Optimization of the Biological Activity of Combinatorial Compound Libraries by a Genetic Algorithm

Weber, Lutz; Wallbaum, Sabine; Broger, Clemens; Gubernator, K.

doi:10.1002/anie.199522801

Cited by 187 publications

(139 citation statements)

References 9 publications

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“…The Eu, Si, and Ca solutions were prepared by dissolving Eu 2 O 3 (0.1 mol), Si(C 2 H 5 O) 4 -(TEOS) (0.5 mol), and CaCO 3 (0.5 mol) in nitric acid. The Mg, Sr, Ba, and B solutions were prepared by dissolving Mg(NO 3 ) 2´6 H 2 O (0.5 mol), Sr(NO 3 ) 2 (0.5 mol), Ba(NO 3 ) 2 (0.25 mol), and H 3 BO 3 (0.5 mol) in deionized water.…”

Section: Methodsmentioning

confidence: 99%

A Search for New Red Phosphors Using a Computational Evolutionary Optimization Process

Sohn¹,

Lee²,

Shin³

2003

Advanced Materials

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A computational evolutionary optimization process is adopted to screen a Eu3+‐doped alkali earth borosilicate system in an attempt to search for red phosphors with a high luminescent efficiency when excited by soft ultraviolet irradiation. The evolutionary optimization process involves a genetic algorithm and combinatorial chemistry, which was tailored for the development of light‐emitting diode (LED) phosphors. Vertical simulations and an actual synthesis have been carried out in the present investigation, and promising red phosphors for three‐band white LED applications have been obtained.

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Section: Methodsmentioning

confidence: 99%

A Search for New Red Phosphors Using a Computational Evolutionary Optimization Process

Sohn¹,

Lee²,

Shin³

2003

Advanced Materials

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“…Recently we have introduced the application of genetic algorithms to solve complex multi dimensional problems in MCR chemistry. 31 Based on automated parallel synthesis that assures exactness and consciousness of the preparative execution and high-throughput LC we have now performed a genetic algorithm (GA) driven optimization of MCR reaction conditions ( Figure 4). …”

Section: Optimisation Of Multi Component Reactionsmentioning

confidence: 99%

Discovery of New Multi Component Reactions with Combinatorial Methods

Weber

Illgen

Almstetter

1999

Synlett

Self Cite

578

191

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Abstract:The discovery of novel chemical reactions or reaction sequences that are able to generate useful chemical products may be regarded as the heart of organic chemistry. We present here concepts and methods on how to find and explore new multi component reactions, especially with automated combinatorial methods. This "combinatorial reaction finding" provides also a powerful tool to the understanding of the rules of organic chemistry, especially structure-reactivity relationships.

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“…An alternative computer-based approach minimizing the number of compounds to be synthesized is the iterative use of genetic algorithms [139], [140] in library design. Here, in order to find biologically active compounds from a virtual library of hundred thousand compounds only a small number of randomly selected compounds are synthesized, followed by biological evaluation.…”

Section: Iterative Optimization Methodsmentioning

confidence: 99%

Combinatorial Chemistry

Tiebes

Peters

2000

Ullmann's Encyclopedia of Industrial Chemistry

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The article contains sections titled: 1. Introduction 2. Concept of Combinatorial Chemistry 3. Methods and Techniques of Combinatorial Synthesis 3.1. Synthetic Strategies Towards Combinatorial Libraries 3.1.1. Split ‐ Pool Synthesis Towards Combinatorial Libraries 3.1.1.1. Techniques using Resin Beads 3.1.1.2. Tea‐Bag Method 3.1.2. Parallel Synthesis Towards Combinatorial Libraries 3.1.2.1. Reaction Apparatus using Resin Beads 3.1.2.2. Multipin Technique 3.1.2.3. Spatially Addressable Parallel Synthesis on Silica Wafer 3.1.3. Reagent Mixture Synthesis Towards Combinatorial Libraries 3.2. Synthetic Methodology for Organic Library Construction 3.2.1. Solid‐Phase Organic Synthesis 3.2.1.1. Solid Support, Linker, and Cleavage Strategies 3.2.1.2. Reaction Portfolio 3.2.2. Synthesis in Solution and Liquid‐Phase Synthesis 4. Characterization of Combinatorial Libraries 4.1. Analytical Characterization 4.2. Hit Identification in Combinatorial Libraries by High‐Throughput Screening 4.2.1. Strategies for Libraries of Compound Mixtures 4.2.1.1. On‐Bead Screening 4.2.1.2. Deconvolution 4.2.1.2.1. Iterative Deconvolution 4.2.1.2.2. Deconvolution by Positional Scanning 4.2.1.2.3. Deconvolution by Orthogonal Libraries 4.2.1.3. Encoding 4.2.1.4. Multiple Cleavable Linkers 4.2.2. Strategies for Libraries of Separate Single Compounds 4.2.3. Conclusion 5. Automation and Data Processing 5.1. Synthesis Automation and Data Processing 5.2. Automated Purification 6. Library Design and Diversity Assessment 6.1. Diversity Assessment for Selection of Building Blocks or Compound 6.2. Iterative Optimization Methods 7. Economic Aspects and Industrial Case Studies 8. Further Developments 8.1. Combinatorial Chemistry and Catalysis 8.2. Combinatorial Chemistry and Material Science

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Optimization of the Biological Activity of Combinatorial Compound Libraries by a Genetic Algorithm

Cited by 187 publications

References 9 publications

A Search for New Red Phosphors Using a Computational Evolutionary Optimization Process

A Search for New Red Phosphors Using a Computational Evolutionary Optimization Process

Discovery of New Multi Component Reactions with Combinatorial Methods

Combinatorial Chemistry

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