粟杨｜重庆科技学院｜学者主页

2026

MMRCL: An interpretable multi-modal deep learning framework for predicting hERG blockers

Yang Su, Jin-Zhou Wu, Ao Yang, Yumin Yuan, Wenli Du, Yi Xiang, Weifeng Shen

Comput. Biol. Chem.

DOI

2024

Insight to the prediction of CO2 solubility in ionic liquids based on the interpretable machine learning model

Ao Yang, Shirui Sun, Yang Su, Zong Yang Kong, Jingzheng Ren, Weifeng Shen

Chemical Engineering Science

DOI

2023

An improved multi-modal representation-learning model based on fusion networks for property prediction in drug discovery

Jinzhou Wu, Yang Su, Ao Yang, Jingzheng Ren, Yi Xiang

Computers in Biology and Medicine

DOI

2022

An accurate and interpretable deep learning model for environmental properties prediction using hybrid molecular representations

Jun Zhang, Qin Wang, Yang Su, Saimeng Jin, Jingzheng Ren, Mario R. Eden, Weifeng Shen

AIChE Journal

DOI

2021

Insights into ensemble learning-based data-driven model for safety-related property of chemical substances

Zihao Wang, Huaqiang Wen, Yang Su, Weifeng Shen, Jingzheng Ren, Yingjie Ma, Jie Li

Chemical Engineering Science

DOI

2021

A systematic modeling methodology of deep neural network‐based structure‐property relationship for rapid and reliable prediction on flashpoints

Huaqiang Wen, Yang Su, Zihao Wang, Saimeng Jin, Jingzheng Ren, Weifeng Shen, Mario R. Eden

AIChE Journal

DOI

2021

A multi-task deep learning neural network for predicting flammability-related properties from molecular structures

Ao Yang, Yang Su, Zihao Wang, Saimeng Jin, Jingzheng Ren, Xiangping Zhang, Weifeng Shen, James H. Clark

Green Chemistry

DOI

2021

A systematic DNN-based QSPR modeling methodology for rapid and reliable prediction on flashpoints of chemicals

Huaqiang Wen, Yang Su, Zi-Hao Wang, Saimeng Jin, Jing-Zheng Ren, Wei-Feng Shen, M. Eden

DOI

2021

Deep learning in QSPR modeling for the prediction of critical properties

Yang Su, Weifeng Shen

DOI

2020

基于人工智能的溶剂性质预测建模及风险评估研究

粟杨

《重庆大学》

2020

基于深度学习的溶剂定量构效关系建模研究进展

田璐瑶，王梓豪，粟杨，文华强，申威峰

《化工学报》

2020

A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties

Zi-Hao Wang, Yang Su, Saimeng Jin, Wei-Feng Shen, Jing-Zheng Ren, Xiang-Ping Zhang, J. Clark

DOI

2020

Optimal Design and Energy-Saving Investigation of the Triple CO2 Feeds for Methanol Production System by Combining Steam and Dry Methane Reforming

Hong Wang, Yang Su, Dan Wang, Saimeng Jin, Shun'an Wei, Wei-Feng Shen

DOI

2020

Understanding Activity Origin for Oxygen Reduction Reaction on Bi-Atom Catalysts by DFT Study and Machine-Learning

Chao-Fang Deng, Yang Su, Fu-Hua Li, Wei-Feng Shen, Zhongfang Chen, Qing Tang

DOI

2020

Optimization and control of energy saving side-stream extractive distillation with heat integration for separating ethyl acetate-ethanol azeotrope

Tao Shi, Wei Chun, Ao Yang, Yang Su, Saimeng Jin, Jing-Zheng Ren, Wei-Feng Shen

DOI

2020

Investigation of energy-efficient and sustainable reactive/pressure-swing distillation processes to recover tetrahydrofuran and ethanol from the industrial effluent

Ao Yang, Yang Su, Liumei Teng, Saimeng Jin, Teng Zhou, Wei-Feng Shen

Separation and Purification Technology

DOI

2020

Investigation on ternary system tetrahydrofuran/ethanol/water with three azeotropes separation via the combination of reactive and extractive distillation

Yang Su, Ao Yang, Saimeng Jin, Wei-Feng Shen, Pei-Zhe Cui, Jing-Zheng Ren

DOI

2019

Multi-objective optimization of organic Rankine cycle system for the waste heat recovery in the heat pump assisted reactive dividing wall column

Ao Yang, Yang Su, Wei-Feng Shen, I-Lung Chien, Jing-Zheng Ren

Energy Conversion and Management

DOI

2019

Investigation of an energy-saving double-thermally coupled extractive distillation for separating ternary system benzene/toluene/cyclohexane

Ao Yang, Yang Su, I-Lung Chien, Saimeng Jin, Chenglei Yan, Shun'an Wei, Wei-Feng Shen

Energy

DOI

2019

An efficient technique for improving methanol yield using dual CO2 feeds and dry methane reforming

Yang Su, Li-Ping Lu, Wei-Feng Shen, Shun'an Wei

Frontiers of Chemical Science and Engineering

DOI

2019

Systematic approach for screening organic and ionic liquid solvents in homogeneous extractive distillation exemplified by the tert-butanol dehydration

Yan-Jie Hu, Yang Su, Saimeng Jin, I-Lung Chien, Wei-Feng Shen

Separation and Purification Technology

DOI

2019

Predictive deep learning models for environmental properties: the direct calculation of octanol–water partition coefficients from molecular graphs

Zi-Hao Wang, Yang Su, Wei-Feng Shen, Saimeng Jin, J. Clark, Jing-Zheng Ren, Xiang-Ping Zhang

Green Chemistry

DOI

2019

An architecture of deep learning in QSPR modeling for the prediction of critical properties using molecular signatures

Yang Su, Zi-Hao Wang, Saimeng Jin, Wei-Feng Shen, Jing-Zheng Ren, M. Eden

AIChE Journal

DOI

2015

基于CAPE-OPEN的固定床模拟软件的设计与开发

周小波，粟杨

《低碳化学与化工》

粟杨

精选作品

期刊论文

MMRCL: An interpretable multi-modal deep learning framework for predicting hERG blockers

Insight to the prediction of CO2 solubility in ionic liquids based on the interpretable machine learning model

An improved multi-modal representation-learning model based on fusion networks for property prediction in drug discovery

An accurate and interpretable deep learning model for environmental properties prediction using hybrid molecular representations

Insights into ensemble learning-based data-driven model for safety-related property of chemical substances

A systematic modeling methodology of deep neural network‐based structure‐property relationship for rapid and reliable prediction on flashpoints

A multi-task deep learning neural network for predicting flammability-related properties from molecular structures

A systematic DNN-based QSPR modeling methodology for rapid and reliable prediction on flashpoints of chemicals

Deep learning in QSPR modeling for the prediction of critical properties

基于人工智能的溶剂性质预测建模及风险评估研究

基于深度学习的溶剂定量构效关系建模研究进展

A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties

Optimal Design and Energy-Saving Investigation of the Triple CO2 Feeds for Methanol Production System by Combining Steam and Dry Methane Reforming

Understanding Activity Origin for Oxygen Reduction Reaction on Bi-Atom Catalysts by DFT Study and Machine-Learning

Optimization and control of energy saving side-stream extractive distillation with heat integration for separating ethyl acetate-ethanol azeotrope

Investigation of energy-efficient and sustainable reactive/pressure-swing distillation processes to recover tetrahydrofuran and ethanol from the industrial effluent

Investigation on ternary system tetrahydrofuran/ethanol/water with three azeotropes separation via the combination of reactive and extractive distillation

Multi-objective optimization of organic Rankine cycle system for the waste heat recovery in the heat pump assisted reactive dividing wall column

Investigation of an energy-saving double-thermally coupled extractive distillation for separating ternary system benzene/toluene/cyclohexane

An efficient technique for improving methanol yield using dual CO2 feeds and dry methane reforming

Systematic approach for screening organic and ionic liquid solvents in homogeneous extractive distillation exemplified by the tert-butanol dehydration

Predictive deep learning models for environmental properties: the direct calculation of octanol–water partition coefficients from molecular graphs

An architecture of deep learning in QSPR modeling for the prediction of critical properties using molecular signatures

基于CAPE-OPEN的固定床模拟软件的设计与开发

预印本

Machine Learning-Driven Scaffold Hopping Identifies a Non-Classical GSK-3β Inhibitor with Potent Anti-Inflammatory Efficacy

研究方向