首页|ArtiDock: fast and accurate machine learning approach to proteinligand docking based on multimodal data augmentation
ArtiDock: fast and accurate machine learning approach to proteinligand docking based on multimodal data augmentation
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from bi orxiv.org: "We present ArtiDock - the deep learning technique for predicting ligand poses i n the protein binding pockets (aka \"AI docking\ "), which is based on augmenting inherently limited training data with algorithm ically generated artificial binding pockets and the ensembles of representative conformations of the ligand-protein complexes obtained from MD simulations. Perf ormance of ArtiDock is compared systematically with other AI docking techniques and conventional docking programs on the PoseBusters dataset, which is dedicated for benchmarking the AI pose prediction algorithms. ArtiDock outperforms the be st AI docking techniques and the major conventional docking programs, being at l east an order of magnitude faster while providing superior accuracy in terms of RMSD and additional ligand pose correctness metrics. "The influence of data augmentation on the model performance is evaluated and th e perspectives of further development are discussed."
NETL
NSTL
