CCK-6

The next Comp Chem Kitchen for computational chemists, cheminformaticians, and molecular modelers will be on Thursday January 12th, 2017, at 5.00 pm in the Abbot’s Kitchen in the Inorganic Chemistry Laboratory. Our main speaker will be: Dr Russell Viner from Syngenta, UK, on Structure-Based Design of a Novel Class of Herbicidal HPPD Inhibitors. You can register here.

Refreshments will be provided.

We will also have a lightning talk:
  • Anthony Bradley (Chemistry / DLS)  MMTF: Faster access to protein structure data from the PDB
Please get in touch to volunteer (or nominate) anyone (students, postdocs, professionals, PIs, Emeritus Professors) to give a “lightning talk” of up to 5 minutes. The talks usually resemble one of the following styles:
  • an overview of computational chemistry in your research
  • a (live!) demonstration of some software that you are developing or using
  • a summary of a computational chemistry paper, method, or tool that you’ve seen recently

See previous CCK announcements for examples. Contact Garrett M. Morris, Richard Cooper, Phil Biggin or Rob Paton to volunteer.

CCK-5 [including RDKit workshop]

evotecukCCK-5, on Tuesday, November 1st, 2016, will be another “two-parter” comprised of CCK-5.1 and CCK-5.2. We would like to thank Evotec(UK) Ltd. for supporting this event.

For our fifth meeting, CCK-5.1, on Tuesday, November 1st, 2016, we are honoured to be hosting Greg Landrum; he is the leading developer of the open source cheminformatics toolkit, RDKit.

RDKit is an open source C++ toolkit for cheminformatics with Python, Java and C# wrappers and a number of KNIME cheminformatics nodes. A full overview of its functionality is provided in the documentation.


CCK-5.1

2:00-4:00 pm
Abbot’s Kitchen, Inorganic Chemistry Laboratory, South Parks Road, Oxford, OX1 3QR:

  • Dr Greg Landrum (VP Life Sciences, KNIME.com; and Managing Director,T5 Informatics GmbH) will run a hands-on workshop and hackathon on RDKit. Please bring your laptop (Windows, Mac or Linux): and don’t worry if you do not have RDKit installed. We will help you get set up.

A total of 35 places are available, please register early to avoid disappointment. Registration  for CCK-5.1.


CCK-5.2

5.00 – 6:00 pm
Talks and refreshments/beer, will again be held in the Abbot’s Kitchen in the Inorganic Chemistry Laboratory, South Parks Road, Oxford, OX1 3QR:

Register for CCK-5.2.

CCK-4


The fourth Comp Chem Kitchen for computational chemists, cheminformaticians, and molecular modelers will be on Tuesday October 4th, 2016, at 5.30 pm in the ICL Abbot’s Kitchen. Confirmed speaker: Dr Nathan Brown from The Institute of Cancer Research, London, who will speak about his recent paper on the origins of three-dimensionality in drug-like molecules.

Lightning talks (5 minutes or fewer):
Refreshements provided. Five-minute lightning contributions welcome – contact Garrett M. Morris, Richard Cooper, Phil Biggin or Rob Paton.

CCK-2 (Tuesday 14th June)

Our second meeting, CCK-2 will be held in the Abbott’s Kitchen in the Inorganic Chemistry Laboratory, South Parks Road, Oxford, OX1 3QR at 5 pm on Tuesday June 14th 2016 (8th Week). Free tickets are available.

Speakers

  • Mike Bodkin (Vice President, Research Informatics, Evotec), “Chemical space and how to warp drive discovery”.
  • Jonathan Yates (Department of Materials, University of Oxford), Lightning talk,  “A brief introduction to the Collaborative Computational Project for NMR Crystallography (CCP-NC)”.
  • Jonny Brooks-Bartlett (Elspeth Garman Group, Department of Biochemistry); Lightning talk: “The Julia Programming Language”.
  • Fernanda Duarte (Rob Paton Group, Department of Chemistry, University of Oxford): Lightning talk: “Exploring biochemical systems using the Empirical Valence Bond (EVB) approach”.
  • Matteo Degiacomi (Justin Benesch Group, Department of Chemistry, University of Oxford): Lightning talk: “The Python package BiobOx: a collection of data structures, tools and methods for biomolecular modelling” BiobOx is used for manipulation, measurement, analysis and assembly of atomistic and super coarse-grain structures as well as EM maps.

Talks will take place between 5pm and 6pm, please stay for refreshments and chat afterwards.

We would like to thank Prof. Philip Biggin and the MRC Proximity to Discovery Fund for supporting CCK.

CSD Python API example

Number of non-H atoms in molecules reported in the Cambridge Structural Database

An unexplained phenomenon in the CSD collection of molecular crystal structures is shown below.

The code below is included in Jerome Wicker’s talk from CCK-1 as a simple example of how to iterate through and extract information from the CSD using the Python API. The phenomenon has been noted many times by CCDC researchers.

The CSD Python API is used to retreive each crystal structure entry from the database using the EntryReader() iterator. The number of heavy atoms (non-hydrogen atoms) in the heaviest molecular component of organic crystal structure is appended to a list heavy_atoms.

Finally a histogram of these heavy atom counts shows that molecules with even numbers of heavy atoms are observed more frequently than those with odd numbers in the same range.

In [6]:
%pylab inline
Populating the interactive namespace from numpy and matplotlib

This page is a copy of an interactive Python 2 notebook exported from Jupyter. In Jupyter, the %pylab inline command above loads numerical and plotting libraries and ensures that plots appear in the notebook instead of in a separate window. It isn’t required if running python from the command line, and the required libraries (numpy and matplotlib) are reimported below for convenience.

In [7]:
from ccdc.io import EntryReader
from matplotlib import pyplot as plt
import numpy as np

csd_reader = EntryReader('CSD')
heavy_atoms = []

for entry in csd_reader:
    if entry.is_organic:
        try:
            mol = entry.molecule.heaviest_component
        except:
            continue
        heavy_atoms.append(len(mol.heavy_atoms))
        
plt.figure()
plt.hist(heavy_atoms,bins=np.max(heavy_atoms))
plt.xlabel('Number of heavy atoms',fontsize=20)
plt.ylabel('Hits in CSD', fontsize=20)
plt.show()

Limiting the x-axis range:

In [11]:
plt.figure()
plt.hist(heavy_atoms,bins=np.max(heavy_atoms))
plt.xlabel('Number of heavy atoms',fontsize=20)
plt.ylabel('Hits in CSD', fontsize=20)
plt.xlim([10,60])
plt.show()
In [ ]: