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1,726 claims from 1,063 papers are on the record. 46 have been checked so far; the other 1,680 have no check with a result yet.

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Status: Unchecked ×Topic: Computational Drug Discovery Methods ×Clear all

5 claims from 3 papers

1. Computer Science › Computational Drug Discovery Methods

   Chai-1: Decoding the molecular interactions of life

   Discovery, Boitreaud, Dent et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2024

   The paper introduces Chai-1, a multi-modal foundation model for molecular structure prediction that it reports performs at state-of-the-art across drug-discovery tasks, and releases it for use.

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   1. UncheckedThe paper states that Chai-1 can run without multiple sequence alignments (single-sequence mode) and keep most of its performance.“Chai-1 can also be run in single-sequence mode with-out MSAs while preserving most of its performance.”

2. Computer Science › Computational Drug Discovery Methods

   AlphaFold2 structures guide prospective ligand discovery

   Lyu, Kapolka, Gumpper et al. · Science · 2024

   Docking large libraries against unrefined AlphaFold2 models of two receptors found new ligands as well as docking against experimental structures, extending structure-based drug design.

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   1. UncheckedDocking against AlphaFold2 models and experimental structures gave similarly high hit rates and similar binding affinities for two receptors.“Hit rates were high and similar for the experimental and AF2 structures, as were affinities.”
   2. UncheckedA cryo-EM structure of a potent 5-HT2A ligand found by AlphaFold2 docking showed residue adjustments resembling the AlphaFold2 prediction.“Determination of the cryo–electron microscopy structure for one of the more potent 5-HT2A ligands from the AF2 docking revealed residue accommodations that resembled the AF2 prediction.”

3. Computer Science › Computational Drug Discovery Methods

   Efficient generation of protein pockets with PocketGen

   Zhang, Shen, Liu and Žitnik · Nature Machine Intelligence · 2024

   The authors introduce PocketGen, a deep generative model that designs the ligand-binding region of a protein, including its residue sequence and atomic structure, using a graph transformer and a protein language model.

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   1. UncheckedPocketGen is reported to run ten times faster than physics-based methods, with 97% of its generated pockets binding a ligand more strongly than reference pockets.“It operates ten times faster than physics-based methods and achieves a 97% success rate, defined as the percentage of generated pockets with higher binding affinity than reference pockets.”
   2. UncheckedPocketGen, a model that designs ligand-binding protein pockets, is reported to recover more than 63% of amino acids when compared with reference pockets.“Additionally, it attains an amino acid recovery rate exceeding 63%.”