The majority of biologically active compounds have both pharmacotherapeutic and side/toxic actions. To estimate general efficacy and safety of the molecules under study, their biological potential should be thoroughly evaluated. In an early stage of study, only information about structural formulae was available and was used as an input for computational prediction. Based on a structural formulae of compounds presented as SDF or MOL-files, computer program PASS predicts 900 pharmacological effects, mechanism of action, and specific toxicity. An average accuracy of prediction in leave-one-out cross-validation is about 85%. For evaluating new compounds, scientific community may use PASS via the Internet for free at URL: http://www.ibmh.msk.su/PASS. In the first 18 months of PASS Inet's use, approximately 1000 researchers from 60 countries have obtained predicted biological activity spectra for about 23,000 different chemical compounds. More than 64 million PASS predictions for almost 250,000 compounds from Open NCI database are available on the web site http://cactus.nci.nih.gov/ncidb2/. These predictions are used for selecting compounds with desirable and without unwanted types of biological activities among the NCI samples available for screening.

The program PASS-BioTransfo is presented, which is capable of predicting many classes of biotransformation for chemical compounds. A particular class of biotransformation is defined by the chemical transformation type and may additionally include the name of the enzyme involved in a transformation. An evaluation of the approach is presented, using biotransformations taken from the databases Metabolite (MDL) and Metabolism (Accelrys), respectively. When trained with biotransformations from Metabolite, PASS-BioTransfo predicts 1927 classes of biotransformation; the average accuracy estimated in LOO cross-validation is about 88%. After training with the biotransformations from the Metabolism database, 178 classes of biotransformation are predicted with an average accuracy of about 85%. The results of cross-prediction with several training and evaluation sets are presented and discussed.

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Computers in chemistryComputers in chemistry V 0380 Predicting Biotransformation Potential from Molecular Structure. -(BORODINA*, Y.; SADYM, A.; FILIMONOV, D.; BLINOVA, V.; DMITRIEV, A.; POROIKOV, V.; J. Chem. Inf.