outliers
- Column Expressions
- Git Nodes
- Call Workflow (Table Based)
- KNIME Server Connection
- Text Processing
- Usability Improvements
- Connect/Unconnect nodes using keyboard shortcuts
- Replacing and connecting nodes with node drop
- Node repository search
- Usability improvements in the KNIME Explorer
- Performance: Column Store (Preview)
- Making views beautiful: CSS changes
- KNIME Big Data Extensions
- Create Local Big Data Environment
- KNIME H2O Sparkling Water Integration
- Support for Apache Spark v2.3
- Big Data File Handling Nodes (Parquet/ORC)
- Spark PCA
- Spark Pivot
- Frequent Item Sets and Association Rules
- Create Spark Context via Livy
- Database Integration
- Apache Kafka Integration
Management (Client Preferences)
- Job View (Preview)
- Distributed Executors (Preview)
General release notes
JSON Path library update
- Java Snippet Bundle Imports
I suspect it will be the KNIME Deep learning that will catch the eye, the ability to set up deep learning models using drag and drop. Use regular Tensorflow models within KNIME Analytics Platform and seamlessly convert from Keras to Tensorflow for efficient network execution
The new Create Local Big Data Environment node creates a fully functional local big data environment including Apache Spark, Apache Hive and HDFS. It allows you to try out the nodes of the KNIME Big Data Extensions without a Hadoop cluster.
The Knime blog has a post containing lots of user submitted tips and tricks
Ever sat next to a friend or colleague at the computer and were awed when you suddenly realised the way they do certain tasks is much better? We recently asked KNIME users to share their tips and tricks on using KNIME. In this series of posts we’ll be showing you how the experts use KNIME in the hopes that by sharing ideas you’ll discover some handy techniques.
Greg Landrum's ICCS 2018 presentation on slideshare
Don't forget to sign up for your chance to hear a webinar by Greg Landrum, Knime's VP for Life Sciences, this Wednesday, He will be talking about processing malaria HTS results using Knime and will give a tutorial on workflows developed for ligand-based virtual screening, based on results of a phenotypic HTS against malaria.
Wed, Feb 21, 2018 3:00 PM - 4:00 PM GMT
The MedChemWizard is a KNIME workflow designed to assist medicinal chemists with idea generation, ligand design and lead optimization using a number of common functional group transformations and medchem rules-of-thumb, this tutorial provided by Dr. Alastair Donald gives a detailed description of it's use.
KNIME 2.7 has been released.
KNIME now runs on Java 7 for Windows and Linux systems (Mac stays on Java 6) Eclipse update 3.7 increases stability on Mac and some Linux systems. BIRT 3.7 brings Open Office support among other new features
JFreeChart nodes have now more setting options in the “General Plot Options” tab of their configuration window.
In R-> Local there are a number of new nodes to import:
- “Table to R” can read a KNIME table into R and output the R workspace.
- “R to Table” takes an R workspace and outputs a KNIME table.
- “R +Data to R” takes an R workspace and optional data input and outputs an R workspace.
- “R to R-View” takes an R workspace and outputs a KNIME view
There is a KNIME tutorial here