The CSB approach to prediction of chemical reactions

被引:3
|
作者
Fic, G
Nowak, G
机构
[1] Rzeszow Univ Technol, Fac Chem, Dept Comp Chem, PL-35041 Rzeszow, Poland
[2] Rzeszow Univ Technol, Fac Chem, Dept Phys Chem, PL-35041 Rzeszow, Poland
关键词
CAOS; computer prediction of reactions; machine learning; combinatorial libraries; multicomponent reactions; CSB;
D O I
10.1016/j.chemolab.2004.05.013
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The methodology and recent advances in developing the chemical sense builder (CSB) system for simulation of organic reactions are presented. This system comprises two functional modules that can be used separately or in combination. Four logic-based and knowledge-based models for reaction generation and discovering constitute the first module. The second one, newly designed, provides knowledge acquisition and learning tools for exploration and derivation of knowledge that can be employed in the reaction simulation process. An overview of the CSB programming tools and a knowledge source are given. The new CSB features are illustrated by an example concerned with the generation and evaluation of example reactions. (C) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码:137 / 148
页数:12
相关论文
共 50 条
  • [21] Optimal selection of learning data for highly accurate QSAR prediction of chemical biodegradability: a machine learning-based approach
    Takeda, K.
    Takeuchi, K.
    Sakuratani, Y.
    Kimbara, K.
    SAR AND QSAR IN ENVIRONMENTAL RESEARCH, 2023, 34 (09) : 729 - 743
  • [22] Molecular Docking for Prediction and Interpretation of Adverse Drug Reactions
    Luo, Heng
    Fokoue-Nkoutche, Achille
    Singh, Nalini
    Yang, Lun
    Hu, Jianying
    Zhang, Ping
    COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING, 2018, 21 (05) : 314 - 322
  • [23] Cloud computing for fast prediction of chemical activity
    Cala, Jacek
    Hiden, Hugo
    Woodman, Simon
    Watson, Paul
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2013, 29 (07): : 1860 - 1869
  • [24] End-to-End Representation Learning for Chemical-Chemical Interaction Prediction
    Kwon, Sunyoung
    Yoon, Sungroh
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2019, 16 (05) : 1436 - 1447
  • [25] Computational models for the prediction of adverse cardiovascular drug reactions
    Salma Jamal
    Waseem Ali
    Priya Nagpal
    Sonam Grover
    Abhinav Grover
    Journal of Translational Medicine, 17
  • [26] Using a library of chemical reactions to fit systems of ordinary differential equations to agent-based models: a machine learning approach
    Burrage, Pamela M.
    Weerasinghe, Hasitha N.
    Burrage, Kevin
    NUMERICAL ALGORITHMS, 2024, 96 (03) : 1063 - 1077
  • [27] Computational models for the prediction of adverse cardiovascular drug reactions
    Jamal, Salma
    Ali, Waseem
    Nagpal, Priya
    Grover, Sonam
    Grover, Abhinav
    JOURNAL OF TRANSLATIONAL MEDICINE, 2019, 17
  • [28] Classification Approach to Prediction of Geomagnetic Disturbances
    I. M. Gadzhiev
    I. V. Isaev
    O. G. Barinov
    S. A. Dolenko
    I. N. Myagkova
    Moscow University Physics Bulletin, 2023, 78 : S96 - S103
  • [29] Classification Approach to Prediction of Geomagnetic Disturbances
    Gadzhiev, I. M.
    Isaev, I. V.
    Barinov, O. G.
    Dolenko, S. A.
    Myagkova, I. N.
    MOSCOW UNIVERSITY PHYSICS BULLETIN, 2023, 78 (SUPPL 1) : S96 - S103
  • [30] A New Approach for Employee Attrition Prediction
    Douaidi, Lydia
    Kheddouci, Hamamache
    GRAPH-BASED REPRESENTATION AND REASONING, ICCS 2022, 2022, 13403 : 115 - 128