Abstract
Multiparameter optimization (MPO) scoring functions are popular tools for providing guidance on how to design desired molecules in medicinal chemistry. The utility of a new probabilistic MPO (pMPO) scoring function method and its application as a scoring function for CNS drugs are described in this letter. In this new approach, a minimal number of statistically determined empirical boundaries is combined with the probability distribution of the desired molecules to define desirability functions. This approach attempts to minimize the number of parameters that define MPO scores while maintaining a high level of predictive power. Results obtained from a test-set of orally approved drugs show that the pMPO approach described here can be used to separate desired molecules from undesired ones with accuracy comparable to a Bayesian model with the advantage of better human interpretability. The application of this pMPO approach for blood-brain barrier penetrant drugs is also described.
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