no code implementations • 26 Nov 2024 • Jiahan Li, Tong Chen, Shitong Luo, Chaoran Cheng, Jiaqi Guan, Ruihan Guo, Sheng Wang, Ge Liu, Jian Peng, Jianzhu Ma
To address these challenges, we introduce PepHAR, a hot-spot-driven autoregressive generative model for designing peptides targeting specific proteins.
1 code implementation • 4 Oct 2024 • Wenhao Gao, Shitong Luo, Connor W. Coley
We demonstrate SynFormer's effectiveness in two key applications: (1) local chemical space exploration, where the model generates synthesizable analogs of a reference molecule, and (2) global chemical space exploration, where the model aims to identify optimal molecules according to a black-box property prediction oracle.
no code implementations • 1 Jul 2024 • Ruidong Wu, Ruihan Guo, Rui Wang, Shitong Luo, Yue Xu, Jiahan Li, Jianzhu Ma, Qiang Liu, Yunan Luo, Jian Peng
Despite the striking success of general protein folding models such as AlphaFold2(AF2, Jumper et al. (2021)), the accurate computational modeling of antibody-antigen complexes remains a challenging task.
1 code implementation • 2 Jun 2024 • Jiahan Li, Chaoran Cheng, Zuofan Wu, Ruihan Guo, Shitong Luo, Zhizhou Ren, Jian Peng, Jianzhu Ma
Peptides, short chains of amino acid residues, play a vital role in numerous biological processes by interacting with other target molecules, offering substantial potential in drug discovery.
3 code implementations • 15 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.
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.
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.
1 code implementation • 28 Jan 2022 • Jiahan Li, Shitong Luo, Congyue Deng, Chaoran Cheng, Jiaqi Guan, Leonidas Guibas, Jian Peng, Jianzhu Ma
In this work, we propose the Orientation-Aware Graph Neural Networks (OAGNNs) to better sense the geometric characteristics in protein structure (e. g. inner-residue torsion angles, inter-residue orientations).
no code implementations • NeurIPS 2021 • Shitong Luo, Chence Shi, Minkai Xu, Jian Tang
However, these non-bonded atoms may be proximal to each other in 3D space, and modeling their interactions is of crucial importance to accurately determine molecular conformations, especially for large molecules and multi-molecular complexes.
no code implementations • 3 Nov 2021 • Haolan Chen, Bi'an Du, Shitong Luo, Wei Hu
3D point clouds acquired by scanning real-world objects or scenes have found a wide range of applications including immersive telepresence, autonomous driving, surveillance, etc.
2 code implementations • ICCV 2021 • Shitong Luo, Wei Hu
Since $p * n$ is unknown at test-time, and we only need the score (i. e., the gradient of the log-probability function) to perform gradient ascent, we propose a neural network architecture to estimate the score of $p * n$ given only noisy point clouds as input.
1 code implementation • 15 May 2021 • Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gomez-Bombarelli, Jian Tang
Specifically, the molecular graph is first encoded in a latent space, and then the 3D structures are generated by solving a principled bilevel optimization program.
no code implementations • 11 May 2021 • Jiaxiang Wu, Shitong Luo, Tao Shen, Haidong Lan, Sheng Wang, Junzhou Huang
In this paper, we propose a fully-differentiable approach for protein structure optimization, guided by a data-driven generative network.
6 code implementations • 9 May 2021 • Chence Shi, Shitong Luo, Minkai Xu, Jian Tang
We study a fundamental problem in computational chemistry known as molecular conformation generation, trying to predict stable 3D structures from 2D molecular graphs.
3 code implementations • CVPR 2021 • Shitong Luo, Wei Hu
We present a probabilistic model for point cloud generation, which is fundamental for various 3D vision tasks such as shape completion, upsampling, synthesis and data augmentation.
3 code implementations • ICLR 2021 • Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, Jian Tang
Inspired by the recent progress in deep generative models, in this paper, we propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
no code implementations • 1 Jan 2021 • Shitong Luo, Wei Hu
Point cloud generation thus amounts to learning the reverse diffusion process that transforms the noise distribution to the distribution of a desired shape.
1 code implementation • 27 Jul 2020 • Shitong Luo, Wei Hu
Afterwards, the decoder infers the underlying manifold by transforming each sampled point along with the embedded feature of its neighborhood to a local surface centered around the point.