A new diagnostic reveals that L=2 equivariant force field backbones preserve frequencies up to l=4 but collapse at l=5 on aspirin, consistent with a finite-degree span theorem and controls.
Boltz-1: Democratizing biomolecular interaction modeling.bioRxiv, 2024
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Sesame introduces spatial density-map conditioning and a pairformer module in a diffusion framework to enable de novo and scaffold-conditioned molecular generation for drug design.
HADES is an agentic AI system that generates mechanistic hypotheses for drug-induced liver injury using molecular, metabolite, and pathway evidence, outperforming prior binary classifiers on the new DILER benchmark while establishing a baseline for hypothesis alignment.
PLM embeddings improve antibody monomer CDR-H3 accuracy but fail on complexes without co-evolution signals, while MSA refinement and convergence-aware recycling yield gains over AlphaFold3 on held-out antibody-antigen data without retraining.
Boltz-2 and fine-tuned DrugFormDTA lead ML-based binding prediction while GNINA leads docking tools on a cleaned antiviral dataset, with performance varying by viral protein.
citing papers explorer
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Diagnosing Spectral Ceilings in Equivariant Neural Force Fields
A new diagnostic reveals that L=2 equivariant force field backbones preserve frequencies up to l=4 but collapse at l=5 on aspirin, consistent with a finite-degree span theorem and controls.
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Sesame: Structure-Aware Molecular Generation via Spatial Density-Map Conditioning
Sesame introduces spatial density-map conditioning and a pairformer module in a diffusion framework to enable de novo and scaffold-conditioned molecular generation for drug design.
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An explainable hypothesis-driven approach to Drug-Induced Liver Injury with HADES
HADES is an agentic AI system that generates mechanistic hypotheses for drug-induced liver injury using molecular, metabolite, and pathway evidence, outperforming prior binary classifiers on the new DILER benchmark while establishing a baseline for hypothesis alignment.
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Computational Modeling of Antibody-Antigen Complexes: PLM-Based and MSA-Based Approaches
PLM embeddings improve antibody monomer CDR-H3 accuracy but fail on complexes without co-evolution signals, while MSA refinement and convergence-aware recycling yield gains over AlphaFold3 on held-out antibody-antigen data without retraining.