most citedDo Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking?

23 citations · 44 across the 5 of their papers we have counts for

collaborators

5 papers

astro-ph.SR2024

Physical Parameters of 11,100 Short-Period ASAS-SN Eclipsing Contact Binaries

Xu-Zhi Li, Qing-Feng Zhu, Xu Ding +4

Starting from more than 11,200 short-period (less than 0.5 days) EW-type eclipsing binary candidates with the All-Sky Automated Survey for Supernovae (ASAS-SN) V-band light curves,…

q-bio.BM202313 cited

Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction

Zhifeng Gao, Xiaohong Ji, Guojiang Zhao +4

Recently deep learning based quantitative structure-activity relationship (QSAR) models has shown surpassing performance than traditional methods for property prediction tasks in d…

cs.CE20236 cited

Do Deep Learning Methods Really Perform Better in Molecular Conformation Generation?

Gengmo Zhou, Zhifeng Gao, Zhewei Wei +2

Molecular conformation generation (MCG) is a fundamental and important problem in drug discovery. Many traditional methods have been developed to solve the MCG problem, such as sys…

q-bio.BM202323 cited

Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking?

Yuejiang Yu, Shuqi Lu, Zhifeng Gao +2

Molecular docking, given a ligand molecule and a ligand binding site (called ``pocket'') on a protein, predicting the binding mode of the protein-ligand complex, is a widely used t…

q-bio.BM20232 cited

3D Molecular Generation via Virtual Dynamics

Shuqi Lu, Lin Yao, Xi Chen +3

Structure-based drug design, i.e., finding molecules with high affinities to the target protein pocket, is one of the most critical tasks in drug discovery. Traditional solutions,…