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Providing a rationale that associates a chemical structure of an odorant to its induced perception has been sought for a long time. To achieve this, a detailed atomic structure of both the odorant and the olfactory receptor must be known. State-of-the-art techniques to model the 3D structure of an olfactory receptor in complex with various odorants are presented here. These range from sequence alignment with known structures to molecular dynamics simulations in a realistic environment.

Processing text from scientific literature has become a necessity due to the burgeoning amounts of information that are fast becoming available, stemming from advances in electronic information technology. We created a program, NeuroText ( http://senselab.med.yale.edu/textmine/neurotext.pl ), designed specifically to extract information relevant to neuroscience-specific databases, NeuronDB and CellPropDB ( http://senselab.med.yale.edu/senselab/ ), housed at the Yale University School of Medicine. NeuroText extracts relevant information from the Neuroscience literature in a two-step process: each step parses text at different levels of granularity. NeuroText uses an expert-mediated knowledge base and combines the techniques of indexing, contextual parsing, semantic and lexical parsing, and supervised and non-supervised learning to extract information. The constrains, metadata elements, and rules for information extraction are stored in the knowledge base. NeuroText was created as a pilot project to process 3 years of publications in Journal of Neuroscience and was subsequently tested for 40,000 PubMed abstracts. We also present here a template to create domain non-specific knowledge base that when linked to a text-processing tool like NeuroText can be used to extract knowledge in other fields of research.