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Cambridge Cheminformatics Newsletter, 23 August 2022

Dear All,

I would like to circulate some current Cheminformatics- (and related) news to everyone as follows.

In particular I would like to point out our next Cambridge Cheminformatics Meeting on 7 September 2022, which will be held as a hybrid event at the CCDC as well as via Zoom (, and the 3rd In Silico Toxicology Conference on 29 September 2022, which will be held fully virtually ( Both events are free to attend and open to all – please distribute to your contacts who might be interested as well, and hope to see you there!

If you have information from your side for distribution please just let me know, and I am very happy to include it on the next occasion.

So here we go…


23 August 2022
Challenges of Developing Antibiotic Combinations
Virtual Event

7 September 2022
Cambridge Cheminformatics Meeting
Hybrid Event (at the CCDC/via Zoom)

12-16 September 2022
OpenTox 2022 Virtual Conference
Virtual Event

16 September 2022
CeBIL Annual Symposium 2022 – Intellectual Property and Drug Repurposing: New Frontiers
Virtual Event

29 September 2022
3rd In Silico Toxicology Conference
Virtual Event

3/4 October 2022
NIH Clustering and Classification Workshop:
Applications to Investigate Adverse Effects of Chemicals on Human Health and Environment
Virtual Event

3/4 November 2022
HESIโ€™s Emerging Systems Toxicology for the Assessment of Risk (eSTAR) Annual Meeting
Virtual Event

8-10 November 2022
BioTechX 2022
Basel, Switzerland

10/11 November 2022
11th Drug Discovery Strategic Summit
Amsterdam, The Netherlands


Tenure Track Assistant Professor with Education – Pharma Data Sciences and Statistics
University of Groningen
Groningen, The Netherlands

Data Developer
Jerusalem, Israel

(Senior) Scientist – Computer Aided Drug Discovery / Molecular Modelling
Cracow, Poland

Scientist/Bioinformatician with Protein Data Science experience
Penzberg, Germany

Senior Machine Learning Scientist, Senior Structural Computational Scientist
Wild Biotech
Rehovot, Israel

Machine Learning Research Scientist
Berlin, Germany

Drug Discovery Data Scientist (and others)
Oxford/Cambridge/Dundee, UK

Computational Chemist/Cheminformatician
Cambridge, UK

Postdoctoral Researcher – Computational Chemistry
Jealotts Hill, UK

Postdoctoral Fellow – Computational Toxicology
Environmental Protection Agency (EPA)
Research Triangle Park, NC

Senior Scientist, Research Informatics
Tango Therapeutics
Cambridge, MA


Why AlphaFold wonโ€™t revolutionise drug discovery
By Derek Lowe

“molplotly is an add-on to plotly built on RDKit which allows 2D images of molecules to be shown in plotly figures when hovering over the data points”
I remember when you needed plug-ins for Spotfire at horrendous cost to do this – glad times changed!

Assessing PDB Macromolecular Crystal Structure Confidence at the Individual Amino Acid Residue Level
Comparison of X-ray and AlphaFold predictions – yes, there is still value in doing experiments (phew!)

Can Molecular Modeling Overcome The Limitations Of Drug Discovery AI?
Back to the roots… ๐Ÿ™‚

… beyond Cheminformatics …

Folding tools
A list of the ca 20 tools to predict protein folding out there right now, compiled by Christian Dallago

Bioisosteres that influence metabolism
Blog post discussing the above – always good to look at structures

Could machine learning fuel a reproducibility crisis in science?
Well – it probably does already!

Leakage and the Reproducibility Crisis in ML-based Science
The paper cited in the previous article

How scientists fool themselves โ€“ and how they can stop
Also related to the above

Machine Learning Informs RNA-Binding Chemical Space
25k molecules in 36 RNA screens, binding data available, for a target class of current interest

Could a Neuroscientist Understand a Microprocessor?
Hint: Probably not

Machine Learning, by Tom Mitchell
Now freely available as a PDF (very good and easily readable intro to the various ML methods)

Legal templates for early-stage companies
May be useful for some

PyOD – Python Outlier Detection Library
As the title says

Molecular dynamics simulations of 2 evolutionary-linked glycosylated influenza A whole-virion models
MD simulations have clearly gotten bigger…

Deep Learning Lectures
by Frank Noe

Geometric Deep Learning Lectures
By Michael Bronstein, Joan Bruna, Taco Cohen, and Petar Velickovic

Cracking nuts with a sledgehammer: when modern graph neural networks do worse than classical greedy algorithms
… but yes, it is possible to crack nuts with a sledgehammer!

Open innovation: A paradigm shift in pharma R&D?
I am not sure the information gathered this way matches reality on the ground, but still possibly of interest

… and clearly beyond Cheminformatics

Mike Petty’s Resources for Cambridge/Cambridgeshire history
For those interested in Cambridge’s past

Charter Cities: The Real Reason for Brexit and the Bigger Picture (by Cormack Lawson)
So what was Brexit actually about? (Cui bono?)

Related also eg and

Hedy Lemarr: “One of the greatest actors of all time – and also the inventor of Bluetooth”
Quite a diverse life!

“Kentucky Noah’s Ark sues insurance company over damage caused by heavy rains”

“The difference between happiness, meaning, and true psychological richness.”
There is truth to it

“How to Write Good”
The most helpful writing guide I have ever seen

I believe this is all from my side for now โ€“ if you have any information for me to circulate, or wish to present at one of our next Cambridge Cheminformatics ( or Berlin Digital Science for Drug Discovery Meetings (, please just let me know, cheers!

Best wishes,


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