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Found 6 papers, 5 papers with code
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
In these graphs, the geometric attributes transform according to the inherent physical symmetries of 3D atomic systems, including rotations and translations in Euclidean space, as well as node permutations.
Evaluating Zero-Shot Scoring for In Vitro Antibody Binding Prediction with Experimental Validation
The success of therapeutic antibodies relies on their ability to selectively bind antigens.
DiffHopp: A Graph Diffusion Model for Novel Drug Design via Scaffold Hopping
Scaffold hopping is a drug discovery strategy to generate new chemical entities by modifying the core structure, the \emph{scaffold}, of a known active compound.
Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models?
Deep generative models for structure-based drug design (SBDD), where molecule generation is conditioned on a 3D protein pocket, have received considerable interest in recent years.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
On the Expressive Power of Geometric Graph Neural Networks
The expressive power of Graph Neural Networks (GNNs) has been studied extensively through the Weisfeiler-Leman (WL) graph isomorphism test.
