REVIEW 4 cited by
Torsional Diffusion for Molecular Conformer Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Molecular conformer generation is a fundamental task in computational chemistry. Several machine learning approaches have been developed, but none have outperformed state-of-the-art cheminformatics methods. We propose torsional diffusion, a novel diffusion framework that operates on the space of torsion angles via a diffusion process on the hypertorus and an extrinsic-to-intrinsic score model. On a standard benchmark of drug-like molecules, torsional diffusion generates superior conformer ensembles compared to machine learning and cheminformatics methods in terms of both RMSD and chemical properties, and is orders of magnitude faster than previous diffusion-based models. Moreover, our model provides exact likelihoods, which we employ to build the first generalizable Boltzmann generator. Code is available at https://github.com/gcorso/torsional-diffusion.
Forward citations
Cited by 4 Pith papers
-
FLOWR.root: A flow matching based foundation model for joint multi-purpose structure-aware 3D ligand generation and affinity prediction
A flow-matching model jointly generates pocket-aware 3D ligands and predicts their binding affinities, reporting state-of-the-art generation and competitive affinity accuracy with a speed advantage.
-
Feynman-Kac-Flow: Inference Steering of Conditional Flow Matching to an Energy-Tilted Posterior
Feynman-Kac particle steering, previously diffusion-only, is derived for conditional flow matching and used to generate chirality-correct chemical transition states.
-
Applications of Modular Co-Design for De Novo 3D Molecule Generation
A new transformer architecture with joint continuous and discrete denoising improves 3D molecule generation and moves generated structures closer to low-energy physical minima.
-
Graph Neural Networks in Modern AI-aided Drug Discovery
A comprehensive model-centric review of graph neural network methods and applications in AI-aided drug discovery, from molecular representation to synthesis planning.
Discussion (0). Continue with ORCID to comment.