LIBRARY DESIGN, IN SILICO EXPERIMENTAL DIVERGENCE in .NET framework

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LIBRARY DESIGN, IN SILICO EXPERIMENTAL DIVERGENCE
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Library design viewed as a computational process and the experimental implementation of a library design can result in quite different outcomes. In a library design one might consider a core template that can be modi ed by a variety of substituent groups. The physicochemical pro le of the library is determined by the chemical structure of the core and the range of physicochemical properties of the substituent groups. For example, there might be only one core in a design but many substituent groups. As a result the physicochemical envelope of the library is very much in uenced both by the availability of the chemical pieces that will become the substituents
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library design, in silico experimental divergence
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and by the physicochemical properties of those substituents. If the experimental implementation (synthesis) of a design is 100% successful, then the actual experimental physicochemical envelope pro le is the same as in the library design. In actuality the chemical synthesis is seldom 100% successful. So what matters is whether there is any bias in the experimental synthesis success rate such that the experimental library differs signi cantly from the design library. A priori, the chemical synthesis success rate is usually not known, so in general, libraries are overdesigned. More compounds are designed than will actually ever be synthesized. The experimental synthesis success rate almost always biases the experimental library, so the physicochemical pro le relative to aqueous solubility is signi cantly worse than that in the design library. The fewest chemistry problems are found in lipophilic substituent moieties lacking polar functionality. In almost all cases polar functionality is electron withdrawing, so reactions of a substituent moiety like reactive amination, acylation, and nucleophilic substitution proceed poorly. Blocking and deblocking of a polar group adds to the complexity and length of a synthesis. As a result polar reagents that require blocking and deblocking are experimentally selected against. Robotic pipettors perform poorly or not at all on slurries of precipitates, so any factor that increases the insolubility of a reagent in an organic solvent will bias the library outcome. 16.9.1. Two Causes of Poor Solubility
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Poor solubility in an organic solvent arises from two quite different factors: solvation energy and disruption of intermolecular crystal packing forces in the crystalline reagent. Solvation of a lipophilic reagent in an organic solvent is typically not a problem. But disruption of intermolecular crystal packing forces is very much a problem in an organic solvent, especially if the reagent has a high melting point. This type of problem is most likely to be present in a reagent with polar hydrogen bond acceptor/ donor functionality. Thus the reagent insolubility problem tends to bias a library toward a more lipophilic and hence more aqueous insoluble pro le. To accommodate diversity considerations, a range of substituent moieties is selected. A large structural range translates into a broad molecular weight distribution. The combination of reagent solubility and diversity considerations results in an experimental library that is biased toward higher lipophilicity and higher molecular weight relative to the design library. The bias occurs because high lipophilicity and high molecular weight are the worst combination of rule of 5 parameters in terms of leading to poor aqueous solubility.
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426 16.9.2.
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solubility in the design of combinatorial libraries Library Design Importance of the Rate-Determining Step
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Effectiveness of designing for adequate aqueous solubility depends on whether chemistry protocol development or chemistry production is rate determining. If chemistry production is rate determining, there will be excess validated protocols relative to library production. This means that protocols can be prioritized as to the attractiveness of the compound solubility pro le and the least attractive protocols from a solubility perspective may never be translated into actual library production. However, often protocol development and not library production is the rate-determining step. This eventuality is unfortunate because there is an understandable reluctance to discontinue chemistry synthetic efforts due to poor experimental solubility pro le if considerable chemistry effort has already been expended. Consider the following situation. The effort toward library production is 70% complete. The experimental solubility pro le is poor. Would you discontinue completion of library synthesis because of poor solubility if 70% of the chemistry effort had already been completed So a key issue becomes how much chemistry experimental effort takes place before exemplars are experimentally pro led in solubility screens If protocol development is rate determining, the effectiveness of experimental solubility assays depends on how the early exemplars are synthesized. In theory, the most effective method would be to obtain a well-spaced subset of the library in an experimental design sense. A traditional noncombinatorial synthesis would accomplish this but would not t in well with a combinatorial optimization process. A possible way around this problem is to institute some type of early automated cleanup of combinatorial exemplars from partially optimized reaction schemes. This is not a tidy solution because the most ef cient process would be an automated cleanup on the entire library after the optimization process was complete. The least effective method of providing samples for experimental solubility pro ling is a late-stage selection from the optimized combinatorial libraries. It is least effective, and not because of chemistry ef ciency considerations. A late-stage selection from the optimized combinatorial libraries is actually chemistry ef cient. However, the inef ciency comes from the people aspect. The data comes too late to prevent poor solubility compounds from being made. The timing problem in obtaining combinatorial exemplars is one of the driving forces that makes computational solubility pro ling so attractive. Poor Solubility Business Aspects There is a business aspect here for a company selling combinatorial libraries if the combinatorial libraries contain compounds experimentally veri ed to
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