Our team working on ML for chemistry/drug discovery was unfortunately affected by the recent layoffs at Google.

I’m still very much interested in how new technologies can accelerate research in the life and natural sciences.

#ml #ai #compchem #drugdiscovery #googlelayoffs
If you know of open roles for which expertise in ML, simulation, or modelling as applied to drug/protein/materials design is sought, please feel free to reach out -- any pointer will be much appreciated!
As an opportunity for change, I’m also open to roles that may stretch my skills and expertise. Due to personal constraints, I’ll be looking for positions primarily in the SF Bay and Boston areas.
My colleagues also have considerable experience in ML and drug discovery, and I can’t say enough good things about them. Again, feel free to reach out if aware of potentially suitable positions.

• • •

Missing some Tweet in this thread? You can try to force a refresh
 

Keep Current with Matteo Aldeghi

Matteo Aldeghi Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @matteo_aldeghi

Jul 29, 2022
1/5 In this work, we introduce a quantitative measure of roughness for molecular property landscapes. @cwcoley @MITChemE @MITIBMLab

arxiv.org/abs/2207.09250
2/5 In molecular design, structure-property relationships are often qualitatively or quantitatively analyzed to guide the navigation of chemical space, with rougher landscapes generally expected to pose tougher optimization challenges.
3/5 To quantitatively capture the roughness of molecular property landscapes, we propose an index that is loosely inspired by the concept of fractal dimension.
Read 5 tweets
May 23, 2022
1/7 In this preprint, @cwcoley and myself try to expand the domain of applicability of graph-based representations and models from well-defined molecules to materials that are ensembles of similar molecules, like polymers.
arxiv.org/abs/2205.08619 Image
2/7 We focus in particular on copolymers. The core idea is simple: describe a molecular ensemble by its average graph structure using weighted edges. For polymers, this captures the average repeating unit. We then used an MPNN with messages weighted accordingly. Image
3/7 We built a computational dataset with ~40k copolymers with varying monomer identities, stoichiometries, and chain architectures. On random splits, this representation returned RMSEs ~5 times lower than a baseline MPNN, and ~3 lower than the next-best model. Image
Read 7 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us on Twitter!

:(