Macs in Chemistry

Insanely Great Science

artificial intelligence

Intelligently Automating Machine Learning, Artificial Intelligence, and Data Science

 

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.

Full details here


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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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Second Major DeepChem Release

 

A major update the DeepChem has been announced.

This major version release finishes consolidating the DeepChem codebase around our TensorGraph API for constructing complex models in DeepChem. We've made a variety of improvements to TensorGraph's saving/loading features and added a number of new tutorials improving our documentation of TensorGraph. We've also removed a number of older deprecated submodules and models in favor of the new, standardized TensorGraph implementations.

In addition, we've implemented a number of new deep models and algorithms, including DRAGONNs, Molecular Autoencoders, MIX+GANs, continuous space A3C, MCTS for RL, Mol2Vec and more. We've also continued improving our core graph convolutional implementations.

Also remember the RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry Meeting registration is now open.


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Artificial Intelligence in Chemistry

 

I mentioned the first announcement of a meeting to be held next year.

RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry Friday, 15th June 2018 Royal Society of Chemistry at Burlington House, London, UK.
Twitter hashtag - #RSC_AIChem

AI-web-image-1

A number of the speakers have now been confirmed.

Confirmed Speakers

Keynote: What I learned about machine learning - revisited Bob Sheridan, Merck

Presentation title to be confirmed Nadine Schneider, Novartis

Scaling de novo design, from single target to disease portfolio Wilhem van Hoorn, Exscientia

Presentation title to be confirmed Marwin Segler, Benevolent AI

Molecular de novo design through deep learning Ola Engkvist, AstraZeneca

I also notice that there are a number of EPSRC funding opportunities

Artificial Intelligence - UKRI CDTs EPSRC is expected to support 10-20 doctoral training positions.

The call is now open for around 15 Centres for Doctoral Training (CDTs) focused on areas relevant to Artificial Intelligence (AI) across UKRI's remit. This call opens against the background of Professor Dame Wendy Hall and Jérôme Pesenti's review, Growing the artificial intelligence industry in the UK, and the Government's Industrial Strategy White Paper, Building a Britain fit for the Future. This investment in AI skills will be kick-started by support for over 100 studentships that will be funded during 2018/19 via the Research Councils current mechanisms and schemes.

Universities are invited to apply against two priority areas:

Enabling Intelligence, a priority area within Engineering and Physical Sciences Research Council's (EPSRC) main CDT call
Applications and Implications of Artificial Intelligence (AIAI), a new priority area relevant to all Research Councils.

More info..



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Deep Learning Cheat Sheet (using Python Libraries)

 

Just came across this really invaluable resource.

  • Deep Learning Cheat Sheet (using Python Libraries)
  • PySpark Cheat Sheet: Spark in Python
  • Data Science in Python: Pandas Cheat Sheet
  • Cheat Sheet: Python Basics For Data Science
  • A Cheat Sheet on Probability
  • Cheat Sheet: Data Visualization with R
  • New Machine Learning Cheat Sheet by Emily Barry
  • Matplotlib Cheat Sheet
  • One-page R: a survival guide to data science with R
  • Cheat Sheet: Data Visualization in Python
  • Stata Cheat Sheet
  • Common Probability Distributions: The Data Scientist’s Crib Sheet
  • Data Science Cheat Sheet
  • 24 Data Science, R, Python, Excel, and Machine Learning Cheat Sheets
  • 14 Great Machine Learning, Data Science, R , DataViz Cheat Sheets



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RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry

 

The first announcement of a meeting to be held next year.

RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry Friday, 15th June 2018 Royal Society of Chemistry at Burlington House, London, UK.
Twitter hashtag - #RSC_AIChem

AIfirst_announcement

Artificial Intelligence is presently experiencing a renaissance in development of new methods and practical applications to ongoing challenges in Chemistry. We are pleased to announce that the Biological & Medicinal Chemistry Sector (BMCS) and Chemical Information & Computer Applications Group (CICAG) of the Royal Society of Chemistry are organising a one-day conference entitled Artificial Intelligence in Chemistry to present the current efforts in applying these new methods. We will combine aspects of artificial intelligence and deep machine learning methods to applications in chemistry.

Applications for oral and poster presentations are welcomed. Posters will be displayed throughout the day and applicants will be asked if they would like to provide a two-minute flash oral presentation when submitting their abstract. Closing dates are 31st January for oral and 13th April for poster submissions.

More details here http://www.maggichurchouseevents.co.uk/bmcs/AI-2018.htm.


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“Found in Translation”: Predicting Outcomes of Complex Organic Chemistry Reactions

 

An interesting paper uses 1,808,938 reactions from the patent literature as a training set to build a model to predict reactions.

There is an intuitive analogy of an organic chemist's understanding of a compound and a language speaker's understanding of a word. Consequently, it is possible to introduce the basic concepts and analyze potential impacts of linguistic analysis to the world of organic chemistry. In this work, we cast the reaction prediction task as a translation problem by introducing a template-free sequence-to-sequence model, trained end-to-end and fully data-driven. We propose a novel way of tokenization, which is arbitrarily extensible with reaction information. With this approach, we demonstrate results superior to the state-of-the-art solution by a significant margin on the top-1 accuracy. Specifically, our approach achieves an accuracy of 80.1% without relying on auxiliary knowledge such as reaction templates. Also, 66.4% accuracy is reached on a larger and noisier dataset.

There is also a brief video describing the work.


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