Going to #ACSFall2024 and interested in prospective applications of machine learning in chemistry? Come by on Monday to the mile high ballroom to see a fanstistic lineup of leaders in the field including @chemchristensen, @reisman Brent Koscher, @teodorolaino , and @marwinsegler
We have an awesome postdoc opportunity in my lab @PrescientDesign as part of @genentech Research and Early Development, co-advised by James @crwfrd_SF in Discovery Chemistry. Come work with us on the next generation of ML approaches for chemical discovery! https://t.co/AXCZgwzckN
Amazing workshop, thanks @tiwarylab for all the coordination and getting a stellar lineup of speakers! Lots to be done in ML+Chemistry and the discussions were very enlightening
We @gpggrp will have an opening for an @NSF_CCAS postdoc soon. The ideal candidate must have experience and/or interest in at least some of the following: • computational/physical organic chemistry; • ML & evolutionary computing; • (bio)catalysis/(bio)organic materials.
@CarlNising Thing are definitely looking up and it is good to start being more proactive about it. These types of editorials are great to spread more awareness of the problems.
Mostly agree. The comparison to other realms is not good though. Chemistry data is expensive which means we have to find intelligent ways to use the data not generate data that fits the algorithm. Principled data generation is important but won’t solve the problem.
Explore our latest paper on designing catalysts using deep generative models and computational data. Discover how an RNN-VAE can generate new catalyst candidates for the Suzuki coupling. https://t.co/qaTfzq2JXb @IBMResearch@SchwallerGroup@acvaucher@pschwllr@teodorolaino
Hiring for a post-doctoral position in reaction informatics. In this role, you will develop new ways that allow novel chemical transformations to unlock new chemical spaces. Happy to help with questions!
https://t.co/g4dxm4pSud
#cheminformatics#hiring#postdoc#drugdiscovery
This is a really important question to have answered for yourself before applying and interviewing. I can’t tell you how many candidates I’ve screened that simply say I just want a job. While that is valid, the jobs usually go to someone that has a passion for the type of work.
@rguha For one off data analyst then cartridge is probably better IMO. For delivering to chemists we actually have other even faster option which is not cost effective from the compute side but very effective saving min-hrs of >300 chemists time weekly
@rguha Complicated question! The value for us comes at large scale repeated tasks that can and should be automated 1) freeing scientists time is cost effective and 2) dynamic horizontal scaling only pays for compute for a short time 3) shared responsibility with IT is easier
@rguha Doing some basic filtering at the db query level if you can, then using spark to distribute the SSS or sim search is the best I got so far when cartridges aren’t available, then returning only molecules with match or sim threshold
@rguha We built a lot of MLOps infrastructure on databricks but our tables are all in AWS (redshift or Postgres) so would just use the cartridges there. Not easy with redshift which is probably similar to the tables on databricks itself.
@KRHornberger Happy to team up on best practices from the predictive side as well for anyone that has question on any of these topics in regard to QSAR modeling. Why memorize EVERY rule for EVERY endpoint when you can automate some of the pattern recognition