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学者姓名：孙少瑞
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摘要 ：
Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein-ligand interaction prediction has demonstrated significant potential. In this paper, computational methods, focusing on sequence and structure to study protein-ligand interactions, are examined. Therefore, this paper starts by presenting an overview of the data sets applied in this area, as well as the various approaches applied for representing proteins and ligands. Then, sequence-based and structure-based classification criteria are subsequently utilized to categorize and summarize both the classical machine learning models and deep learning models employed in protein-ligand interaction studies. Moreover, the evaluation methods and interpretability of these models are proposed. Furthermore, delving into the diverse applications of protein-ligand interaction models in drug research is presented. Lastly, the current challenges and future directions in this field are addressed.
关键词 ：
Sequence andstructure Sequence andstructure Drug discovery Drug discovery Feature engineering Feature engineering Artificialintelligence Artificialintelligence Deep learning Deep learning Protein-ligand binding affinity Protein-ligand binding affinity Machine learning Machine learning Protein-ligand interaction Protein-ligand interaction
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| GB/T 7714 | Zhang, Yunjiang , Li, Shuyuan , Meng, Kong et al. Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction [J]. | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2024 , 64 (5) : 1456-1472 . |
| MLA | Zhang, Yunjiang et al. "Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction" . | JOURNAL OF CHEMICAL INFORMATION AND MODELING 64 . 5 (2024) : 1456-1472 . |
| APA | Zhang, Yunjiang , Li, Shuyuan , Meng, Kong , Sun, Shaorui . Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction . | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2024 , 64 (5) , 1456-1472 . |
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摘要 ：
Methanol steam reforming faces a significant challenge due to CO formation, which can lead to poisoning of fuel cell electrodes. This study introduces a series of Pd/ZnO catalysts prepared by ethylene glycol reduction, exhibiting remarkable selectivity in methanol steam reforming (MSR). The majority of palladium species exist in the form of PdZn alloy, attributed to the dual reduction process. This process, along with the generation of zinc vacancies and oxygen vacancies, enhances the interaction between the metal-carrier, promoting the formation of PdZn alloy, significantly improving CO2 selectivity and catalytic activity. Even at a high temperature of 400 degrees C, the active phase remains stable. After 5 hours of MSR, the 3% Pd/ZnO-300 H-2 catalyst achieves a hydrogen production rate of 1628.0 mmolg(cat)(-1)h(-1), with methanol conversion stabilized at 94%, CO2 selectivity reaching 97.7%, and CO content as low as 0.5%. These results outperform recent studies on hydrogen production. Furthermore, DFT calculations elucidate the complete reaction pathway (111) on PdZn. This study provides initial insights into the influence of metal-carrier interaction on the formation of PdZn alloy, suggesting a new direction for palladium-based catalysts in methanol steam reforming.
关键词 ：
Strong metal-support interaction Strong metal-support interaction PdZn alloys PdZn alloys Methanol steam reforming Methanol steam reforming Hydrogen production Hydrogen production Ethylene glycol reduction Ethylene glycol reduction
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| GB/T 7714 | Wang, Huimin , Fang, Zhaolin , Wang, Yaxin et al. The study of strong metal-support interaction enhanced PdZn alloy nanocatalysts for methanol steam reforming [J]. | JOURNAL OF ALLOYS AND COMPOUNDS , 2024 , 986 . |
| MLA | Wang, Huimin et al. "The study of strong metal-support interaction enhanced PdZn alloy nanocatalysts for methanol steam reforming" . | JOURNAL OF ALLOYS AND COMPOUNDS 986 (2024) . |
| APA | Wang, Huimin , Fang, Zhaolin , Wang, Yaxin , Meng, Kong , Sun, Shaorui . The study of strong metal-support interaction enhanced PdZn alloy nanocatalysts for methanol steam reforming . | JOURNAL OF ALLOYS AND COMPOUNDS , 2024 , 986 . |
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摘要 ：
While deep learning has been used in battery computing to speed up the search for new cathode materials, the majority of deep learning techniques only take into account elemental information and topological information, ignoring the significance of geometrical information and global information for electrode average voltage prediction. Multivalent metal-ion Battery Voltage Graph Neural Network (MBVGNN) proposed in present work, which combines global information and geometric information. MBVGNN achieves 43.98 % improvement over reaction-based GATGNN and 47.06 % over TL-CGCNN for the sodium-ion battery dataset. Compared with the best available models, the prediction results for Ca, Zn, Al and Mg electrode materials were improved by 15.50 %, 28.09 %, 44.74 % and 18 %. The formation energy of the cathode material was predicted by MBVGNN, and R2 was 0.969, which was used to evaluate the thermodynamic stability of the cathode material. In addition, density functional theory (DFT) was used to calculate 222 kinds of nickel base sodium-ion battery cathode material's average voltage for high-nickel ternary sodium-ion batteries, and the results were similar with the predicted results. Screening resulted in 194 high-energy-density ternary sodium-ion battery cathode materials. Furthermore, MBVGNN can reliably predict the average voltage of fluorine-substituted layered oxide cathode materials, which may be used to guide the experimental synthesis of these materials. Based on the accuracy of MBVGNN, this research constructs 74,553 high-entropy cathode materials that include layered oxides and three types of poly anions. Through the analysis of the knowledge relationship obtained from the prediction, reference guidance for 16 material types, prioritized elements and 1,369,658 element combinations for the experimental preparation of sodium-ion battery cathode materials is provided. This research employs machine learning methods to demonstrate that the current preparation of high-voltage, high-energy-density cathode materials remains necessary through multiple doping strategies.
关键词 ：
Sodium-ion batteries Sodium-ion batteries Cathode materials Cathode materials Graph neural network Graph neural network High-entropy High-entropy Density functional theory Density functional theory
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| GB/T 7714 | Meng, Kong , Bai, Kun , Sun, Shaorui . Artificial intelligence driven design of cathode materials for sodium-ion batteries using graph deep learning method [J]. | JOURNAL OF ENERGY STORAGE , 2024 , 101 . |
| MLA | Meng, Kong et al. "Artificial intelligence driven design of cathode materials for sodium-ion batteries using graph deep learning method" . | JOURNAL OF ENERGY STORAGE 101 (2024) . |
| APA | Meng, Kong , Bai, Kun , Sun, Shaorui . Artificial intelligence driven design of cathode materials for sodium-ion batteries using graph deep learning method . | JOURNAL OF ENERGY STORAGE , 2024 , 101 . |
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摘要 ：
The oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER) are crucial for the conversion of clean energy. Recently, dual-metal-site catalysts (DMSCs) have gained much attention due to their high atom utilization, stronger stability, and better catalytic performance. An advanced method that combines density functional theory (DFT) and machine learning (ML) has been employed in this study to investigate the adsorption free energies of adsorbates on hundreds of potential catalysts, with the aim of screening for catalysts that are highly active for the ORR and OER. The result of this study is that 30 DMSCs with ORR activity superior to Pt, 10 DMSCs with OER activity superior to RuO2, and 4 bifunctional catalysts for the OER and ORR are identified. This work provides guidance for the rational selection of metals on DMSCs to prepare catalysts with a high electrocatalytic performance for renewable energy applications.
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| GB/T 7714 | Fang, Zhaolin , Li, Shuyuan , Zhang, Yunjiang et al. The DFT and Machine Learning Method Accelerated the Discovery of DMSCs with High ORR and OER Catalytic Activities [J]. | JOURNAL OF PHYSICAL CHEMISTRY LETTERS , 2024 , 15 (1) : 281-289 . |
| MLA | Fang, Zhaolin et al. "The DFT and Machine Learning Method Accelerated the Discovery of DMSCs with High ORR and OER Catalytic Activities" . | JOURNAL OF PHYSICAL CHEMISTRY LETTERS 15 . 1 (2024) : 281-289 . |
| APA | Fang, Zhaolin , Li, Shuyuan , Zhang, Yunjiang , Wang, Yaxin , Meng, Kong , Huang, Chenyu et al. The DFT and Machine Learning Method Accelerated the Discovery of DMSCs with High ORR and OER Catalytic Activities . | JOURNAL OF PHYSICAL CHEMISTRY LETTERS , 2024 , 15 (1) , 281-289 . |
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摘要 ：
本发明提供了一种用于甲醇蒸汽重整制氢的Pd/ZnO‑ZrO2催化剂及其制备方法和应用。催化剂以低负载Pd作为活性组分，以ZnO和ZrO2的复合物作为载体，采用共沉淀法合成ZnO和ZrO2的复合载体，再使用湿法浸渍负载Pd。在甲醇蒸汽重整反应中，该种催化剂表现出高活性和高稳定性，尤其是0.1Pd/ZnO‑ZrO2(Pd含量：0.1％)表现出最佳活性，在反应5h后，甲醇转化率达到93.3％，产氢量高达1146.8mol·gPd‑1·h‑1，CO2选择性达到95.7％，CH4几乎为零，并且在5h内保持良好的稳定性，产氢活性几乎未下降。该催化剂贵金属含量低，Pd以单原子形式存在，比常规Pd基催化剂低了30倍，使成本大大降低。
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| GB/T 7714 | 孙少瑞 , 王慧敏 , 王亚鑫 . 一种用于甲醇蒸汽重整制氢的Pd/ZnO-ZrO2催化剂及其制备方法和应用 : CN202310299567.2[P]. | 2023-03-24 . |
| MLA | 孙少瑞 et al. "一种用于甲醇蒸汽重整制氢的Pd/ZnO-ZrO2催化剂及其制备方法和应用" : CN202310299567.2. | 2023-03-24 . |
| APA | 孙少瑞 , 王慧敏 , 王亚鑫 . 一种用于甲醇蒸汽重整制氢的Pd/ZnO-ZrO2催化剂及其制备方法和应用 : CN202310299567.2. | 2023-03-24 . |
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摘要 ：
In drug design, the design and manufacture of safe and effective compounds is a long-term, complex, and complicated process. Therefore, developing a new rapid and generalizable drug design method is of great value. This study aimed to propose a general model based on reinforcement learning combined with drug-target interaction, which could be used to design new molecules according to different protein targets. The method adopted recurrent neural network molecular modeling and took the drug-target affinity model as the reward function of optimal molecular generation. It did not need to know the three-dimensional structure and active sites of protein targets but only required the information of a one-dimensional amino acid sequence. This approach was demonstrated to produce drugs highly similar to marketed drugs and design molecules with a better binding energy.
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| GB/T 7714 | Zhang, Yunjiang , Li, Shuyuan , Xing, Miaojuan et al. Universal Approach to De Novo Drug Design for Target Proteins Using Deep Reinforcement Learning [J]. | ACS OMEGA , 2023 . |
| MLA | Zhang, Yunjiang et al. "Universal Approach to De Novo Drug Design for Target Proteins Using Deep Reinforcement Learning" . | ACS OMEGA (2023) . |
| APA | Zhang, Yunjiang , Li, Shuyuan , Xing, Miaojuan , Yuan, Qing , He, Hong , Sun, Shaorui . Universal Approach to De Novo Drug Design for Target Proteins Using Deep Reinforcement Learning . | ACS OMEGA , 2023 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Cu-based alloy catalysts are widely used in the field of carbon dioxide reduction reaction (CO2RR), due to the good selectivity and low overpotential. In order to achieve efficient exploration of alloy catalysts for CO2RR, a machine learning (ML) model, based on a gradient boosting regression (GBR) algorithm, is developed. By implementing a rigorous feature selection process, the dimensionality of feature space is reduced from thirteen to five, including work function (W), local electronegativity (Loc_EN), electronegativity (EN), interplanar spacing (D), and atomic number (Z), which is referred to as the WLEDZ model. The few-feature model has a high performance as that with many features, and the ML model successfully and rapidly predicts the adsorption energy of the key intermediates (HCOO, CO, and COOH) in the CO2RR process. In addition, eight Cu-based bimetallic catalysts are predicted with highly promising alternatives. This demonstrates that the WLEDZ few-feature ML model can screen highly promising bimetallic alloy for CO2RR and can also be used for the design of other types of catalysts.
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| GB/T 7714 | Xing, Miaojuan , Zhang, Yunjiang , Li, Shuyuan et al. Prediction of Carbon Dioxide Reduction Catalyst Using Machine Learning with a Few-Feature Model: WLEDZ [J]. | JOURNAL OF PHYSICAL CHEMISTRY C , 2022 . |
| MLA | Xing, Miaojuan et al. "Prediction of Carbon Dioxide Reduction Catalyst Using Machine Learning with a Few-Feature Model: WLEDZ" . | JOURNAL OF PHYSICAL CHEMISTRY C (2022) . |
| APA | Xing, Miaojuan , Zhang, Yunjiang , Li, Shuyuan , He, Hong , Sun, Shaorui . Prediction of Carbon Dioxide Reduction Catalyst Using Machine Learning with a Few-Feature Model: WLEDZ . | JOURNAL OF PHYSICAL CHEMISTRY C , 2022 . |
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摘要 ：
一种用于甲醇蒸汽重整制氢的Pd/ZnO催化剂的制备方法，属于甲醇制氢领域。该方法包括以下步骤：首先将氧化锌分散到乙二醇中形成悬浮液，然后加入四氯钯酸钠前驱体，待浸渍一段时间后离心洗涤干燥，得到Pd/ZnO催化剂，新鲜的Pd/ZnO再经过氢气还原形成部分PdZn合金，双重还原手段实现高选择性制氢。合成方法较工业上的简单且所合成的材料耐高温，400℃反应后副产物中CO含量整体较低，CH4几乎为零。本发明的制氢方法产氢量高，副产物少，操作简单不复杂，可实现便携式制氢等优点，具有很好的工业前景。
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| GB/T 7714 | 孙少瑞 , 王慧敏 , 王亚鑫 . 一种用于甲醇蒸汽重整制氢的Pd/ZnO催化剂的制备方法 : CN202211063569.3[P]. | 2022-08-31 . |
| MLA | 孙少瑞 et al. "一种用于甲醇蒸汽重整制氢的Pd/ZnO催化剂的制备方法" : CN202211063569.3. | 2022-08-31 . |
| APA | 孙少瑞 , 王慧敏 , 王亚鑫 . 一种用于甲醇蒸汽重整制氢的Pd/ZnO催化剂的制备方法 : CN202211063569.3. | 2022-08-31 . |
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摘要 ：
一种针对不同靶点蛋白进行药物设计的通用性方法属于计算机人工智能与新药设计领域，包括训练分子生成模型、训练药物靶标亲和力预测模型，以亲和力模型输出作为奖励函数，通过强化学习使分子生成神经网络生成的分子与蛋白质有更好的亲和力。将某个靶点蛋白的氨基酸序列输入到已经训练好的药物设计机器学习模型中，得到针对这个靶点蛋白的小分子抑制剂化合物。
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| GB/T 7714 | 孙少瑞 , 张云江 , 何洪 . 一种针对不同靶点蛋白进行药物设计的通用性方法 : CN202210051113.9[P]. | 2022-01-17 . |
| MLA | 孙少瑞 et al. "一种针对不同靶点蛋白进行药物设计的通用性方法" : CN202210051113.9. | 2022-01-17 . |
| APA | 孙少瑞 , 张云江 , 何洪 . 一种针对不同靶点蛋白进行药物设计的通用性方法 : CN202210051113.9. | 2022-01-17 . |
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摘要 ：
一种预测二氧化碳电还原铜合金催化剂的方法属于二氧化碳电化学还原领域，通过应用密度泛函理论计算和机器学习，克服了效率低和选择性差的难题。本方法通过优化不同种类CuM合金的表面结构，应用密度泛函理论计算CO2还原反应的关键中间体(CO、HCOO、COOH、H)在各个表面的吸附能。为了降低特征的空间维度，选择5个材料的特征参数，包括功函数(W)、原子序数(AN)、晶面间距(d)、电负性(EN)和局部电负性(χi)，通过机器学习训练，得到预测性能良好的梯度提升回归(GBR)模型，训练结果与包含13个特征的模型的预测性能接近。本方法不仅快速预测二氧化碳电还原铜合金催化剂，而且为设计其他催化剂提供了思路。
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| GB/T 7714 | 孙少瑞 , 行妙娟 , 王亚鑫 et al. 一种预测二氧化碳电还原铜合金催化剂的方法 : CN202210234812.7[P]. | 2022-02-18 . |
| MLA | 孙少瑞 et al. "一种预测二氧化碳电还原铜合金催化剂的方法" : CN202210234812.7. | 2022-02-18 . |
| APA | 孙少瑞 , 行妙娟 , 王亚鑫 , 王慧敏 , 方照临 , 孟孔 . 一种预测二氧化碳电还原铜合金催化剂的方法 : CN202210234812.7. | 2022-02-18 . |
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