Quantifying Biogenic Bias in Screening Libraries

In lead discovery, libraries of 106 molecules are screened for biological activity. Given the over 1060 drug-like molecules thought possible, such screens might never succeed. That they do, even occasionally, implies a biased selection of library molecules. Here a method is developed to quantify the bias in screening libraries towards biogenic molecules. With this approach, we consider what is missing from screening libraries and how they can be optimized.

[1]  A. Barabasi,et al.  Drug—target network , 2007, Nature Biotechnology.

[2]  Peter Ertl,et al.  Quest for the rings. In silico exploration of ring universe to identify novel bioactive heteroaromatic scaffolds. , 2006, Journal of medicinal chemistry.

[3]  G. Bemis,et al.  The properties of known drugs. 1. Molecular frameworks. , 1996, Journal of medicinal chemistry.

[4]  S. Wilhelm,et al.  Discovery and development of sorafenib: a multikinase inhibitor for treating cancer , 2006, Nature Reviews Drug Discovery.

[5]  Ricardo Macarron,et al.  Critical review of the role of HTS in drug discovery. , 2006, Drug discovery today.

[6]  Sunil Gupta,et al.  Comparing the chemical spaces of metabolites and available chemicals: models of metabolite-likeness , 2007, Molecular Diversity.

[7]  R. Spencer,et al.  High-throughput screening of historic collections: observations on file size, biological targets, and file diversity. , 1998, Biotechnology and bioengineering.

[8]  A. Schuffenhauer,et al.  Charting biologically relevant chemical space: a structural classification of natural products (SCONP). , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Jordi Mestres,et al.  Coverage and bias in chemical library design. , 2008, Current opinion in chemical biology.

[10]  Peter Ertl,et al.  Natural Product-likeness Score and Its Application for Prioritization of Compound Libraries , 2008, J. Chem. Inf. Model..

[11]  H. L. Morgan.  The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service. , 1965 .

[12]  Herbert Waldmann,et al.  From protein domains to drug candidates-natural products as guiding principles in the design and synthesis of compound libraries. , 2002, Angewandte Chemie.

[13]  D. Pereira,et al.  Origin and evolution of high throughput screening , 2007, British journal of pharmacology.

[14]  Y. Martin.  Diverse viewpoints on computational aspects of molecular diversity. , 2001, Journal of combinatorial chemistry.

[15]  Andrew C. Good,et al.  Measuring CAMD Technique Performance, 2. How "Druglike" Are Drugs? Implications of Random Test Set Selection Exemplified Using Druglikeness Classification Models , 2007, J. Chem. Inf. Model..

[16]  B. Roth,et al.  Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia , 2004, Nature Reviews Drug Discovery.

[17]  Jean-Louis Reymond,et al.  Virtual exploration of the small-molecule chemical universe below 160 Daltons. , 2005, Angewandte Chemie.

[18]  H. Kubinyi,et al.  A scoring scheme for discriminating between drugs and nondrugs. , 1998, Journal of medicinal chemistry.

[19]  Herbert Waldmann,et al.  Charting Biological and Chemical Space: PSSC and SCONP as Guiding Principles for the Development of Compound Collections Based on Natural Product Scaffolds , 2006 .

[20]  S. Schreiber,et al.  Target-oriented and diversity-oriented organic synthesis in drug discovery. , 2000, Science.

[21]  G. V. Paolini,et al.  Global mapping of pharmacological space , 2006, Nature Biotechnology.

[22]  W. Guida,et al.  The art and practice of structure‐based drug design: A molecular modeling perspective , 1996, Medicinal research reviews.

[23]  Tudor I. Oprea,et al.  Scaffold Topologies. 2. Analysis of Chemical Databases , 2008, J. Chem. Inf. Model..

[24]  Paul A Clemons,et al.  Mapping chemical space using molecular descriptors and chemical genetics: deacetylase inhibitors. , 2004, Combinatorial chemistry & high throughput screening.

[25]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[26]  S. Fox,et al.  High-Throughput Screening: Searching for Higher Productivity , 2004, Journal of biomolecular screening.

[27]  P. Willett,et al.  Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures. , 2004, Organic & biomolecular chemistry.

[28]  Brian K. Shoichet,et al.  ZINC - A Free Database of Commercially Available Compounds for Virtual Screening , 2005, J. Chem. Inf. Model..

[29]  A. Bender,et al.  Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to ADME. , 2006, IDrugs : the investigational drugs journal.

[30]  Stefan Wetzel,et al.  Cheminformatic Analysis of Natural Products and their Chemical Space , 2007 .

[31]  S. Wilhelm,et al.  Discovery and development of sorafenib: a multikinase inhibitor for treating cancer , 2007, Nature Reviews Drug Discovery.

[32]  F. Koehn,et al.  The evolving role of natural products in drug discovery , 2005, Nature Reviews Drug Discovery.

[33]  Jean-Louis Reymond,et al.  Virtual Exploration of the Chemical Universe up to 11 Atoms of C, N, O, F: Assembly of 26.4 Million Structures (110.9 Million Stereoisomers) and Analysis for New Ring Systems, Stereochemistry, Physicochemical Properties, Compound Classes, and Drug Discovery , 2007, J. Chem. Inf. Model..

[34]  Derek S. Tan,et al.  Advancing chemistry and biology through diversity-oriented synthesis of natural product-like libraries. , 2005, Current opinion in chemical biology.