GraphML News (March 10th) - Protein Design Community Principles, RF All Atom weights, ICLR workshops

🤝 More than 100 prominent researchers in protein design, structural biology, and geometric deep learning committed to the principles of Responsible AI in Biodesign. Recognizing the increasing capabilities of deep learning models in designing functional biological molecules, the community came up with several core values and principles such as the benefit of society, safety and security, openness, equity, international collaboration, and responsibility. Particular commitments include more scrutiny towards hazardous biomolecules before their manufacturing, better evaluation and risk assessment of DL models. Good for the protein design community, let’s hope those would be practically implemented!

🧬 Committing to the newly introduced principles, Baker’s lab released RosettaFold All-Atom and RFDiffusion All-Atom together with their model weights and several inference examples. Folks on Twitter who interpret the principles as “closed-source AI taking over” are obviously wrong 😛

📚 ICLR 2024 workshops started posting accepted papers - so far we see the papers from AI 4 Differential Equations, Representational Alignment, and Time Series for Health. ICLR workshop papers are usually good proxies for ICML and NeurIPS submissions, so you might be interested to check those of your domain.

Weekend reading:

A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges by Wei Ju et al

Graph neural network outputs are almost surely asymptotically constant by Sam Adam-Day et al. feat. Ismail Ilkan Ceylan

Pairwise Alignment Improves Graph Domain Adaptation by Shikun Liu et al feat. Pan Li

Understanding Biology in the Age of Artificial Intelligence by Elsa Lawrence, Adham El-Shazly, Srijit Seal feat. our own Chaitanya K. Joshi