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 (http://www.c-inf.net), and the 3rd In Silico Toxicology Conference on 29 September 2022, which will be held fully virtually (http://drugdiscovery.net/tox2022/). 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…
Events
23 August 2022
Challenges of Developing Antibiotic Combinations
Virtual Event
https://register.gotowebinar.com/register/1366747252994157323?source=partners
7 September 2022
Cambridge Cheminformatics Meeting
Hybrid Event (at the CCDC/via Zoom)
http://www.c-inf.net
12-16 September 2022
OpenTox 2022 Virtual Conference
Virtual Event
https://www.opentox.net/events/virtual-conference-2022
16 September 2022
CeBIL Annual Symposium 2022 – Intellectual Property and Drug Repurposing: New Frontiers
Virtual Event
https://www.eventbrite.co.uk/e/intellectual-property-drug-repurposing-new-frontiers-tickets-313095496057
29 September 2022
3rd In Silico Toxicology Conference
Virtual Event
http://drugdiscovery.net/tox2022/
3/4 October 2022
NIH Clustering and Classification Workshop:
Applications to Investigate Adverse Effects of Chemicals on Human Health and Environment
Virtual Event
https://www.niehs.nih.gov/news/events/nams2022/index.cfm
3/4 November 2022
HESI’s Emerging Systems Toxicology for the Assessment of Risk (eSTAR) Annual Meeting
Virtual Event
https://hesiglobal.org/estar-am2022/
8-10 November 2022
BioTechX 2022
Basel, Switzerland
https://www.terrapinn.com/conference/biotechx/index.stm
10/11 November 2022
11th Drug Discovery Strategic Summit
Amsterdam, The Netherlands
https://drugdiscoverystrategicsummit.com/
Jobs
Tenure Track Assistant Professor with Education – Pharma Data Sciences and Statistics
University of Groningen
Groningen, The Netherlands
https://jobs.zeit.de/jobs/tt-assistant-professor-with-education-profile-pharma-data-sciences-statistics-university-of-groningen-groningen-niederlande-1061747
Data Developer
Pepticom
Jerusalem, Israel
https://www.linkedin.com/jobs/view/3188295537
(Senior) Scientist – Computer Aided Drug Discovery / Molecular Modelling
Selvita
Cracow, Poland
https://www.linkedin.com/jobs/view/3190964765
Scientist/Bioinformatician with Protein Data Science experience
Roche
Penzberg, Germany
https://www.linkedin.com/jobs/view/3201462176
Senior Machine Learning Scientist, Senior Structural Computational Scientist
https://wildbio.tech/careers/
Wild Biotech
Rehovot, Israel
Machine Learning Research Scientist
Pfizer
Berlin, Germany
https://www.linkedin.com/jobs/view/3209827634
Drug Discovery Data Scientist (and others)
Exscientia
Oxford/Cambridge/Dundee, UK
https://www.exscientia.ai/careers
Computational Chemist/Cheminformatician
SoseiHeptares
Cambridge, UK
https://cezanneondemand.intervieweb.it/heptares/jobs/computational-chemistcheminformatician-senior-scientist-25877/en/
Postdoctoral Researcher – Computational Chemistry
Syngenta
Jealotts Hill, UK
https://www.access-sciencejobs.uk/job/330145/postdoctoral-researcher-berkshire
Postdoctoral Fellow – Computational Toxicology
Environmental Protection Agency (EPA)
Research Triangle Park, NC
https://cfpub.epa.gov/ordpd/PostDoc_Position.cfm?pos_id=1477
Senior Scientist, Research Informatics
Tango Therapeutics
Cambridge, MA
https://boards.greenhouse.io/tangotherapeutics/jobs/5192005003
Cheminformatics…
Why AlphaFold won’t revolutionise drug discovery
https://www.chemistryworld.com/opinion/why-alphafold-wont-revolutionise-drug-discovery/4016051.article
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”
https://github.com/wjm41/molplotly
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
https://www.biorxiv.org/content/10.1101/2022.05.17.492280v1
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?
https://www.drugdiscoveryonline.com/doc/can-molecular-modeling-overcome-the-limitations-of-drug-discovery-ai-0001
Back to the roots… 🙂
… beyond Cheminformatics …
Folding tools
https://github.com/sacdallago/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
https://www.hyphadiscovery.com/blog/bioisosteres-that-influence-metabolism/
Blog post discussing the above – always good to look at structures
Could machine learning fuel a reproducibility crisis in science?
https://www.nature.com/articles/d41586-022-02035-w
Well – it probably does already!
Leakage and the Reproducibility Crisis in ML-based Science
https://arxiv.org/pdf/2207.07048.pdf
The paper cited in the previous article
How scientists fool themselves – and how they can stop
https://www.nature.com/articles/526182a
Also related to the above
Machine Learning Informs RNA-Binding Chemical Space
https://www.biorxiv.org/content/10.1101/2022.08.01.502065v1
25k molecules in 36 RNA screens, binding data available, for a target class of current interest
Could a Neuroscientist Understand a Microprocessor?
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005268
Hint: Probably not
Machine Learning, by Tom Mitchell
http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlbook.html
Now freely available as a PDF (very good and easily readable intro to the various ML methods)
Legal templates for early-stage companies
https://twitter.com/volodarik/status/1555551475576733696?s=19
May be useful for some
PyOD – Python Outlier Detection Library
https://www.linkedin.com/posts/emanuelefabbiani_opensource-datascience-machinelearnin-activity-6960867177931366402-kB6K
As the title says
Molecular dynamics simulations of 2 evolutionary-linked glycosylated influenza A whole-virion models
https://twitter.com/RommieAmaro/status/1556677117491916801
MD simulations have clearly gotten bigger…
Deep Learning Lectures
https://www.youtube.com/playlist?list=PLqPI2gxxYgMKN5AVcTajQ79BTV4BiFN_0
by Frank Noe
Geometric Deep Learning Lectures
https://geometricdeeplearning.com/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
https://arxiv.org/abs/2206.13211
… but yes, it is possible to crack nuts with a sledgehammer!
Open innovation: A paradigm shift in pharma R&D?
https://www.sciencedirect.com/science/article/pii/S1359644622002094
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
https://www.linkedin.com/posts/mike-petty-6843a664_resources-for-cambridgeshires-history-on-activity-6958006672011227137-DeyJ
For those interested in Cambridge’s past
Charter Cities: The Real Reason for Brexit and the Bigger Picture (by Cormack Lawson)
https://medium.com/@cormack.lawson/charter-cities-the-real-reason-for-brexit-and-the-bigger-picture-4de80dbb69fb
So what was Brexit actually about? (Cui bono?)
Related also eg https://twitter.com/mattprescott/status/1556426098132623363 and https://twitter.com/CabalGretas/status/1556968778294067203
Hedy Lemarr: “One of the greatest actors of all time – and also the inventor of Bluetooth”
https://en.wikipedia.org/wiki/Hedy_Lamarr
Quite a diverse life!
“Kentucky Noah’s Ark sues insurance company over damage caused by heavy rains”
https://www.cbsnews.com/news/kentucky-noahs-ark-encounter-sues-insurance-company-over-heavy-rain-damage/
🙂
“The difference between happiness, meaning, and true psychological richness.”
https://twitter.com/Julian/status/1482506906102943745/photo/1
There is truth to it
“How to Write Good”
https://twitter.com/ReskiLab/status/1554479408198606849?t=tp-D1vCcWaDWTv1dIMdc1w&s=19
The most helpful writing guide I have ever seen
I believe this is all from my side for now – if you have any information for me to circulate, or wish to present at one of our next Cambridge Cheminformatics (http://www.c-inf.net) or Berlin Digital Science for Drug Discovery Meetings (http://www.digidrug.net), please just let me know, cheers!
Best wishes,
Andreas