I will be taking interns and hiring FTEs for my advanced ML team at @insitro.
If you are interested in #AI4science and want to work on methods inspired by real world problems in drug discovery reach out. I am also at #neurips22 until the end of the week.
Topics! Links in 🧵1/4
We’re interested in:
generative models
causal inference
Robustness/uncertainty
Domain specific models for biology/genetics/chemistry/imaging
Probabilistic/deep modeling
Decision making & experimentation
We also have a lot of other roles across machine learning and data science you may be interested in across all levels and seniorities, please have a look at insitro.com/careers
And reach out to me directly via DM or at the conference.
4/4
I should add “clinical data” as a key application domain we are interested in.
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Beautiful new work “Del-Dock” on marrying DELs and docking stemming from the summer internship of @KShmilovich with us, led by him and Benson Chen, and our first of many works with @mmsltn and yours truly.
In this work we learn deep representations using attention models in top of sampled docking poses combined with readouts from DNA encoded libraries to predict binding affinity. We introduce a likelihood that takes care of background matrix binding.
See what DELs do here 2/6
Why does all this matter? Docking poses are typically sampled from somewhat misspecified energy functions. Using the target binding information we can learn representations which do not rank the samples poses by their likelihood, but by how well they bind, retailing the poses 3/6