J Chem Inf Model - RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4.

Tópicos

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Resumo

This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as "metabolophores": A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set. The resulting pathway-independent (O-dealkylation vs N-oxidation vs Csp(3) hydroxylation, etc.), isozyme-specific regioselectivity model may be used to predict potential metabolic liabilities. In the present work, cross-validated RS-Predictor models were generated for a set of 394 substrates of CYP 3A4 as a proof-of-principle for the method. Rank aggregation was then employed to merge independently generated predictions for each substrate into a single consensus prediction. The resulting consensus RS-Predictor models were shown to reliably identify at least one observed site of metabolism in the top two rank-positions on 78% of the substrates. Comparisons between RS-Predictor and previously described regioselectivity prediction methods reveal new insights into how in silico metabolite prediction methods should be compared.

Resumo Limpo

articl describ regioselectivitypredictor rspredictor new silico method generat predict model pmediat metabol druglik compound within method potenti site metabol som repres metabolophor concept describ hierarch combin topolog quantum chemic descriptor need repres reactiv potenti metabol reaction site rspredictor model involv use metabolophor descriptor togeth multipleinst rank mirank generat optim descriptor weight vector encod regioselect trend across case train set result pathwayindepend odealkyl vs noxid vs csp hydroxyl etc isozymespecif regioselect model may use predict potenti metabol liabil present work crossvalid rspredictor model generat set substrat cyp proofofprincipl method rank aggreg employ merg independ generat predict substrat singl consensus predict result consensus rspredictor model shown reliabl identifi least one observ site metabol top two rankposit substrat comparison rspredictor previous describ regioselect predict method reveal new insight silico metabolit predict method compar

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