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We are very excited to have Alán Aspuru-Guzik join us for a special edition of M2D2 to discuss the growing field of self-driving labs for chemistry, materials science, and biotech. Tune in live this Thursday, May 30th from 11am - 12pm ET: https://lnkd.in/gaYs7nqy
Today, at the ICML workshops, Prudencio Tossou shared some details on our QM data generation efforts and Emmanuel Bengio discussed the latest on GFlowNets. Come chat with us and learn more about our open roles in London. We're hiring! https://lnkd.in/ga2PzxJk
We are excited to play a role in supporting the development of more impactful machine learning methods for real-world impact in drug discovery. Get started with Polaris - Benchmarks for methods that matter today. Explore the existing datasets and benchmarks: https://polarishub.io/
🚀 Today, we’re excited to launch Polaris! 🚀 Polaris is a platform where the ML community working on drug discovery problems can easily share and access datasets and benchmarks. Get started with just a few lines of code: https://polarishub.io/ Benchmarking in computational drug discovery is already complicated because we don’t have standardized datasets, clear guidelines, or tools for method evaluation and comparison. It also doesn’t help that datasets and benchmarks are also scattered throughout the literature. We believe the first step towards improving the state of benchmarking is by providing a single source of truth for the community to easily access and compare methods. That’s what Polaris aims to be. ⭐ Whether you’re working with small molecules, phenomics, proteins, or something else, we’re here for you. We aim to support all file types and sizes with simple, fast data access. We’ve also put together a steering committee of experts across industry and academia (AstraZeneca, Relay Therapeutics, Pfizer, Merck, Nimbus Therapeutics, Blueprint Medicines, Johnson & Johnson, Valence Labs, and Mila - Quebec Artificial Intelligence Institute) to propose guidelines around dataset curation, method evaluation, and comparison - starting with a focus on small molecules. This is just the beginning of more industry-academia collaborations. Stay tuned!
Last year, we announced the launch of Valence Labs at ICML. Next week, we’re excited to be back at ICML to share some of our research! We’ll also be at the Polaris - Benchmarks for methods that matter launch event - come and meet the team. RSVP here: https://lu.ma/wj1agv8o See below for a summary of our presentations. 👇 1️⃣ Valence researcher Prudencio Tossou will be discussing the power of past and future QM data generation efforts at the ML4MS workshop on Friday, July 26th at 9:50 AM. We’ll be announcing the release of a new dataset package, stay tuned! Find the full schedule on the ML4MS website: https://ml4lms.bio/work/ 2️⃣ “Graph Positional and Structural Encoder” Where: Poster Session 1 - Hall C 4-9 #701 When: Tuesday, July 23rd from 11:30 - 1:00pm Paper: https://lnkd.in/efhBCiXH Semih Cantürk Dominique Beaini 3️⃣ “Learning to Scale Logits for Temperature-Conditional GFlowNets” Where: Poster Session 3 - Hall C 4-9 #1411 When: Wednesday, July 24th from 11:30am - 1:00pm Paper: https://lnkd.in/ekDVhV47 Emmanuel Bengio
Conducting research with real-world impact has always been at the heart of our mission. We are proud to support Polaris - Benchmarks for methods that matters, a benchmarking platform aiming to foster the development of impactful ML methods in drug discovery. RSVP to the launch at ICML: https://lu.ma/wj1agv8o
Today's benchmarking landscape for ML in drug discovery is complicated. We want to help the community develop methods that matter!🌟 Join us for our launch party at ICML on July 25th to learn more: https://lu.ma/wj1agv8o
May 16th is the new date for our TechBio Meetup at Stanford University with the Stanford Biotechnology Group. Join us on Thursday starting at 6 pm PT. Berton Earnshaw will be there to give an overview of Valence Labs and our research directions. We’ll also hear from Eric Nguyen on Evo, a long-context biological foundation model. There are only a few spots left, RSVP here: https://lnkd.in/gXDD-T-a
Can GNNs scale? This question has divided the community for years. For the first time, we observe that GNNs benefit tremendously from the increasing width, number of molecules, number of labels, and diversity in the pretraining datasets. With these findings, we introduce MolGPS - a 1B parameter model for molecular property prediction. Read the blog here: https://lnkd.in/gXymSrv9 Learn more about MolGPS at the DMLR Workshop at ICLR 2024 on Saturday, May 11th with Valence Labs scientists Maciej Sypetkowski, Frederik Wenkel, and Dominique Beaini. For more details, read the full paper “On the Scalability of GNNs for Molecular Graphs": https://lnkd.in/gFQ-C8-t
Supporting new generic drug development and generic in-licensing opportunities with focus on quality and regulatory compliance
1moWow...amazing achievements within the autonomous chemistry research. The future looks interesting.