Discovery informatics: its evolving role in drug discovery.

Drug discovery and development is a highly complex process requiring the generation of very large amounts of data and information. Currently this is a largely unmet informatics challenge. The current approaches to building information and knowledge from large amounts of data has been addressed in cases where the types of data are largely homogeneous or at the very least well-defined. However, we are on the verge of an exciting new era of drug discovery informatics in which methods and approaches dealing with creating knowledge from information and information from data are undergoing a paradigm shift. The needs of this industry are clear: Large amounts of data are generated using a variety of innovative technologies and the limiting step is accessing, searching and integrating this data. Moreover, the tendency is to move crucial development decisions earlier in the discovery process. It is crucial to address these issues with all of the data at hand, not only from current projects but also from previous attempts at drug development. What is the future of drug discovery informatics? Inevitably, the integration of heterogeneous, distributed data are required. Mining and integration of domain specific information such as chemical and genomic data will continue to develop. Management and searching of textual, graphical and undefined data that are currently difficult, will become an integral part of data searching and an essential component of building information- and knowledge-bases.

[1]  Laura M. Haas,et al.  DiscoveryLink: A system for integrated access to life sciences data sources , 2001, IBM Syst. J..

[2]  G.E. Moore,et al.  Cramming More Components Onto Integrated Circuits , 1998, Proceedings of the IEEE.

[3]  J M Blaney,et al.  Computational approaches for combinatorial library design and molecular diversity analysis. , 1997, Current opinion in chemical biology.

[4]  Christian Lemmen,et al.  Computational methods for the structural alignment of molecules , 2000, J. Comput. Aided Mol. Des..

[5]  Singh,et al.  Quantitative structure-property relationships in pharmaceutical research - Part 1. , 2000, Pharmaceutical science & technology today.

[6]  David C. Spellmeyer,et al.  Chapter 28. Recent Developments in Molecular Diversity: Computational Approaches to Combinatorial Chemistry , 1999 .

[7]  Grover,et al.  Quantitative structure-property relationships in pharmaceutical research - Part 2. , 2000, Pharmaceutical science & technology today.

[8]  Robert P. Sheridan,et al.  Flexibases: A way to enhance the use of molecular docking methods , 1994, J. Comput. Aided Mol. Des..

[9]  D. Underwood,et al.  Advances in automated docking applied to human immunodeficiency virus type 1 protease. , 1994, Methods in Enzymology.

[10]  Jennifer L. Miller,et al.  Combinatorial Library Design: Maximizing Model-Fitting Compounds within Matrix Synthesis Constraints , 2000, J. Chem. Inf. Comput. Sci..

[11]  Shivakumar Vaithyanathan,et al.  Model Selection in Unsupervised Learning with Applications To Document Clustering , 1999, International Conference on Machine Learning.

[12]  E. Fluder,et al.  Latent semantic structure indexing (LaSSI) for defining chemical similarity. , 2001, Journal of medicinal chemistry.

[13]  Robert P. Sheridan,et al.  Chemical Similarity Using Geometric Atom Pair Descriptors , 1996, J. Chem. Inf. Comput. Sci..

[14]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings , 1997 .

[15]  P. Beroza,et al.  A rapid computational method for lead evolution: description and application to alpha(1)-adrenergic antagonists. , 2000, Journal of medicinal chemistry.

[16]  John M. Barnard,et al.  Clustering of chemical structures on the basis of two-dimensional similarity measures , 1992, J. Chem. Inf. Comput. Sci..

[17]  Robert D. Brown Descriptors for diversity analysis , 1996 .

[18]  Andrew Rusinko,et al.  Optimization of focused chemical libraries using recursive partitioning. , 2002, Combinatorial chemistry & high throughput screening.

[19]  Robert P. Sheridan,et al.  Designing targeted libraries with genetic algorithms. , 2000, Journal of molecular graphics & modelling.

[20]  R D Hull,et al.  Chemical similarity searches using latent semantic structural indexing (LaSSI) and comparison to TOPOSIM. , 2001, Journal of medicinal chemistry.

[21]  Robert P. Sheridan,et al.  FLOG: A system to select ‘quasi-flexible’ ligands complementary to a receptor of known three-dimensional structure , 1994, J. Comput. Aided Mol. Des..

[22]  Aris Floratos,et al.  Combinatorial pattern discovery in biological sequences: The TEIRESIAS algorithm [published erratum appears in Bioinformatics 1998;14(2): 229] , 1998, Bioinform..

[23]  Jianchang Mao,et al.  Hierarchical Bayes for Text Classification , 2000, PRICAI Workshop on Text and Web Mining.

[24]  R. Sheridan,et al.  SQ: a program for rapidly producing pharmacophorically relevent molecular superpositions. , 1999, Journal of medicinal chemistry.