COSMOS next generation - A public knowledge base leveraging chemical and biological data to support the regulatory assessment of chemicals.

The COSMOS Database (DB) was originally established to provide reliable data for cosmetics-related chemicals within the COSMOS Project funded as part of the SEURAT-1 Research Initiative. The database has subsequently been maintained and developed further into COSMOS Next Generation (NG), a combination of database and in silico tools, essential components of a knowledge base. COSMOS DB provided a cosmetics inventory as well as other regulatory inventories, accompanied by assessment results and in vitro and in vivo toxicity data. In addition to data content curation, much effort was dedicated to data governance - data authorisation, characterisation of quality, documentation of meta information, and control of data use. Through this effort, COSMOS DB was able to merge and fuse data of various types from different sources. Building on the previous effort, the COSMOS Minimum Inclusion (MINIS) criteria for a toxicity database were further expanded to quantify the reliability of studies. COSMOS NG features multiple fingerprints for analysing structure similarity, and new tools to calculate molecular properties and screen chemicals with endpoint-related public profilers, such as DNA and protein binders, liver alerts and genotoxic alerts. The publicly available COSMOS NG enables users to compile information and execute analyses such as category formation and read-across. This paper provides a step-by-step guided workflow for a simple read-across case, starting from a target structure and culminating in an estimation of a NOAEL confidence interval. Given its strong technical foundation, inclusion of quality-reviewed data, and provision of tools designed to facilitate communication between users, COSMOS NG is a first step towards building a toxicological knowledge hub leveraging many public data systems for chemical safety evaluation. We continue to monitor the feedback from the user community at support@mn-am.com.

Pluggable application server framework.

Building a system based on variants of disparate individual components/programs is usually a challenging task. The components/programs are not designed to communicate with each other but the whole system construction does require a seamless collaboration among them. In this paper, targeting at protein structure prediction, a pluggable application server framework is presented. The framework is capable of combining various existing programs into an efficient unit and the design is devoted to provide a model which is able to integrate heterogeneous components/programs into the system quickly without modifying their codes. Based on the model, different components can be plugged into the system with easy configuration, which would lead to a self-configurable and adaptive system. A protein structure prediction server implementation was developed by applying the design model and the real implementation emphasizes the efficiency and simplicity of the system construction. The method and model are generic and can be applied to other system design as well.

An approach in building a chemical compound search engine in oracle database.

A searching or identifying of chemical compounds is an important process in drug design and in chemistry research. An efficient search engine involves a close coupling of the search algorithm and database implementation. The database must process chemical structures, which demands the approaches to represent, store, and retrieve structures in a database system. In this paper, a general database framework for working as a chemical compound search engine in Oracle database is described. The framework is devoted to eliminate data type constrains for potential search algorithms, which is a crucial step toward building a domain specific query language on top of SQL. A search engine implementation based on the database framework is also demonstrated. The convenience of the implementation emphasizes the efficiency and simplicity of the framework.

Knowledge-based algorithms for chemical structure and property analysis.

We have successfully developed 'rule-based' algorithms that efficiently perform sub- and exact-structure searching, as well as accurately describe the chemistry of small molecules. These algorithms use a simple and concise set of rules for information extraction from molecule files. This design is intended to reduce the computational time required for the process, while improving the accuracy in the tasks. The performances of these algorithms have been successfully validated with a wide range of small molecules. Our future goal is to combine these algorithms with our newly designed knowledge-based object database, such that their tasks can be automated with a high efficiency.