Search Results for author: Jiaqi Guan

Found 10 papers, 6 papers with code

A 3D Generative Model for Structure-Based Drug Design

3 code implementations NeurIPS 2021 Shitong Luo, Jiaqi Guan, Jianzhu Ma, Jian Peng

In this paper, we propose a 3D generative model that generates molecules given a designated 3D protein binding site.

valid

Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets

3 code implementations15 May 2022 Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, Jianzhu Ma

Deep generative models have achieved tremendous success in designing novel drug molecules in recent years.

Specificity

3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction

2 code implementations6 Mar 2023 Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, Jianzhu Ma

Rich data and powerful machine learning models allow us to design drugs for a specific protein target \textit{in silico}.

Equivariant Point Cloud Analysis via Learning Orientations for Message Passing

1 code implementation CVPR 2022 Shitong Luo, Jiahan Li, Jiaqi Guan, Yufeng Su, Chaoran Cheng, Jian Peng, Jianzhu Ma

In this work, we propose a novel and simple framework to achieve equivariance for point cloud analysis based on the message passing (graph neural network) scheme.

Energy-efficient Amortized Inference with Cascaded Deep Classifiers

no code implementations10 Oct 2017 Jiaqi Guan, Yang Liu, Qiang Liu, Jian Peng

Deep neural networks have been remarkable successful in various AI tasks but often cast high computation and energy cost for energy-constrained applications such as mobile sensing.

Image Classification

Generative Hybrid Representations for Activity Forecasting with No-Regret Learning

no code implementations CVPR 2020 Jiaqi Guan, Ye Yuan, Kris M. Kitani, Nicholas Rhinehart

Automatically reasoning about future human behaviors is a difficult problem but has significant practical applications to assistive systems.

Neural Energy Minimization for Molecular Conformation Optimization

no code implementations ICLR 2022 Jiaqi Guan, Wesley Wei Qian, Qiang Liu, Wei-Ying Ma, Jianzhu Ma, Jian Peng

Assuming different forms of the underlying potential energy function, we can not only reinterpret and unify many of the existing models but also derive new variants of SE(3)-equivariant neural networks in a principled manner.

Cannot find the paper you are looking for? You can Submit a new open access paper.