23 reposts on BlueSky vs 5 here. Just saying…😉

A well anticipated overview highlighting the first CACHE challenge, which brought together 23 research teams from 10 countries to collectively predict 1,955 compounds targeting LRRK2-a protein linked to #ParkinsonsDisease. https://t.co/5P4SAMcmOy

Save the date for the second annual Conscience Symposium on Open Drug Discovery! Join us in Montreal on April 7-8, 2025, for two days dedicated to advancing open and AI-driven drug discovery. Learn more: https://t.co/JLBekkFFMG

A new Science study presents “Evo”—a machine learning model capable of decoding and designing DNA, RNA, and protein sequences, from molecular to genome scale, with unparalleled accuracy. Evo’s ability to predict, generate, and engineer entire genomic sequences could change the

#AlphaFold3 is open-source for academic use now! See what our partner, @MoAlQuraishi, has to say about our upcoming OpenFold3 project and commercial availability. #BioAI #OpenSource #OpenFold https://t.co/Ln3nBHIlFr

Excited to share that the AlphaFold 3 model code and weights are now available for academic use. Looking forward to seeing what new research this unlocks and how the research community builds on AlphaFold 3 for scientific discoveries https://t.co/GKIOGHm317 1/2

Critical Assessment of Protein Engineering (CAPE): A Student Challenge on the Cloud @ACSSynBio 1. The CAPE challenge is a groundbreaking student competition designed to advance protein engineering through a cloud-based, iterative design-build-test-learn (DBTL) cycle. Inspired by

Ab initio characterization of protein molecular dynamics with AI2BMD @Nature @MSFTResearch 1. Introducing AI2BMD: This new AI-powered biomolecular dynamics system allows for ab initio accuracy in protein simulations, greatly enhancing the exploration of protein conformations

All participants to CACHE challenges are invited to join the overview paper. https://t.co/mvPwayM8kD CACHE#1 learnings: -Workflows were diverse. -Most top methods included some ML. -Hits were weak: lots of room for improvement. @thesgconline @consciencemeds #compchem

Does machine learning work on DEL datasets? Indeed it does. Sometimes. https://t.co/og9sbu7PPC

For SPR data aficionados, all data and sensorgrams generated at @thesgconline for thousands of CACHE #1 and CACHE #2 compounds are now available at https://t.co/FNMrblpR7m CACHE #3 data will be added next month... @consciencemeds #compchem #CACHE

Practically significant method comparison protocols for machine learning in small molecule drug discovery. #machinelearning #compchem

If you like total synthesis and Wordle, check out Synthordle, a retrosynthetic analysis game created by Shenvi Lab associates Kevin Zong (GS 2) and Jonah Luo (HS sophomore!). https://t.co/zWsXtQqSya Coming to @scrippsresearch Chemical Synthesis class in Winter ’25.

Smart data factory: Volunteer computing platform for active learning-driven molecular data acquisition • The Smart Data Factory (SDF) introduces a volunteer computing platform that harnesses the power of personal computers worldwide to accelerate quantum chemistry (DFT)

🎙️ The Target 2035 podcast series is officially LIVE! In our pilot episode, meet our hosts - @MilkaKostic, @LabScribbles and Opher Gileadi who share their personal motivations for joining the Target 2035 initiative. Tune in to the pilot episode now! https://t.co/HVvdc6mLkb 🎧

We've released a new version (v0.2.0) of the chai-lab Python package that makes it significantly easier to pass Multiple Sequence Alignments (MSAs) to the Chai-1 model. When provided, MSAs can improve the accuracy of predicted structures. https://t.co/EFz6edaTpf

There is a lot of interest in applying ML to DEL screening data. Here’s our open model applied to a real-world dataset, that successfully predicted new single digit uM binders from @EnamineLtd, and that is automated (no chemist decided what to purchase). https://t.co/DbWVxCPQJj

Enabling Open Machine Learning of DNA-Encoded Library Selections to Accelerate the Discovery of Small Molecule Protein Binders • This study presents a fully open framework for DNA-encoded library (DEL) screening, integrated with machine learning (ML) to discover small molecule

Chai-1: Impressive results in our hands. https://t.co/Ek2RWimZTn #compchem

The future of machine learning for small-molecule drug discovery will be driven by data | @NatComputSci Perspectives - "we propose that a greater focus on the data for training and benchmarking these models is more likely to drive future improvement, and explore avenues for