Search Results for author: Zhifeng Gao

Found 21 papers, 8 papers with code

Predicting Polymer Properties Based on Multimodal Multitask Pretraining

no code implementations7 Jun 2024 Fanmeng Wang, Wentao Guo, Minjie Cheng, Shen Yuan, Hongteng Xu, Zhifeng Gao

Subsequently, we further fine-tune the pretrained MMPolymer for downstream polymer property prediction tasks in the supervised learning paradigm.

Property Prediction

Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction

1 code implementation20 May 2024 Eric Alcaide, Zhifeng Gao, Guolin Ke, Yaqi Li, Linfeng Zhang, Hang Zheng, Gengmo Zhou

In recent years, machine learning (ML) methods have emerged as promising alternatives for molecular docking, offering the potential for high accuracy without incurring prohibitive computational costs.

Molecular Docking Pose Prediction

Uni-SMART: Universal Science Multimodal Analysis and Research Transformer

no code implementations15 Mar 2024 Hengxing Cai, Xiaochen Cai, Shuwen Yang, Jiankun Wang, Lin Yao, Zhifeng Gao, Junhan Chang, Sihang Li, Mingjun Xu, Changxin Wang, Hongshuai Wang, Yongge Li, Mujie Lin, Yaqi Li, Yuqi Yin, Linfeng Zhang, Guolin Ke

Scientific literature often includes a wide range of multimodal elements, such as molecular structure, tables, and charts, which are hard for text-focused LLMs to understand and analyze.

SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis

no code implementations4 Mar 2024 Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Changxin Wang, Zhifeng Gao, Hongshuai Wang, Yongge Li, Mujie Lin, Shuwen Yang, Jiankun Wang, Yuqi Yin, Yaqi Li, Linfeng Zhang, Guolin Ke

Recent breakthroughs in Large Language Models (LLMs) have revolutionized natural language understanding and generation, igniting a surge of interest in leveraging these technologies in the field of scientific literature analysis.

Benchmarking Memorization +1

End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction

no code implementations8 Jan 2024 Qingsi Lai, Lin Yao, Zhifeng Gao, Siyuan Liu, Hongshuai Wang, Shuqi Lu, Di He, LiWei Wang, Cheng Wang, Guolin Ke

XtalNet represents a significant advance in CSP, enabling the prediction of complex structures from PXRD data without the need for external databases or manual intervention.

Contrastive Learning Retrieval

Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction

no code implementations24 Apr 2023 Zhifeng Gao, Xiaohong Ji, Guojiang Zhao, Hongshuai Wang, Hang Zheng, Guolin Ke, Linfeng Zhang

Recently deep learning based quantitative structure-activity relationship (QSAR) models has shown surpassing performance than traditional methods for property prediction tasks in drug discovery.

Drug Discovery Model Selection +4

Do Deep Learning Methods Really Perform Better in Molecular Conformation Generation?

no code implementations14 Feb 2023 Gengmo Zhou, Zhifeng Gao, Zhewei Wei, Hang Zheng, Guolin Ke

However, to our surprise, we design a simple and cheap algorithm (parameter-free) based on the traditional methods and find it is comparable to or even outperforms deep learning based MCG methods in the widely used GEOM-QM9 and GEOM-Drugs benchmarks.

Drug Discovery

Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking?

no code implementations14 Feb 2023 Yuejiang Yu, Shuqi Lu, Zhifeng Gao, Hang Zheng, Guolin Ke

What's more, they claim to perform better than traditional molecular docking, but the approach of comparison is not fair, since traditional methods are not designed for docking on the whole protein without a given pocket.

Molecular Docking

Boosted ab initio Cryo-EM 3D Reconstruction with ACE-EM

no code implementations13 Feb 2023 Lin Yao, Ruihan Xu, Zhifeng Gao, Guolin Ke, Yuhang Wang

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 (poses).

3D Reconstruction

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

1 code implementation ChemRxiv 2022 Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, Guolin Ke

Uni-Mol is composed of two models with the same SE(3)-equivariant transformer architecture: a molecular pretraining model trained by 209M molecular conformations; a pocket pretraining model trained by 3M candidate protein pocket data.

3D Geometry Prediction molecular representation +3

Impact of pandemic fatigue on the spread of COVID-19: a mathematical modelling study

no code implementations9 Apr 2021 Disheng Tang, Wei Cao, Jiang Bian, Tie-Yan Liu, Zhifeng Gao, Shun Zheng, Jue Liu

We used a stochastic metapopulation model with a hierarchical structure and fitted the model to the positive cases in the US from the start of outbreak to the end of 2020.

PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning

3 code implementations17 Mar 2021 Yunbo Wang, Haixu Wu, Jianjin Zhang, Zhifeng Gao, Jianmin Wang, Philip S. Yu, Mingsheng Long

This paper models these structures by presenting PredRNN, a new recurrent network, in which a pair of memory cells are explicitly decoupled, operate in nearly independent transition manners, and finally form unified representations of the complex environment.

 Ranked #1 on Video Prediction on KTH (Cond metric)

Video Prediction Weather Forecasting

LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks

no code implementations1 Jan 2021 Yihan He, Wei Cao, Shun Zheng, Zhifeng Gao, Jiang Bian

In recent years, research communities have been developing stochastic sampling methods to handle large graphs when it is unreal to put the whole graph into a single batch.

Graph Representation Learning

Dynamic Graph Representation Learning with Fourier Temporal State Embedding

1 code implementation1 Jan 2021 Yihan He, Wei Cao, Shun Zheng, Zhifeng Gao, Jiang Bian

In this work, we present a new method named Fourier Temporal State Embedding (FTSE) to address the temporal information in dynamic graph representation learning.

Graph Embedding Graph Representation Learning

Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning

1 code implementation25 Nov 2020 Chao Du, Zhifeng Gao, Shuo Yuan, Lining Gao, Ziyan Li, Yifan Zeng, Xiaoqiang Zhu, Jian Xu, Kun Gai, Kuang-Chih Lee

In this paper, we propose a novel Deep Uncertainty-Aware Learning (DUAL) method to learn CTR models based on Gaussian processes, which can provide predictive uncertainty estimations while maintaining the flexibility of deep neural networks.

Click-Through Rate Prediction Gaussian Processes

Self-paced Ensemble for Highly Imbalanced Massive Data Classification

1 code implementation8 Sep 2019 Zhining Liu, Wei Cao, Zhifeng Gao, Jiang Bian, Hechang Chen, Yi Chang, Tie-Yan Liu

To tackle this problem, we conduct deep investigations into the nature of class imbalance, which reveals that not only the disproportion between classes, but also other difficulties embedded in the nature of data, especially, noises and class overlapping, prevent us from learning effective classifiers.

Classification General Classification +1

PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs

no code implementations NeurIPS 2017 Yunbo Wang, Mingsheng Long, Jian-Min Wang, Zhifeng Gao, Philip S. Yu

The core of this network is a new Spatiotemporal LSTM (ST-LSTM) unit that extracts and memorizes spatial and temporal representations simultaneously.

Video Prediction

Full-reference image quality assessment-based B-mode ultrasound image similarity measure

no code implementations10 Jan 2017 Kele Xu, Xi Liu, Hengxing Cai, Zhifeng Gao

During the last decades, the number of new full-reference image quality assessment algorithms has been increasing drastically.

Image Quality Assessment

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