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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…
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…
Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+
Shuqi Lu, Zhifeng Gao, Di He +2
Recent developments in deep learning have made remarkable progress in speeding up the prediction of quantum chemical (QC) properties by removing the need for expensive electronic s…
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…
Boosted ab initio Cryo-EM 3D Reconstruction with ACE-EM
Lin Yao, Ruihan Xu, Zhifeng Gao +2
The central problem in cryo-electron microscopy (cryo-EM) is to recover the 3D structure from noisy 2D projection images which requires estimating the missing projection angles (po…
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,…