刘奇磊    博士  副教授  博士生导师  硕士生导师

(欢迎博士后加入本团队)

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Email: liuqilei@dlut.edu.cn

地址：辽宁省大连市甘井子区凌工路2号大连理工大学西部校区化工实验楼G315

ORCID: https://orcid.org/0000-0002-3879-1827

科研情况

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刘奇磊，中共党员，博士，大连理工大学化工学院制药工程系副教授，博士生导师、硕士生导师。围绕AI4S开展精细化学品智能设计与合成相关研究工作。获大连市青年科技之星(2023)、大连市高层次人才-青年才俊(2024)等荣誉奖项。主持国家自然科学基金青年项目、中国博士后科学基金面上项目等纵向项目7项。发表高水平论文40余篇，包括AIChE J.、Chem. Eng. Sci.、Ind. Eng. Chem. Res.、J. Chem. Inf. Model.、Green Chem.、Fuel等，总计被引700余次，H-index为11(Google学术)。多次受邀重要学术会议报告。参编6本国内外教材/专著。授权中国专利1件，获批软件著作权7件。

科研团队

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在读: 

·    博士生: (1) 唐坤; 

·    硕士生: (1) 吴国訢; (2) 罗广; (3) 王俊涛; (4) 张广燃; (5) 宋雨欣;

·    本科生: (1) 毕欣炜; (2) 张永雯; (3) 岳鸿宇; (4) 荆枫杰; (5) 刘泽轩; (6) 王瑞涵; (7) 陈高健; 

毕业: 

·    博士: (1) 赵雨靓 (2024, 大连理工大学生物工程学院博士后);

·    硕士: (1) 吴心远 (2023, 大连理工大学优秀硕士学位论文, 浙江大学读博);  (2) 向晟 (2024, 万华化学(四川)有限公司);

·    学士: (1) 冯锟 (2018); (2) 吴心远 (2019); (3) 王文龙 (2020); (4) 唐坤 (2021); (5) 蒋寅恪 (2021); 

科研助理: 

(1) 任凯派 (2024-2025);

大学生创新创业训练计划项目: 

(1) 唐坤、冯艺璇、张金媛 (2020, 国家级, 优秀结题);

(2) 孙静蕾、付莉岭、赵韵婷、韦融雪 (2024, 校级, 在研);

教育科研经历

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·    2024.3-至今          大连理工大学化工学院        副教授

·    2021.7-2024.3       大连理工大学化工学院        博士后、助理研究员 (合作导师: 孟庆伟 教授)

·    2016.9-2021.7       大连理工大学化学工程        博士 (导师: 都健 教授、张磊 教授 (优青))

·    2012.9-2016.7       大连理工大学化学工程        学士

研究方向

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·    基于机器学习势的分子构效关系

       耦合深度学习与量子化学技术，研究替代传统密度泛函理论的电子结构计算方法，兼顾分子力学的预测效率与密度泛函理论的预测精度

·    分子产品智能设计

       结合机器学习势与分子生成算法，高通量、智能化反向优化设计新型分子产品，研究体系包括催化剂、药物、溶剂等

·    分子产品智能合成

       结合机器学习势与逆合成规划算法，敏捷智能设计分子产品合成路线，并优化催化剂、溶剂等反应条件

纵向科研项目

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[7] 2024.09-2026.08, 辽宁省博士科研启动基金计划项目 (2024-BSBA-08), 考虑反应动力学速率与反应条件的药物逆合成路线智能设计方法. (主持, 在研)

[6] 2024.09-2025.08, AI4S交叉研究基金项目, 光动力治疗近红外光敏剂智能设计方法研究. (主持, 在研)

[5] 2024.05-2026.04, 中央高校基本科研业务费项目 (the Fundamental Research Funds for the Central Universities [DUT24RC(3)042]), 基于机器学习势的精细化学品智能设计与合成. (主持, 在研)

[4] 2023.11-2025.10, 大连市青年科技之星项目 (Dalian High-level Talents Innovation Support Program [2023RQ059]), 基于量子化学与深度学习的小分子药物智能设计与合成. (主持, 在研)

[3] 2023.01-2025.12, 国家自然科学基金青年科学基金项目 (National Natural Science Foundation of China [22208042]), 基于量子化学与深度学习的燃烧前碳捕集低共熔溶剂设计方法. (主持, 在研)

[2] 2022.04-2024.03, 中国博士后科学基金第71批面上资助二等项目 (China Postdoctoral Science Foundation [2022M710578]), 基于深度势能与蒙特卡洛树搜索的药物逆合成路线设计方法. (主持, 结题)

[1] 2022.01-2023.12, 2022年度基本科研业务费交叉探索科研专题医工交叉联合基金-中心医院项目 (the Fundamental Research Funds for the Central Universities [DUT22YG218]), PD-1小分子抑制剂的筛选及其联合NK细胞在肺癌免疫治疗中的应用. (主持, 结题)

获奖情况

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[15] 2024年过程系统工程年会(PSE 2024)优秀论文二等奖

[14] 2024 International Congress on Separation and Purification Technology-Best Oral Presentation

[13] 2024年大连市高层次人才-青年才俊

[12] 2023年大连市青年科技之星

[11] 2022年过程系统工程年会(PSE 2022)优秀论文一等奖

[10] 第一届中国医药化工大会(CPCEC2022)优秀海报奖

[9] 博士研究生国家奖学金

[8] 硕士研究生(优秀推免生)国家奖学金

[7] 本科生国家奖学金 (3次)

[6] 辽宁省优秀毕业生

[5] 大连理工大学优秀毕业生

[4] 大连理工大学博士一等学业奖学金

[3] 大连理工大学校优秀研究生

[2] 大连理工大学化工学院第一届博士生学术论坛优秀学术论文二等奖

[1] 大连理工大学优秀团员 (2次)

发表论文

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一作&通讯: 

[24] Zhao, Y., Zhang, L., Du, J., Meng, Q., Zhang, L., Wang, H., Sun, L., Liu, Q.*. Mixture-of-experts based dissociation kinetic model for de novo design of HSP90 inhibitors with prolonged residence time. Journal of Chemical Information and Modeling, 2024, Accepted. (SCI)

[23] Tang, K., Zhang, L., Meng, Q., Du, J., Liu, Q.*. TSeC: an efficient transition state search tool driven by machine learning potential. Computer Aided Chemical Engineering, Elsevier, 2024, 53, 3355-3360. (CPCI)

[22] Wu, G., Liu, Q.*, Du, J., Meng, Q., Zhang, L.*. A deep learning-based energy and force prediction framework for high-throughput quantum chemistry calculations. Computer Aided Chemical Engineering, Elsevier, 2024, 53, 715-720. (CPCI)

[21] Zhao, Y., Liu, Q.*, Zhuang, Y., Dong, Y., Liu, L., Du, J., Meng, Q., Zhang, L.*. Hybrid deep learning model for evaluations of protein-ligand binding kinetic property. Computer Aided Chemical Engineering, Elsevier, 2024, 53, 259-264. (CPCI)

[20] Yu, Z., Li, S., Wu, Y., Ma, C., Li, J., Duan, L., Liu, Z., Sun, H., Zhao, G., Lu, Y., Liu, Q.*, Meng, Q.*, Zhao, J.*. Continuous photocatalytic preparation of hydrogen peroxide with anthraquinone photosensitizers. Green Chemistry, 2024, 26, 9310. (SCI)

[19] Liu, Q., Xiang, S., Du, J., Meng, Q., Chen, J., Gao, M., Xing, B., Zhang, L.*. Improved prediction of reaction kinetics for amine absorbent-based carbon capture using reactive site-based transition state conformer search method. Fuel, 2024, 361, 130730. (SCI)

[18] 向晟, 刘奇磊*, 张磊, 都健. 基于反应动力学的计算机辅助碳捕集有机胺溶剂设计. 清华大学学报(自然科学版), 2024, 64(3), 520-527. (EI)

[17] Zhao, Y., Liu, Q.*, Du, J., Meng, Q., Zhang, L.*. Machine learning methods for developments of binding kinetic models in predicting protein-ligand dissociation rate constants. Smart Molecules, 2023, 1, e20230012.

[16] Tang, K., Zhuang, Y., Wang W., Liu Q.*, Zhang L., Du, J., Meng, Q. GC-NORM-based thermodynamic framework for evaluations of organic reactions involving carbon dioxide utilization. Chemical Engineering Science, 2023, 278, 118913. (SCI)

[15] Liu, Q., Tang, K., Zhang, L., Du, J.*, Meng, Q. Computer-assisted synthetic planning considering reaction kinetics based on transition state automated generation method. AIChE Journal, 2023, 69(7), e18092. (SCI) 

[14] 吴心远, 刘奇磊*, 曹博渊, 张磊, 都健. Group2vec：基于无监督机器学习的基团向量表示及其物性预测应用. 化工学报, 2023, 74(3), 1187-1194. (EI)

[13] Wang, W., Liu, Q.*, Dong, Y., Du, J., Meng, Q., Zhang, L.*. ConvPred: A deep learning-based framework for predictions of potential organic reactions. AIChE Journal, 2023, 69(5), e18019. (SCI)

[12] Wu, X., Liu, Q.*, Zhao, Y., Zhang, L., Du, J. Reaction kinetic model considering the solvation effect based on the FMO theory and deep learning. Industrial & Engineering Chemistry Research, 2022, 61(41), 15261-15272. (SCI, Supplementary Cover)

[11] Zhao, Y., Liu, Q.*, Wu, X., Zhang, L., Du, J.*, Meng, Q. De novo drug design framework based on mathematical programming method and deep learning model. AIChE Journal, 2022, 68(9), e17748. (SCI)

[10] Liu, Q., Jiang, Y., Zhang, L.*, Du, J. A computational toolbox for molecular property prediction based on quantum mechanics and quantitative structure-property relationship. Frontiers of Chemical Science and Engineering, 2022, 16(2), 152-167. (SCI)

[9] Liu, Q., Zhang, L.*, Tang, K., Liu, L., Du, J., Meng, Q., Gani, R. Machine learning‐based atom contribution method for the prediction of surface charge density profiles and solvent design. AIChE Journal, 2021, 67(2), e17110. (SCI, AIChE Top Cited Article 2020-2022)

[8] Liu, Q., Zhang, L.*, Du, J., Gani, R. Computer-aided solvent design integrated with a machine learning-based atom contribution method. Computer Aided Chemical Engineering, Elsevier, 2021, 50, 69-74. (CPCI)

[7] Li, S.^∥, Liu, Q.^∥, Wang, X., Wu, Q., Fan, L., Zhang, W., Shen, Z., Wang, L., Ling, M.*, Lu, Y.*. Constructing a phosphating–nitriding interface for practically used lithium metal anode. ACS Materials Letters, 2020, 2(1), 1-8. (SCI)

[6] Liu, Q., Tang, K., Zhang, J., Feng, Y., Xu, C., Liu, L., Du, J., Zhang, L.*. QMaC: a quantum mechanics/machine learning-based computational tool for chemical product design. Computer Aided Chemical Engineering, Elsevier, 2020, 48, 1807-1812. (CPCI)

[5] Liu, Q., Zhang, L., Tang, K., Feng, Y., Zhang, J., Zhuang, Y., Liu, L., Du, J.*. Computer-aided reaction solvent design considering inertness using group contribution-based reaction thermodynamic model. Chemical Engineering Research and Design, 2019, 152, 123-133. (SCI)

[4] Liu, Q., Zhang, L.*, Liu, L., Du, J., Meng, Q., Gani, R. Computer-aided reaction solvent design based on transition state theory and COSMO-SAC. Chemical Engineering Science, 2019, 202, 300-317. (SCI)

[3] 刘奇磊, 冯锟, 刘琳琳, 都健, 孟庆伟, 张磊*. 基于Dragon描述符与改进的决策树-遗传算法的反应溶剂设计方法. 化工学报, 2019, 70(2), 533-540. (EI)

[2] Liu, Q., Zhang, L.*, Liu, L., Du, J., Tula, A. K., Eden, M., Gani, R. OptCAMD: an optimization-based framework and tool for molecular and mixture product design. Computers & Chemical Engineering, 2019, 124, 285-301. (SCI)

[1] Liu, Q., Zhang, L.*, Liu, L., Du, J., Liang, X., Mao, H., Meng, Q. GC-COSMO based reaction solvent design with new kinetic model using CAMD. Computer Aided Chemical Engineering, Elsevier, 2018, 44, 235-240. (CPCI)

 

二作&其它: 

[17] Che, X., Liu, Q., Yu, F., Zhang, L.*. Computer-Aided Drug Screening Based on the Binding Site Selectivity of ACE2: Machine Learning, Docking, and Molecular Dynamics Simulations. Computer Aided Chemical Engineering, Elsevier, 2024, 53, 2431-2436. (CPCI)

[16] Zhao, Y., Liu, Q., Du, J., Meng, Q., Sun, L., Zhang, L.*. Accelerating Factor Xa inhibitor discovery with a de novo drug design pipeline. Chinese Journal of Chemical Engineering, 2024, 72, 85-94. (SCI)

[15] Che, X., Liu, Q., Yu, F., Zhang, L.*, Gani, R. A virtual screening framework based on the binding site selectivity for small molecule drug discovery. Computers & Chemical Engineering, 2024, 184, 108626. (SCI)

[14] Li, S.^∥, Zhang, J.^∥, Zhang, S.^∥, Liu, Q., Cheng, H., Fan, L., Zhang, W., Wang, X., Wu, Q., Lu, Y.*. Cation replacement method enables high-performance electrolytes for multivalent metal batteries. Nature Energy, 2023, Online. (SCI)

[13] Che, X., Liu, Q., Zhang, L.*. An accurate and universal protein-small molecule batch docking solution using Autodock Vina. Results in Engineering, 2023, 19, 101335. (SCI)

[12] Wang, W., Liu, Q., Zhang, L.*, Dong, Y., Du, J. RetroSynX: A retrosynthetic analysis framework using hybrid reaction templates and group contribution-based thermodynamic models. Chemical Engineering Science, 2022, 248, 117208. (SCI)

[11] Wang, W., Liu, Q., Zhang, L.*, Dong, Y., Du, J. Retrosynthesis pathway design using hybrid reaction templates and group contribution-based thermodynamic models. Computer Aided Chemical Engineering, Elsevier, 2022, 49, 85-90. (CPCI)

[10] Guo, W., Liu, Q., Zhang, L.*, Du, J., Zhu, X., Fung, K. Y., Yong, Y., Ng, K. M. Computer-aided design of a perfluorinated sulfonic acid proton exchange membrane using stochastic optimization and molecular dynamic method. Industrial & Engineering Chemistry Research, 2021, 60(49), 18045-18057. (SCI, Supplementary Cover)

[9] 唐坤, 刘奇磊, 张磊*, 刘琳琳, 都健, 孟庆伟. 基于高阶基团贡献法与COSMO-SAC模型的溶剂设计方法. 化工进展, 2021, 40(S2), 48-55. (EI)

[8] Li, S., Liu, Q., Zhang, W., Fan, L., Wang, X., Wang, X., Shen, Z., Zang, X., Zhao, Y., Ma, F., Lu, Y.*. High‐efficacy and polymeric solid‐electrolyte interphase for closely packed Li electrodeposition. Advanced Science, 2021, 8(6), 2003240. (SCI)

[7] 赵红庆, 刘奇磊, 张磊*, 董亚超, 都健. 考虑选择性和反应速率的多目标制药反应溶剂设计. 化工学报, 2021, 72(3), 1465-1472. (EI, 封面文章)

[6] Chai, S., Liu, Q., Liang, X., Guo, Y., Zhang, S., Xu, C., Du, J., Yuan, Z., Zhang, L.*, Gani, R.*. A grand product design model for crystallization solvent design. Computers & Chemical Engineering, 2020, 135, 106764. (SCI)

[5] Zhao, Y., Chen, J., Liu, Q., Li, Y.*. Profiling the structural determinants of aryl benzamide derivatives as negative allosteric modulators of mGluR5 by in silico study. Molecules, 2020, 25(2), 406. (SCI)

[4] Zhang, L.*, Mao, H., Liu, Q., Gani, R.*. Chemical product design–recent advances and perspectives. Current Opinion in Chemical Engineering, 2020, 27, 22-34. (SCI, COChE Top Cited Article 2020-2022)

[3] 柴士阳, 刘奇磊, 梁馨元, 张颂, 郭彦锁, 徐承秋, 张磊, 都健*, 袁志宏. 计算机辅助高纯2-巯基苯并噻唑结晶溶剂设计方法. 清华大学学报(自然科学版), 2020, 60(8), 701-706. (EI)

[2] Li, S., Liu, Q., Zhou, J., Pan, T., Gao, L., Zhang, W., Fan, L., Lu, Y.*. Hierarchical Co3O4 nanofiber–carbon sheet skeleton with superior Na/Li‐philic property enabling highly stable alkali metal batteries. Advanced Functional Materials, 2019, 29(19), 1808847. (SCI, 高被引)

[1] Zhuang, Y., Liu, L., Liu, Q., Du, J.*. Step-wise synthesis of work exchange networks involving heat integration based on the transshipment model. Chinese Journal of Chemical Engineering, 2017, 25(8), 1052-1060. (SCI)

学术会议

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[24] 刘奇磊. 机器学习势驱动的分子智能设计与合成. 第十三届中国颗粒大会过程工程中的介科学与人工智能分会场, 苏州. 10月24-28日, 2024.

[23] 刘奇磊. 基于反应机器学习势的有机金属催化剂智能设计. 2024年第三届全国精细化工大会, 西安. 10月11-13日, 2024.

[22] 吴国訢, 张磊, 都健, 孟庆伟, 刘奇磊*. 基于机器学习势的分子几何结构优化方法. 2024年过程系统工程年会, 大连. 8月23-25日, 2024.

[21] 唐坤, 陈骏芃, 张磊, 都健, 孟庆伟, 刘奇磊*. 反应机器学习势驱动的过渡态结构优化方法. 2024年过程系统工程年会, 大连. 8月23-25日, 2024.

[20] 唐坤, 陈骏芃, 张磊, 都健, 孟庆伟, 刘奇磊*. 基于反应机器学习势的不对称催化剂智能设计. 2024年中国化工学会“应星”青年论坛, 大连. 8月2-4日, 2024.

[19] Xiang, S., Zhao, Y., Zhang, L., Du, J., Meng, Q., Liu, Q.*. Computer-aided amine solvent design framework for carbon capture based on quantum chemistry and data-driven methods. International Congress on Separation and Purification Technology 2024. Zhengzhou, Henan. July. 7-11, 2024. (Oral)

[18] Wu, G., Liu, Q.*, Du, J., Meng, Q., Zhang, L.*. A deep learning-based energy and force prediction framework for high-throughput quantum chemistry calculations. Proceedings of the 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering (ESCAPE34/PSE24). Florence, Italy. June. 2-6, 2024. (Oral)

[17] Tang, K., Zhang, L., Meng, Q., Du, J., Liu, Q.*. TSeC: an efficient transition state search tool driven by machine learning potential. Proceedings of the 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering (ESCAPE34/PSE24). Florence, Italy. June. 2-6, 2024. (Poster)

[16] Zhao, Y., Liu, Q.*, Zhuang, Y., Dong, Y., Liu, L., Du, J., Meng, Q., Zhang, L.*. Hybrid deep learning model for evaluations of protein-ligand binding kinetic property. Proceedings of the 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering (ESCAPE34/PSE24). Florence, Italy. June. 2-6, 2024. (Poster)

[15] 刘奇磊. 基于量子化学与深度学习的化工产品智能设计与合成. 第一期"交叉学科未来论坛"(AI4S系列研讨会), 大连. 12月8日, 2023. (口头报告)

[14] 吴国訢, 刘奇磊*, 张磊, 都健, 孟庆伟. DeepEF-COSMO-SAC: 基于深度势能的化工活度系数性质预测方法. 2023科学智能峰会, 北京. 8月10-11日, 2023. (墙报)

[13] 向晟, 刘奇磊*, 张磊, 都健. 基于反应动力学的计算机辅助碳捕集有机胺溶剂设计. 2023年过程系统工程年会, 天津. 8月4-6日, 2023. (口头报告)

[12] 赵雨靓, 刘奇磊*, 张磊, 都健, 孟庆伟. 基于深度学习与数学规划法的小分子药物从头设计. 中国材料大会2022-2023, D26-智能分子材料, 深圳. 7月7日-10日, 2023. (口头报告)

[11] Wu, X., Liu, Q.*, Zhang L., Du J. Deep Learning Model for Prediction of Dielectric Constant: Application to Reaction Kinetics considering Solvation Effect. 10th Asian Symposium on Process Systems Engineering (PSE Asia 2022). Chennai, India. Dec. 11-14, 2022. (Oral)

[10] 吴心远, 曹博渊, 刘奇磊, 张磊, 都健. Group2vec: 基于无监督机器学习的基团向量表示及其物性预测应用. 2022年过程系统工程年会, 北京. 8月26-28日, 2022. (口头报告)

[9] 刘奇磊, 唐坤, 董亚超, 张磊, 都健, 孟庆伟. GENiniTS-RS: 基于反应位点的过渡态初猜结构生成算法. 第一届中国医药化工大会, 杭州, 浙江. 7月16-18日, 2022. (墙报)

[8] Liu, Q., Zhang, L., Du, J., Gani, R. Computer-aided solvent design integrated with a machine learning-based atom contribution method. Proceedings of the 31st European Symposium on Computer Aided Process Engineering (ESCAPE31), İstanbul, Turkey. June. 6-9, 2021. (Poster)

[7] Liu, Q., Tang, K., Zhang, J., Feng, Y., Xu, C., Liu, L., Du, J., Zhang, L. QMaC: A quantum mechanics/machine learning-based computational tool for chemical product design. Proceedings of the 30th European Symposium on Computer Aided Process Engineering (ESCAPE30), Milano, Italy. May. 24-27, 2020. (Poster)

[6] Liu, Q., Zhang, L., Zhuang, Y., Liu, L., Du, J. A GC-SMARTS based computer-aided reaction solvent design considering inertness. 全国化学工程前沿博士生学术论坛, 衢州, 浙江. Nov. 3-6, 2019. (口头报告)

[5] Liu, Q., Mao, H., Zhang, L., Liu, L., Du, J., Meng, Q., Gani, R. An optimization-based COSMO-CAMD framework for solvent design using GC+-COSMO method. 大连理工大学化工学院第一届博士生学术论坛, 大连, 辽宁. May. 21, 2019. (口头报告)

[4] Zhang, L., Liu, Q., Liu, L., Du, J., Gani, R. A new optimization-based computer-aided molecular and mixture design (OptCAMD) framework. 2018 AIChE Annual Meeting. Pittsburgh, PA, USA. Oct. 26-Nov. 2, 2018. (Poster)

[3] Liu, Q., Zhang, L., Liu, L., Du, J., Meng, Q. Computer-aided reaction solvent design integrated with new reaction mechanism model. 2018 AIChE Annual Meeting. Pittsburgh, PA, USA. Oct. 26-Nov. 2, 2018. (Oral)

[2] 刘奇磊, 冯锟, 刘琳琳, 都健, 孟庆伟, 张磊. 基于Dragon描述符与改进的决策树—遗传算法的反应溶剂设计方法. 2018年中国过程系统工程年会, 西宁, 青海. 8月22-24日, 2018. (分场特邀报告)

[1] Liu, Q., Zhang, L., Liu, L., Du, J., Liang, X., Mao, H., Meng, Q. GC-COSMO based reaction solvent design with new kinetic model using CAMD. Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018. San Diego, California, USA. July. 1-5, 2018. (Oral)

教材专著

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[6] 刘奇磊, 赵雨靓, 任凯派, 张磊, 都健, 智能分子材料基础(第12章——人工智能与分子设计), 已提交. (教材)

[5] Liu, Q., Mao, H., Wang, L., Zhang, L.*. Hunting for better aromatic chemicals with AI techniques. Applied AI Techniques in the Process Industry, Wiley, Submited. (专著)

[4] 都健, 邱彤, 项曙光, 董亚超, 罗祎青, 肖武, 史彬, 胡山鹰, 张磊, 庄钰, 刘琳琳, 刘奇磊, 过程系统工程(第13章——化工产品设计), 化学工业出版社, 已完稿排版中, 适用专业(化学工程), 属于核心课程. (教材)

[3] 分册主编(张磊, 刘奇磊, 柴士阳), 分子炼油技术丛书(第四册——炼化过程中的物性预测与智能分子设计), 石油工业出版社, 已完稿排版中, 已入选“十四五”时期国家重点图书初版专项规划、中国石油科技精品图书, 获评2024年度国家出版基金资助项目. (专著)

[2] 都健, 董亚超, 张磊, 刘奇磊, 庄钰, 刘琳琳, 顾偲雯, 孟庆伟, 赵静喃, 任婧杰, 高伟(作者按章节出现顺序排序), 化工智能制造概论(第三章——机器学习与化工产品及过程建模), 化学工业出版社, 2023年, 教材等级(已入选战略性新兴领域“十四五”高等教育教材体系建设团队、大连理工大学精品教材建设项目), 适用专业(化工与制药类), 属于核心课程. (教材)

[1] Liu, Q., Mao, H., Zhang, L.*, Liu, L., Du, J. Integrated machine learning framework for computer-aided chemical product design. In Applications of Artificial Intelligence in Process Systems Engineering, Elsevier, 2021, 325-359. (专著)

软件著作权

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[7] 刘奇磊, 吴国訢, 赵雨靓, 张磊, 都健, 孟庆伟. DeePEST基于深度学习的分子势能面预测工具软件(2024SR1284251), 2024.09.02.

[6] 刘奇磊, 唐坤, 张磊, 都健, 孟庆伟, 高志刚, 何宇鹏, 于芳. GENiniTS-RS基于反应模板的过渡态初猜结构生成软件(2023SR0474878), 2023.04.17.

[5] 刘奇磊, 向晟, 张磊, 都健, 孟庆伟, 高志刚, 何宇鹏, 于芳. GENConf-TS基于反应位点的过渡态构象异构体搜索软件(2023SR0474876), 2023.04.17.

[4] 刘奇磊, 赵雨靓, 张磊, 都健, 孟庆伟. DrugCAMD基于数学规划模型的高通量药物分子优化设计软件(2022SR0622099), 2022.05.23.

[3] 张磊, 刘奇磊. OptCAMD2高通量化工分子性质预测及产品设计软件(2021SR0987620), 2021.07.05. (已成果转化)

[2] 张磊, 王文龙, 刘奇磊. RetroSynX分子逆合成路径设计软件(2020SR1257999), 2020.11.20.

[1] 张磊, Rafiqul Gani, 刘奇磊. OptCAMD基于数学优化模型的高通量化工分子产品设计软件(2019SR0734574), 2019.07.16.

专利

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[3] 张磊, 赵雨靓, 刘奇磊, 都健, 孟庆伟. 一种化合物在抑制Xa因子方面的应用. CN117860737A. 2024.04.12. (公开)

[2] 刘奇磊, 都健, 赵雨靓, 张磊, 吴心远, 孟庆伟. 基于门控注意力机制的靶标-配体结合亲和力的深度学习预测方法. 专利号: ZL 202210394865.5. 授权公告日: 2024.10.01. (授权)

[1] 刘奇磊, 张磊, 赵雨靓, 都健, 孟庆伟. 一种优化的从头药物设计方法. CN114842924A. 2022.08.02. (公开)