Bibliography of computer-aided Drug Design

Updated on 7/18/2014. Currently 2130 references

Visualization / Reviews

2013 / 2011 / 2010 / 2009 /


2013

  • Progress in the Visualization and Mining of Chemical and Target Spaces
    Medina-Franco, José L and Aguayo-Ortiz, Rodrigo
    Molecular Informatics, 2013, 32(11-12), 942-953
    doi: 10.1002/minf.201300041
     
    Chemogenomics is a growing field that aims to integrate the chemical and target spaces. As part of a multi-disciplinary effort to achieve this goal, computational methods initially developed to visualize the chemical space of compound collections and mine single-target structure-activity relationships, are being adapted to visualize and mine complex relationships in chemogenomics data sets. Similarly, the growing evidence that clinical effects are many times due to the interaction of single or multiple drugs with multiple targets, is encouraging the development of novel methodologies that are integrated in multi-target drug discovery endeavors. Herein we review advances in the development and application of approaches to generate visual representations of chemical space with particular emphasis on methods that aim to explore and uncover relationships between chemical and target spaces. Also, progress in the data mining of the structure-activity relationships of sets of compounds screened across multiple targets are discussed in light of the concept of activity landscape modeling.

2011

  • Lessons learned from molecular scaffold analysis
    Hu, Y and Stumpfe, D and Bajorath, J
    Journal of chemical information and\ldots}, 2011
     

2010

  • Bioactivity-Guided Navigation of Chemical Space
    Bon, Robin S and Waldmann, Herbert
    Accounts of chemical research, 2010, 43(8), 1103-1114
    PMID: 20481515     doi: 10.1021/ar100014h
     

  • Data structures and computational tools for the extraction of SAR information from large compound sets.
    Wawer, Mathias and Lounkine, Eugen and Wassermann, Anne M and Bajorath, Jürgen
    Drug discovery\ldots}, 2010, 15(15-16), 630-639
    PMID: 20547243     doi: 10.1016/j.drudis.2010.06.004
     
    Computational data mining and visualization techniques play a central part in the extraction of structure-activity relationship (SAR) information from compound sets including high-throughput screening data. Standard statistical and classification techniques can be used to organize data sets and evaluate the chemical neighborhood of potent hits; however, such methods are limited in their ability to extract complex SAR patterns from data sets and make them readily accessible to medicinal chemists. Therefore, new approaches and data structures are being developed that explicitly focus on molecular structure and its relationship to biological activity across multiple targets. Here, we review standard techniques for compound data analysis and describe new data structures and computational tools for SAR mining of large compound data sets.

  • Cheminformatics approaches to analyze diversity in compound screening libraries.
    Akella, Lakshmi B and DeCaprio, David
    Current opinion in chemical biology, 2010, 14(3), 325-330
    PMID: 20457001     doi: 10.1016/j.cbpa.2010.03.017
     
    As high-throughput screening matures as a discipline, cheminformatics is playing an increasingly important role in selecting new compounds for diverse screening libraries. New visualization techniques such as multi-fusion similarity maps, scaffold trees, and principal moments of inertia plots provide complementary information on compound libraries and enable identification of unexplored regions of chemical space with potential biological relevance. Quantitative metrics have been developed to analyze libraries for properties such as natural product-likeness and shape complexity. Analysis of high-throughput screening results and drug discovery programs identify compounds problematic for screening. Taken together these approaches allow us to increase the diversity of biological outcomes available in compound screening libraries and improve the success rates of high-throughput screening against new targets without making significant increases in the size of compound libraries.

2009

  • Chemical biology: Branching out into chemical space
    Harrison, Charlotte
    Nature reviews. Drug discovery, 2009, 8(8), 615-615
     

  • Staring off into chemical space
    Irwin, John J
    Nature chemical biology, 2009, 5(8), 536-537
    doi: 10.1038/nchembio0809-536
     
    New software to browse chemical space, with structures organized by rings, will enable chemical insight.

  • Systematic computational analysis of structure-activity relationships: concepts, challenges and recent advances
    Peltason, Lisa and Bajorath, Jürgen
    Future medicinal chemistry, 2009, 1(3), 451-466
    doi: 10.4155/fmc.09.41
     
    The exploration of structure-activity relationships (SARs) of small molecules is a central aspect of medicinal chemistry. Typically, SARs are analyzed on a one-by-one basis, and chemical intuition and experience play an important role in this process. Since the 1960s, computational approaches have been developed to aid in SAR exploration that largely, but not exclusively, rely on the quantitative (Q)SAR paradigm. Accordingly, QSAR analysis has long been a mainstay of compound optimization efforts. However, the strong compound class dependence of SAR features and their intrinsic heterogeneity often pose severe constraints on the applicability of these methods. In addition to QSAR approaches, conceptually different molecular similarity methods are also applied to identify novel active compounds. In order to complement and further extend the current repertoire of computational methods, SAR analysis functions have recently been introduced that evaluate and compare SAR features on a large scale, extract SAR in...