An Overview of Computer Aided Drug Design and Intelligentized Virtual Screening

2019-05-14
     

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Progress

COMPELETED:

ONGOING:

TODO:

大局观及导图

语义化思维导图:G:目标 P:问题 S:方案 E:特殊情况 A:主体人员和团体

G:鉴别有制药潜力的先导化合物 P-传统筛选方法效率不足及化合物分子多样性

  • PS-Computer Aided Screening
    • P1-化合物表示法和数据库?
      • P12-化合物表示法,1-3 维
      • P12-S-SMILE, Graph, Matrix, Connective Talbes
      • P12-E-免费生物学信息库:FCD,ACD,CSD
    • P2-搜索方法
      • P21-小分子搜索的生物化学基础?
      • P21-S1-靶点对接: 靶点预处理+数据库预处理+分子对接+打分
      • P21-S2-基于配体相似性的筛选
        • P21-S21-二维子结构或相似性筛选
          • P21-S21E-快速但容易假阳性
        • P21-S22-三维药效基团搜寻
      • P21-S3-反向筛选(通过小分子搜寻靶点库):对接计算 or 药效基团
      • P22-计算机辅助方法?
        • P22-S1-基于统计学的软计算方法
          • P22-S11-机器学习(泛用)
          • P22-S12-深度学习(泛用)
          • P22-S13-最优化学习(研究验证)1 2
          • P22-S14-深度强化学习(早期实现)
        • P22-S2-基于知识的表征和推理3 4
          • P22-S21-Fuzzy Modelling(生物信息学理论)5
          • P22-S22-高维数据的特征提取(基础理论)6
        • P22-S3-约束空间的搜索和推理(生物信息学理论)7 8
        • P22-S4-传统搜索算法
          • P22-S41-遗传算法(应用)
          • P22-S42-蒙特卡罗搜索树(应用)
          • P22-S43-待补充

DeerRIDER 的个人思路:

  • Knowledge-based reasoning and inference, 已验证应用3 4
  • Optimal Learning, 充分验证应用1 2,效果显著
  • Constraint-based reasoning and modeling,部分验证于抗菌剂7
  • Knowledge-based虚拟筛选模型再应用ML,剽窃自 schrodinger 的应用科学家,无应用验证,但该技术有一些理论性资料9
  • Fuzzy Modelling,存在生物信息学中的建模应用,基于 Fuzzy Logic5
  • Feature Selection of High-Dimensional Data,存在 Drug Discovery 的应用场景
  • Contraint-Based Bioinformatics,存在Drug Design的应用场景8
  • 其他未形成应用的数据科学方法?

References

  1. Frazier, D. M. N. P. I. (2009). Optimal Learning for Drug Discovery in Ewing’s Sarcoma.  2

  2. Negoescu, D. M., Frazier, P. I. & Powell, W. B. (2011), ‘The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery’, INFORMS Journal on Computing 23(3), 346-363.  2

  3. Ghose, A. K., Herbertz, T., Pippin, D. A., Salvino, J. M., & Mallamo, J. P. (2008). Knowledge based prediction of ligand binding modes and rational inhibitor design for kinase drug discovery. Journal of medicinal chemistry, 51(17), 5149-5171.  2

  4. Ghose, A. K., Viswanadhan, V. N., & Wendoloski, J. J. (1999). A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. Journal of combinatorial chemistry, 1(1), 55-68.  2

  5. Reghunadhan, R., & Arulmozhi, V. (2013). FUZZY LOGIC FOR CHEMOINFORMATICS–A REVIEW. Journal of Theoretical & Applied Information Technology, 47(1).  2

  6. Verónica bolón-canedo , Amparo alonso-betanzos & noelia sánchez-maroño. (2015). Feature Selection for High-Dimensional Data. 

  7. Trawick, John & H Schilling, Christophe. (2006). Use of constraint-based modeling for the prediction and validation of antimicrobial targets. Biochemical pharmacology. 71. 1026-35. 10.1016/j.bcp.2005.10.049.  2

  8. Rossi, F., Van Beek, P., & Walsh, T. (Eds.). (2006). Handbook of constraint programming. Elsevier.  2

  9. Towell, G. G., & Shavlik, J. W. (1994). Knowledge-based artificial neural networks. Artificial intelligence, 70(1-2), 119-165 


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