Macs in Chemistry

Insanely Great Science

BBEdit updated

 

BBEdit 12.1.5 contains fixes for reported issues. This update does not contain any new features.

The full release notes are available here https://www.barebones.com/support/bbedit/notes-12.1.5.html.


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KNIME update

 

What’s New in KNIME Analytics Platform 3.6.

  • KNIME Deep Learning
  • Constant Value Column Filter
  • Numeric Outliers
  • Column Expressions
  • Scorer (JavaScript)
  • Git Nodes
  • Call Workflow (Table Based)
  • KNIME Server Connection
  • Text Processing
  • Usability Improvements
  • Connect/Unconnect nodes using keyboard shortcuts
  • Zooming
  • Replacing and connecting nodes with node drop
  • Node repository search
  • Usability improvements in the KNIME Explorer
  • Copy from/Paste to JavaScript Table view/editor
  • Miscellaneous
  • 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
  • Previews
  • Create Spark Context via Livy
  • Database Integration
  • Apache Kafka Integration
  • KNIME Server

  • 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

deeplearning

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.


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Resuts from Avogadro Survey

 

The results of the Avogadro 2018 Community Survey are now in.

Avogadro is an advanced 3D molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible high quality rendering and a powerful plugin architecture.

The results are well worth browsing though but here are a few things I've picked out

  • The most common way people hear about Avogadro by word of mouth.
  • Most people install downloaded binaries
  • Many users can code, mainly Python
  • Most tasks performed centre around initial molecule building and editing

avogadro

You can download from sourceforge here https://sourceforge.net/projects/avogadro/files/latest/download


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The use of augmented reality in chemistry

 

A couple more examples of the use of augmented reality to display chemistry

This also looks interesting.
Touching proteins with virtual bare hands

….A more accessible and intuitive visualization of the three-dimensional configuration of the atomic geometry in the models can be achieved through the implementation of immersive virtual reality (VR). While bespoke commercial VR suites are available, in this work, we present a freely available software pipeline for visualising protein structures through VR. New consumer hardware, such as the HTC Vive and the Oculus Rift utilized in this study, are available at reasonable prices….

https://doi.org/10.1007/s10822-018-0123-0


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ChEMBL 24 predictive models

 

Recently ChEMBL was updated to version 24 the update contains:

  • 2,275,906 compound records
  • 1,828,820 compounds (of which 1,820,035 have mol files)
  • 15,207,914 activities
  • 1,060,283 assays
  • 12,091 targets
  • 69,861 documents

In addition today they released the predictive models built on the updated database, they can be downloaded from the ChEMBL ftp server ftp://ftp.ebi.ac.uk/pub/databases/chembl/target_predictions

There are 1569 models.


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