GraphML News (June 14th) - Boltz-2, OpenBind, Musings on equivariance
Back to the normal schedule!
🧬 The biggest announcement of the week - MIT and Recursion released Boltz-2, perhaps the most successful open-source reproduction of AlphaFold 3. v2 brings binding affinity prediction (orders of magnitude faster than physics simulations), model improvements and inference speedups. The preprint also report an experiment combining Boltz with SynflowNet to generate binders for the TYK2 protein. Code and model weights are already available.
🇬🇧 UK announced the OpenBind initiative aiming to collect data for 500k protein-ligand complexes using X-ray crystallography and synchrotron facilities at Diamond Light Source. The academic side includes all the big names you’d expect - Charlotte Deane, Frank von Delft, David Baker - as well as the industrial part which include Isomorphic Labs, Roche, Boltz, and others. Let’s hope it will be the next PDB for protein design.
🌀 The need for equivariance continues to be a hot discussion topic - first, Chaitanya K. Joshi published a post reviewing two sides of the spectrum: low-data regimes where equivariance might help (by restricting model capacity) and high-data regimes (like generative modeling) where symmetries can be learned from data. Later on, Mark Neumann (Orbital Materials) published his take on the need for rotational equivariance and conservation of energy and how those can be achieved without strictly equivariant models (tricks like Equigrad, for instance). The post also features a handful of fresh papers on the topic - check them out too.
Weekend reading (or github repos, heh):
Automated Non-Hermitian Spectral Graph Construction and GnLTransformer - a cool application, takes in the characteristic polynomial of Hamiltonians of 1D-crystals and returns the spectral graph
Anomaly Detection with Graph Neural Networks (GNNs) - a PyG library and datasets for anomaly detection, thanks Federico Bello for the pointer
On Measuring Long-Range Interactions in Graph Neural Networks - proposes a metric to compute effective range of MPNNs and GTs and studies LRGB tasks as requiring long-range connections or not
Back to the normal schedule!
🧬 The biggest announcement of the week - MIT and Recursion released Boltz-2, perhaps the most successful open-source reproduction of AlphaFold 3. v2 brings binding affinity prediction (orders of magnitude faster than physics simulations), model improvements and inference speedups. The preprint also report an experiment combining Boltz with SynflowNet to generate binders for the TYK2 protein. Code and model weights are already available.
🇬🇧 UK announced the OpenBind initiative aiming to collect data for 500k protein-ligand complexes using X-ray crystallography and synchrotron facilities at Diamond Light Source. The academic side includes all the big names you’d expect - Charlotte Deane, Frank von Delft, David Baker - as well as the industrial part which include Isomorphic Labs, Roche, Boltz, and others. Let’s hope it will be the next PDB for protein design.
🌀 The need for equivariance continues to be a hot discussion topic - first, Chaitanya K. Joshi published a post reviewing two sides of the spectrum: low-data regimes where equivariance might help (by restricting model capacity) and high-data regimes (like generative modeling) where symmetries can be learned from data. Later on, Mark Neumann (Orbital Materials) published his take on the need for rotational equivariance and conservation of energy and how those can be achieved without strictly equivariant models (tricks like Equigrad, for instance). The post also features a handful of fresh papers on the topic - check them out too.
Weekend reading (or github repos, heh):
Automated Non-Hermitian Spectral Graph Construction and GnLTransformer - a cool application, takes in the characteristic polynomial of Hamiltonians of 1D-crystals and returns the spectral graph
Anomaly Detection with Graph Neural Networks (GNNs) - a PyG library and datasets for anomaly detection, thanks Federico Bello for the pointer
On Measuring Long-Range Interactions in Graph Neural Networks - proposes a metric to compute effective range of MPNNs and GTs and studies LRGB tasks as requiring long-range connections or not