Back in June Apple revealed its plan to transition away from Intel processors inside the Mac to its own processors, Apple Silicon a custom ARM based chip. Whilst initial reports on performance have been very promising, there is always the concern about support for the key scientific software libraries such as numpy and scipy.

Well it seems a new platform osx-arm64 has been added to the build matrix of conda-forge.

Full details are here https://conda-forge.org/blog/posts/2020-10-29-macos-arm64/.

Installed conda will be able to install packages like numpy, scipy. Currently there are about 100 packages out of 10000 packages pre-built for this platform.

Also see clang compilers for conda-build 3

https://anaconda.org/conda-forge/clang_osx-arm64

 

The 2nd RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry is now over, two intensive days of presentations and posters. Many thanks for all who took part and made it such a successful event.

Special mention to the Poster prize winners.

P17 by Jenke Scheen of the University of Edinburgh Entitled: "Improving the accuracy of alchemical free energy methods by learning correction terms for binding energy estimates"

P6 by Adam Green of the University of Leeds Entitled: "Activity-directed discovery of inhibitors of the p53/MDM2 interaction: towards autonomous functional molecule discovery"

P3 by Ya Chen of the University of Hamburg Entitled: "NP-Scout: machine learning approach for the identification of natural products and natural product-like compounds in large molecular databases"

If you want to browse through the Twitter feeds search for the #AIChem19 hashtag.

Many of the presentations are now available in pdf format on the meeting website.

We are already thinking about a possible 3rd meeting, and any feedback would be much appreciated.

 

Camelot is described as a PDF Table Extraction for Humans, it is a Python library that makes it easy to extract tables from PDF files.

>>> import camelot
>>> tables = camelot.read_pdf('foo.pdf')
>>> tables
<TableList n=1>
>>> tables.export('foo.csv', f='csv', compress=True) # json, excel, html
>>> tables[0]
<Table shape=(7, 7)>
>>> tables[0].parsing_report
{
    'accuracy': 99.02,
    'whitespace': 12.24,
    'order': 1,
    'page': 1
}
>>> tables[0].to_csv('foo.csv') # to_json, to_excel, to_html
>>> tables[0].df # get a pandas DataFrame!

Camelot only works with text-based PDFs and not scanned documents. Camelot also comes with a command-line interface. It can be installed using conda

$ conda install -c camelot-dev camelot-py

I've added it to the Data Analysis tools page

 

A while back I wrote a very popular page describing how to install a wide variety of chemiformatics packages on a Mac, since there have been some changes with Homebrew which have meant that a few of the scientific applications are no longer available so I've decided to rewrite the page on installing the missing packages using Anaconda.

I've also included a list of quick demos so you can everything is working as expected.

Full details are here

Packages include:

• OpenBabel
• RDKit
• brew install cdk
• chemspot
• indigo
• inchi
• opsin
• osra
• pymol
• oddt

In addition to gfortran and a selection of developers tools.