Macs in Chemistry

Insanely Great Science

ChemDoodle 2D v11.3 Update

 

I just heard version 11.3 of ChemDoodle 2D software has been released.

ChemDoodle 2D v11.3 is a feature update. The main new feature is expert IUPAC naming support for free radicals. Other major features include new image output sizing options with previews, and stoichiometry table options.

ChemDoodle 2D is a popular and extensively featured Chemical Drawing and cheminformatics software tool.

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Mathematica System requirements

 

A couple of folks have asked me about running Mathematica on Apple Silicon. I don't use Mathematica but the system compatibility is on their website.

Mathematica 12.2 is optimized for the latest operating systems and hardware. mathematicaSystemRequirements

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Analyzing the runtime, energy usage, and performance of Tensorflow training on a M1 Mac Mini and Nvidia V100

 

An interesting comparison of Apple Intel and M1 chips machines with Nvidia 100 when using Tensorflow.

https://wandb.ai/vanpelt/m1-benchmark/reports/Can-Apple-s-M1-help-you-train-models-faster-cheaper-than-NVIDIA-s-V100---VmlldzozNTkyMzg

We ran a sweep of 8 different configurations of our training script and show that the Apple M1 offers impressive performance within reach of much more expensive and less energy efficient accelerators such as the Nvidia V100 for smaller architectures and datasets.

Code is available on Colab https://colab.research.google.com/drive/1RvZBpzJRW9MNPWQ2rZG8HIyHJbaCwnTI.

They also include tips on setting up a Mac mini to run Tensorflow.

Only initial results on modest data sets, will be interesting to see the performance when Apple releases more Pro hardware.

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Latest RSC CICAG newsletter

 

The latest RSC CICAG newsletter (Winter 2020) is now available http://rsccicag.org/newsletters.htm. It includes:-

Chemical Information & Computer Applications Group Chair's Report
CICAG Planned and Proposed Future Meetings
Memories of Dr Angus McDougall, 1934-2020
CASP14: DeepMind’s AlphaFold 2 – an Assessment
Meeting Report: 3rd RSC BMCS & CICAG AI in Chemistry Meeting
ReadMe and HowTo for Lightning Poster Presentations
As Conferences went Online: What do we miss the most?
Alan F Neville, 1943-2020, BSc, PhD
Parallel Processing for Molecular Modeling in ChemDoodle 3D
Catalyst Science Discovery Centre & Museum Trust: A Year in Review
The 6th Tony Kent Strix Annual Memorial Lecture 2020
Open Chemical Science Meetings and Workshops - Introduction
Open Access Publishing for Chemistry – Meeting Reports
Open Data for Chemistry – Meeting Reports
Open Source Tools for Chemistry – Workshop Reports
RSC Open Access Journals and Future Plans
RSC’s Journal Archives Available for Text and Data Mining
Chemical Information / Cheminformatics and Related Books
News from AI3SD 64 Other Chemical Information Related News

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Why Apple's M1 Chip is So Fast

 

A technical but still very accessible (15 min) analysis of the latest Apple M1 chip. Well worth spending a coffee break viewing.

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JupyterLab 3.0 released

 

JupyterLab is the next-generation web-based user interface for Project Jupyter.

JupyterLab 3.0 includes a number of new features and enhancements that are described on the Jupyter blog. Full details are described in the ChangeLog

To install using conda

conda install -c conda-forge jupyterlab=3

However note that some extensions may not yet have been updated.

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OpenChem

 

OpenChem is a deep learning toolkit for Computational Chemistry with PyTorch backend. The goal of OpenChem is to make Deep Learning models an easy-to-use tool for Computational Chemistry and Drug Design Researchers.

You can read about in this publication DOI.

All code is available on GitHub https://github.com/Mariewelt/OpenChem.

Requires

  • Modern NVIDIA GPU, compute capability 3.5 or newer.
  • Python 3.5 or newer (we recommend Anaconda distribution)
  • CUDA 9.0 or newer

numpy, pyyaml, scipy, ipython, mkl, scikit-learn, six, pytest, pytest-cov

The software is licensed under the MIT license

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RDKit blog

 

If you are a RDKit user then you should bookmark Greg Landrum's RDKit blog https://greglandrum.github.io/rdkit-blog/about/. This is a new site and all the old content will be migrated in due course.

RDKitBlog

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Annual Site Review

 

At the end of each year I have a look at the website analytics to see which items were the most popular.

Over the year there were 90,006 visitors, an increase of 28% over 2019, spending an average of 2.5 minutes per session, looking at the regular visitors there are around 4000 who visited 20-200 times per year. The US provided 28% of the visitors and the UK 8% with Germany, India, China and Japan around 5%. As might be expected 57% of the visitors were using a Mac, but 23% of the visitors were Windows users, 9% iOS and 6% Android, also 4% Linux. There has been a gradual increase in the number of visitors using mobile devices.

Again the most popular page was Fortran on a Mac which has been updated a couple of times this year with reader suggestions. Other popular pages include the Reviews and the Hints and Tutorials. The page describing the update to iBabel was particularly popular.

viewerTab

The post about Scientific Applications under Catalina made it to number 4 in the years listing and elicited a significant amount of reader feedback.

The Mobile Science site has seen increased visitor numbers.

The most popular apps viewed were.

Merck PTE
IBM Micromedex Drug Info
PocketCAS: Mathematics Toolkit
The Periodic Table Project
Periodic Table

Also popular were

Python3IDE Human Anatomy Atlas 2019
ChemTube3D.
Molecular Constructor

The Twitter feed @macinchem has steadily attracted new followers and currently has 993 followers.

The most popular tweets were

IOData: A python library for reading, writing, and converting computational chemistry file formats

and

Google Colab is very cool. .


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