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Home of the TechBio community. LoGG/M2D2/CARE reading groups, blogs, events, and more.
Joined December 2023
In June, we hosted the molecular machine learning conference @Mila_Quebec. With 180+ attendees and 45+ posters, it was amazing to see the progress in ML research for drug discovery. Thank you everyone for a great conference! See you at the next MoML đź‘‹
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RT @hsu_jonny: I'll be hosting Jeremy Ash from the Polaris Small Molecule Steering Committee in 30 minutes to discuss the latest guidelines….
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Watch the recording of this week’s LoGG Talk with Lazar Atanackovic here:
Next Monday at 12 pm ET, Lazar Atanackovic will join LoGG host Hannes Stark to discuss modeling the continuous evolution of interacting biological systems using Meta Flow Matching (MFM). 📄 Read the paper: ⏺️ Tune in live:
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Watch the recording of this week’s LoGG Talk with Ali Saberi here:
Next Monday at 12 pm ET, Ali Saberi will join LoGG host @HannesStaerk to discuss LoRNASH, a foundation model that decodes how unspliced pre-mRNA sequences shape transcriptome architecture. 📄 Read the paper: ⏺️ Tune in live:
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In last week’s newsletter, we cover all things on proteins inspired by round 2 of @adaptyvbio's protein design competition, from one-shot design of protein binders, sequence-structure co-generation for protein design, and more! Read below👇.
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Next Monday at 12 pm ET, Ali Saberi will join LoGG host @HannesStaerk to discuss LoRNASH, a foundation model that decodes how unspliced pre-mRNA sequences shape transcriptome architecture. 📄 Read the paper: ⏺️ Tune in live:
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Next Monday at 12 pm ET, @cdomingoenrich will join LoGG host @HannesStaerk to discuss stochastic optimal control (SOC) and a new algorithm named Adjoint Matching. 📄 Read the paper: ⏺️ Tune in live:
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Next Monday at 12pm ET, @brekelmaniac will join LoGG host @HannesStaerk to discuss Sequential Monte Carlo (SMC) for probabilistic inference problems. 📄 Read the paper: ⏺️ Tune in live:
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On Monday at 12 pm ET, @lovrovrcek joined LoGG host @HannesStaerk to discuss his work on GNNome, a framework for path identification that enables training without relying on existing assembly strategies. đź“„Â Read the paper:
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