1 code implementation • 26 Feb 2024 • Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu
Designing 3D ligands within a target binding site is a fundamental task in drug discovery.
no code implementations • 4 Feb 2024 • Haowei Lin, Baizhou Huang, Haotian Ye, Qinyu Chen, ZiHao Wang, Sujian Li, Jianzhu Ma, Xiaojun Wan, James Zou, Yitao Liang
The ever-growing ecosystem of LLMs has posed a challenge in selecting the most appropriate pre-trained model to fine-tune amidst a sea of options.
no code implementations • NeurIPS 2023 • Jing Gong, Minsheng Hao, Xingyi Cheng, Xin Zeng, Chiming Liu, Jianzhu Ma, Xuegong Zhang, Taifeng Wang, Le Song
Advances in high-throughput sequencing technology have led to significant progress in measuring gene expressions at the single-cell level.
1 code implementation • 12 Oct 2023 • Haowei Lin, ZiHao Wang, Jianzhu Ma, Yitao Liang
To pursue the goal of creating an open-ended agent in Minecraft, an open-ended game environment with unlimited possibilities, this paper introduces a task-centric framework named MCU for Minecraft agent evaluation.
1 code implementation • 12 Sep 2023 • Xingchao Liu, Xiwen Zhang, Jianzhu Ma, Jian Peng, Qiang Liu
Leveraging our new pipeline, we create, to the best of our knowledge, the first one-step diffusion-based text-to-image generator with SD-level image quality, achieving an FID (Frechet Inception Distance) of $23. 3$ on MS COCO 2017-5k, surpassing the previous state-of-the-art technique, progressive distillation, by a significant margin ($37. 2$ $\rightarrow$ $23. 3$ in FID).
1 code implementation • 11 May 2023 • Xingang Peng, Jiaqi Guan, Qiang Liu, Jianzhu Ma
Deep generative models have recently achieved superior performance in 3D molecule generation.
2 code implementations • 6 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}.
no code implementations • 12 Feb 2023 • Ruiyang Chen, Yingheng Tang, Jianzhu Ma, Weilu Gao
Diffractive optical neural networks (DONNs) have been emerging as a high-throughput and energy-efficient hardware platform to perform all-optical machine learning (ML) in machine vision systems.
1 code implementation • 20 Nov 2022 • Zhizhou Ren, Anji Liu, Yitao Liang, Jian Peng, Jianzhu Ma
To bridge this gap, we study the problem of few-shot adaptation in the context of human-in-the-loop reinforcement learning.
2 code implementations • 22 Oct 2022 • Yinan Huang, Xingang Peng, Jianzhu Ma, Muhan Zhang
To the best of our knowledge, it is the first linear-time GNN model that can count 6-cycles with theoretical guarantees.
1 code implementation • 15 May 2022 • Yinan Huang, Xingang Peng, Jianzhu Ma, Muhan Zhang
The main computational challenges include: 1) the generation of linkers is conditional on the two given molecules, in contrast to generating full molecules from scratch in previous works; 2) linkers heavily depend on the anchor atoms of the two molecules to be connected, which are not known beforehand; 3) 3D structures and orientations of the molecules need to be considered to avoid atom clashes, for which equivariance to E(3) group are necessary.
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.
no code implementations • 22 Mar 2022 • Fan Ding, Yijie Wang, Jianzhu Ma, Yexiang Xue
Here we propose XOR-PGD, a novel algorithm based on Projected Gradient Descent (PGD) coupled with the XOR sampler, which is guaranteed to solve the constrained stochastic convex optimization problem still in linear convergence rate by choosing proper step size.
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 • 5 Dec 2021 • Xuesong Wang, Zhihang Hu, Tingyang Yu, Ruijie Wang, Yumeng Wei, Juan Shu, Jianzhu Ma, Yu Li
Our approach can efficiently map the above data with high sparsity and noise from different spaces to a low-dimensional manifold in a unified space, making the downstream alignment and integration straightforward.
no code implementations • NeurIPS 2021 • Andersen Ang, Jianzhu Ma, Nianjun Liu, Kun Huang, Yijie Wang
We demonstrate that the proposed algorithm can produce a solution of the projection problem with high precision on large scale datasets, and the algorithm is able to significantly outperform the state-of-the-art methods in terms of runtime (about 6-8 times faster than a commercial software with respect to CPU time for input vector with 1 million variables or more).
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.
1 code implementation • 11 Jun 2021 • Susheel Suresh, Vinith Budde, Jennifer Neville, Pan Li, Jianzhu Ma
We find that the prediction performance of a wide range of GNN models is highly correlated with the node level assortativity.
Graph Learning Node Classification on Non-Homophilic (Heterophilic) Graphs
2 code implementations • 10 Jun 2021 • Yunyu Liu, Jianzhu Ma, Pan Li
HIT extracts the structural representation of a node triplet of interest on the temporal hypergraph and uses it to tell what type of, when, and why the interaction expansion could happen in this triplet.
1 code implementation • 1 Jan 2021 • Jiawei Xue, Nan Jiang, Senwei Liang, Qiyuan Pang, Takahiro Yabe, Satish V. Ukkusuri, Jianzhu Ma
We apply the method to 11, 790 urban road networks across 30 cities worldwide to measure the spatial homogeneity of road networks within each city and across different cities.
1 code implementation • 2 Dec 2015 • Sheng Wang, Jian Peng, Jianzhu Ma, Jinbo Xu
Protein secondary structure (SS) prediction is important for studying protein structure and function.
Ranked #1 on Protein Secondary Structure Prediction on CullPDB
no code implementations • 19 Oct 2015 • Jianzhu Ma
In this thesis I introduce a new machine learning based method to predict the protein structure.
no code implementations • 12 Jan 2014 • Jianzhu Ma, Sheng Wang, Zhiyong Wang, Jinbo Xu
A sequence profile is usually represented as a position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection.
no code implementations • 10 Dec 2013 • Jianzhu Ma, Sheng Wang, Zhiyong Wang, Jinbo Xu
To further improve the accuracy of the estimated precision matrices, we employ a supervised learning method to predict contact probability from a variety of evolutionary and non-evolutionary information and then incorporate the predicted probability as prior into our GGL framework.