Resource Collection for Intelligentized Computer-aided Drug Design



Virtual Screening

  • Virtual Screening: Principles, Challenges and Practical Guidelines(2011)
  • Kitchen, D. B., Decornez, H., Furr, J. R., & Bajorath, J. (2004). Docking and scoring in virtual screening for drug discovery: methods and applications. Nature reviews Drug discovery, 3(11), 935.


  • AutoDock 4 for Virtual Screening and its Tutorial
  • PDB 优化后的数据库:PDB bind(version 2018)
  • ZINC15,a free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable compounds in ready-to-dock, 3D formats.
  • Click2Drug, a comprehensive list of computer-aided drug design (CADD) software, databases and web services


Machine Learning

  • Machine learning for ligand-based virtual screening and chemogenomics, link
  • Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., … & Pande, V. (2018). MoleculeNet: a benchmark for molecular machine learning. Chemical science, 9(2), 513-530.

Deep Learning

  • Ragoza, M., Hochuli, J., Idrobo, E., Sunseri, J., & Koes, D. R. (2017). Protein–ligand scoring with convolutional neural networks. Journal of chemical information and modeling, 57(4), 942-957.
  • Pereira, J. C., Caffarena, E. R., & dos Santos, C. N. (2016). Boosting docking-based virtual screening with deep learning. Journal of chemical information and modeling, 56(12), 2495-2506.
  • Feriante, J. (2015). Massively Multitask Deep Learning for Drug Discovery. University of Wisconsin-Madison

Optimal Learning

  • Optimal Learning for Drug Discovery in Ewing’s Sarcoma, link
  • 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.
  • Powell, W. B., & Frazier, P. (2008). Optimal learning. In State-of-the-Art Decision-Making Tools in the Information-Intensive Age (pp. 213-246). Informs.
  • Powell, W. B., & Ryzhov, I. O. (2012). Optimal learning (Vol. 841). John Wiley & Sons.

Feature Selection for High-Dimensional Data

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

Fuzzy Logic and Fuzzy Modelling

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

Knowledge-based Approach

  • 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.
  • 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

Constraint-based Approach

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