최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Biotechnology journal, v.16 no.5, 2021년, pp.2000605 -
Kim, Yeji (Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, KAIST Institute for BioCentury Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea) , Ryu, Jae Yong (Data Convergence Drug Research Center Korea Research Institute of Chemical Technology Daejeon Republic of Korea) , Kim, Hyun Uk (Systems Metabolic Engineering and Systems Healthcare Cross‐) , Jang, Woo Dae (Generation Collaborative Laboratory KAIST Daejeon Republic of Korea) , Lee, Sang Yup (Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, KAIST Institute for BioCentury Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea)
AbstractRetrobiosynthesis allows the designing of novel biosynthetic pathways for the production of chemicals and materials through metabolic engineering, but generates a large number of reactions beyond the experimental feasibility. Thus, an effective method that can reduce a large number of the in...
Ko, Yoo-Sung, Kim, Je Woong, Lee, Jong An, Han, Taehee, Kim, Gi Bae, Park, Jeong Eum, Lee, Sang Yup. Tools and strategies of systems metabolic engineering for the development of microbial cell factories for chemical production. Chemical Society reviews, vol.49, no.14, 4615-4636.
Hadadi, N., Hatzimanikatis, V.. Design of computational retrobiosynthesis tools for the design of de novo synthetic pathways. Current opinion in chemical biology, vol.28, 99-104.
Wang, Lin, Dash, Satyakam, Ng, Chiam Yu, Maranas, Costas D.. A review of computational tools for design and reconstruction of metabolic pathways. Synthetic and systems biotechnology, vol.2, no.4, 243-252.
Kanehisa, Minoru, Furumichi, Miho, Tanabe, Mao, Sato, Yoko, Morishima, Kanae. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic acids research, vol.45, no.d1, D353-D361.
Caspi, Ron, Billington, Richard, Keseler, Ingrid M, Kothari, Anamika, Krummenacker, Markus, Midford, Peter E, Ong, Wai Kit, Paley, Suzanne, Subhraveti, Pallavi, Karp, Peter D. The MetaCyc database of metabolic pathways and enzymes - a 2019 update. Nucleic acids research, vol.48, no.d1, D445-D453.
Jeske, Lisa, Placzek, Sandra, Schomburg, Ida, Chang, Antje, Schomburg, Dietmar. BRENDA in 2019: a European ELIXIR core data resource. Nucleic acids research, vol.47, no.d1, D542-D549.
Cho, Ayoun, Yun, Hongseok, Park, Jin Hwan, Lee, Sang Yup, Park, Sunwon. Prediction of novel synthetic pathways for the production of desired chemicals. BMC systems biology, vol.4, 35-35.
Marchant, Carol A., Briggs, Katharine A., Long, Anthony. In Silico Tools for Sharing Data and Knowledge on Toxicity and Metabolism: Derek for Windows, Meteor, and Vitic. Toxicology mechanisms and methods, vol.18, no.2, 177-187.
Moriya, Yuki, Shigemizu, Daichi, Hattori, Masahiro, Tokimatsu, Toshiaki, Kotera, Masaaki, Goto, Susumu, Kanehisa, Minoru. PathPred: an enzyme-catalyzed metabolic pathway prediction server. Nucleic acids research, vol.38, no.2, W138-W143.
Campodonico, M.A., Andrews, B.A., Asenjo, J.A., Palsson, B.O., Feist, A.M.. Generation of an atlas for commodity chemical production in Escherichia coli and a novel pathway prediction algorithm, GEM-Path. Metabolic engineering, vol.25, 140-158.
Hadadi, Noushin, Hafner, Jasmin, Shajkofci, Adrian, Zisaki, Aikaterini, Hatzimanikatis, Vassily. ATLAS of Biochemistry: A Repository of All Possible Biochemical Reactions for Synthetic Biology and Metabolic Engineering Studies. ACS Synthetic biology, vol.5, no.10, 1155-1166.
Kumar, Akhil, Wang, Lin, Ng, Chiam Yu, Maranas, Costas D.. Pathway design using de novo steps through uncharted biochemical spaces. Nature communications, vol.9, no.1, 184-.
Delépine, Baudoin, Duigou, Thomas, Carbonell, Pablo, Faulon, Jean-Loup. RetroPath2.0: A retrosynthesis workflow for metabolic engineers. Metabolic engineering, vol.45, 158-170.
Yang, Xue, Yuan, Qianqian, Luo, Hao, Li, Feiran, Mao, Yufeng, Zhao, Xin, Du, Jiawei, Li, Peishun, Ju, Xiaozhi, Zheng, Yangyang, Chen, Yang, Liu, Yuwan, Jiang, Huifeng, Yao, Yonghong, Ma, Hongwu, Ma, Yanhe. Systematic design and in vitro validation of novel one-carbon assimilation pathways. Metabolic engineering, vol.56, 142-153.
Yim, Harry, Haselbeck, Robert, Niu, Wei, Pujol-Baxley, Catherine, Burgard, Anthony, Boldt, Jeff, Khandurina, Julia, Trawick, John D, Osterhout, Robin E, Stephen, Rosary, Estadilla, Jazell, Teisan, Sy, Schreyer, H Brett, Andrae, Stefan, Yang, Tae Hoon, Lee, Sang Yup, Burk, Mark J, Van Dien, Stephen. Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol. Nature chemical biology, vol.7, no.7, 445-452.
Fehér, Tamás, Planson, Anne‐Gaëlle, Carbonell, Pablo, Fernández‐Castané, Alfred, Grigoras, Ioana, Dariy, Ekaterina, Perret, Alain, Faulon, Jean‐Loup. Validation of RetroPath, a computer‐aided design tool for metabolic pathway engineering. Biotechnology journal, vol.9, no.11, 1446-1457.
Ren, Jie, Zhou, Libang, Wang, Chuang, Lin, Chen, Li, Zhidong, Zeng, An-Ping. An Unnatural Pathway for Efficient 5-Aminolevulinic Acid Biosynthesis with Glycine from Glyoxylate Based on Retrobiosynthetic Design. ACS Synthetic biology, vol.7, no.12, 2750-2757.
Mu, Fangping, Unkefer, Clifford J., Unkefer, Pat J., Hlavacek, William S.. Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds. Bioinformatics, vol.27, no.11, 1537-1545.
Su, Shimin, Yang, Yuyao, Gan, Hanlin, Zheng, Shuangjia, Gu, Fenglong, Zhao, Chao, Xu, Jun. Predicting the Feasibility of Copper(I)-Catalyzed Alkyne-Azide Cycloaddition Reactions Using a Recurrent Neural Network with a Self-Attention Mechanism. Journal of chemical information and modeling, vol.60, no.3, 1165-1174.
Fooshee, David, Mood, Aaron, Gutman, Eugene, Tavakoli, Mohammadamin, Urban, Gregor, Liu, Frances, Huynh, Nancy, Van Vranken, David, Baldi, Pierre. Deep learning for chemical reaction prediction. Molecular systems design and engineering, vol.3, no.3, 442-452.
Baylon, Javier L., Cilfone, Nicholas A., Gulcher, Jeffrey R., Chittenden, Thomas W.. Enhancing Retrosynthetic Reaction Prediction with Deep Learning Using Multiscale Reaction Classification. Journal of chemical information and modeling, vol.59, no.2, 673-688.
Rahman, Syed Asad, Torrance, Gilliean, Baldacci, Lorenzo, Martínez Cuesta, Sergio, Fenninger, Franz, Gopal, Nimish, Choudhary, Saket, May, John W., Holliday, Gemma L., Steinbeck, Christoph, Thornton, Janet M.. Reaction Decoder Tool (RDT): extracting features from chemical reactions. Bioinformatics, vol.32, no.13, 2065-2066.
Adv. Neural Inf. Process. Syst. Snoek J. 2951 2 2012 Practical Bayesian optimization of machine learning algorithms
Rogers, David, Hahn, Mathew. Extended-Connectivity Fingerprints. Journal of chemical information and modeling, vol.50, no.5, 742-754.
Gómez-Bombarelli, Rafael, Wei, Jennifer N., Duvenaud, David, Hernández-Lobato, José Miguel, Sánchez-Lengeling, Benjamín, Sheberla, Dennis, Aguilera-Iparraguirre, Jorge, Hirzel, Timothy D., Adams, Ryan P., Aspuru-Guzik, Alán. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS central science, vol.4, no.2, 268-276.
Int. Conf. Mach. Learn. Gal Y. 1050 48 2016 Dropout as a Bayesian approximation: representing model uncertainty in deep learning
Duigou, Thomas, du Lac, Melchior, Carbonell, Pablo, Faulon, Jean-Loup. RetroRules: a database of reaction rules for engineering biology. Nucleic acids research, vol.47, no.d1, D1229-D1235.
Kim, Sunghwan, Thiessen, Paul A., Bolton, Evan E., Chen, Jie, Fu, Gang, Gindulyte, Asta, Han, Lianyi, He, Jane, He, Siqian, Shoemaker, Benjamin A., Wang, Jiyao, Yu, Bo, Zhang, Jian, Bryant, Stephen H.. PubChem Substance and Compound databases. Nucleic acids research, vol.44, no.d1, D1202-D1213.
Abadi M. Agarwal A. Barham P. Brevdo E. Chen Z. Citro C. …Zheng X.(2016).TensorFlow: Large‐scale machine learning on heterogeneous distributed systems. arXiv:1603.04467v2.
J. Mach. Learn. Res. Pedregosa F. 2825 12 2011 Scikit‐learn: machine learning in python
해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.