Upjohn, Cousin compound information system

This paper proposes extensions to the relational database model that allow chemical structure and other complex data types to be included in a relational database. It is argued that this approach provides benefits over the common practice of storing chemical structures in a chemical database system and associated research data in a relational or other general database system. The design, implementation, and usage patterns of an extended relational system are discussed in the context of the Upjohn Cousin compound information system. Emerging extensibiUty features that support the proposed approach within commercially available database systems are reviewed. 

The evolution of molecular graphics (7) is described in an earlier ACS Symposium Series book (77) which acts as an interesting precursor to this present volume. Chemical reaction systems such as LHASA (Logic and Heuristics Applied to Synthetic Analysis) 18) and SECS (Simulation and Evaluation of Chemical Synthesis) (79) had long used graphics but it was some time before the first in-house, proprietary system appeared, attracting much interest in the chemical and pharmaceutical industries. This was Upjohn s Compound Information System, COUSIN 20-21). 

In the maximum dissimilarity (MD) selection method used at Pharmacia Upjohn [6], the first compound is selected at random and subsequent compounds are then chosen iteratively such that the distance to the nearest of the compounds already chosen is a maximum. The compounds are represented by COUSIN fragments [13]. Polinsky et al. [42] use a similar algorithm in the LiBrain system. In this case, the molecules are represented by a feature vector that contains information about the following affinity types aliphatic hydrophobic, aromatic hydrophobic, basic, acidic, hydrogen bond donor, hydrogen bond acceptor, and polarizable heteroatom. 

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