82 citations · 220 across the 7 of their papers we have counts for
7 papers
3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction
Jiaqi Guan, Wesley Wei Qian, Xingang Peng +3
Rich data and powerful machine learning models allow us to design drugs for a specific protein target \textit{in silico}. Recently, the inclusion of 3D structures during targeted d…
Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
Xingang Peng, Shitong Luo, Jiaqi Guan +3
Deep generative models have achieved tremendous success in designing novel drug molecules in recent years. A new thread of works have shown the great potential in advancing the spe…
Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning
Jiahan Li, Shitong Luo, Congyue Deng +5
By folding into particular 3D structures, proteins play a key role in living beings. To learn meaningful representation from a protein structure for downstream tasks, not only the…
Protein secondary structure prediction using deep convolutional neural fields
Sheng Wang, Jian Peng, Jianzhu Ma +1
Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, current…
iTreePack: Protein Complex Side-Chain Packing by Dual Decomposition
Jian Peng, Raghavendra Hosur, Bonnie Berger +1
Protein side-chain packing is a critical component in obtaining the 3D coordinates of a structure and drug discovery. Single-domain protein side-chain packing has been thoroughly s…
Statistical inference for template-based protein structure prediction
Jian Peng
Protein structure prediction is one of the most important problems in computational biology. The most successful computational approach, also called template-based modeling, identi…