To be useful, a system which predicts the metabolic fate of a chemical should predict the more likely metabolites rather than every possibility. Reasoning can be used to prioritize biotransformations, but a real biochemical domain is complex and cannot be fully defined in terms of the likelihood of events. This paper describes the combined use of two models for reasoning under uncertainty in a working system, METEOR-one model deals with absolute reasoning and the second with relative reasoning.

In this paper we describe Zeneth, a new expert computational system for the prediction of forced degradation pathways of organic compounds. Intermolecular reactions such as dimerization, reactions between the query compound and its degradants, as well as interactions with excipients can be predicted. The program employs a knowledge base of patterns and reasoning rules to suggest the most likely transformations under various environmental conditions relevant to the pharmaceutical industry. Building the knowledge base is facilitated by data sharing between the users.

Computers in chemistryComputers in chemistry V 0380 Using Absolute and Relative Reasoning in the Prediction of the Potential Metabolism of Xenobiotics. -(BUTTON, W. G.; JUDSON*, P. N.; LONG, A.; VESSEY, J. D.; J. Chem. Inf. Comput. Sci. 43 (2003) 5, 1371-1377; Dep. Chem., Univ. Leeds, Leeds LS2 9JT, UK; Eng.) -Lindner 50-230

A 'proof-of-concept' version of a software tool for making transparent predictions of acute aquatic toxicity has been developed. It is primarily limited to semi-quantitative predictions in one species, the ciliated protozoan, Tetrahymena pyriformis. A freely available system, 'Eco-Derek', was derived by adapting a well-established, knowledge-based structure-activity and reasoning platform (Derek for Windows, Lhasa Limited). The Derek reasoning code was modified to express potency rather than confidence. Structure-activity relationship (SAR) development utilised a curated version of a published dataset, supplemented with the CADASTER Challenge datasets. Forty-five structural alerts were produced. The dependence on log P was examined for each alert and entered into the system as qualitative reasoning rules specifying the predicted potency as Very Low, Low, Moderate, High or Very High. Evaluation studies showed: (a) moderate accuracy for the training set but low accuracy for an external test set; (b) non-linearity in the toxicity-log P relationship for chemicals without identified structural alerts; (c) insufficient differentiation of substituent effects in some of the reactivity-based structural alerts resulting in too few chemicals predicted with Very High toxicity; and (d) the need for additional structural alerts covering polar narcosis and less common reactive or metabolically activated chemical functionality.