The concept of matched (molecular) series was introduced by Wawer and Bajorath in 2011 [1]. It is the general form of a matched pair, namely, a set of two or more molecules with the same scaffold but different R groups at the same position.While matched pairs have been successfully used to predict physicochemical properties, their performance with respect to biological activity prediction has been more limited. The reason is straightforward; in one biological context changing group A to group B may increase the activity while in another binding site context it may decrease the activity.In contrast, longer matched series incorporate the biological context implicitly in the form of observed activity trends for R groups. For example, if we know that the chlorine analogue is more active than the fluorine, then this gives us improved information on whether the bromine analogue is likely to be more active than the chlorine.We present the �Matsy� algorithm [2] which uses an existing source of activity data (e.g. the ChEMBLdb, or an internal database) to make predictions for what R group is likely to increase activity given an observed activity order for measured R groups. We show that the basis for predictive success is preferred orders in matched series and that this preference is stronger for longer series. We describe a large-scale retrospective test to measure performance, comparing the performance of series of different lengths.The Matsy algorithm allows medicinal chemists to integrate activity trends from a diverse set of medicinal chemistry programmes and apply it to problems of interest as a Topliss-like recommendation or as a hypothesis generator to aid compound design.[1] Wawer, M.; Bajorath, J. J. Med. Chem. 2011, 54, 2944.[2] O'Boyle, N.M.; Bostr�m, J.; Sayle, R.A.; Gill, A. J. Med. Chem. 2014, Accepted.

Using matched series to predict R groups that improve biological activity

248th National Meeting (2014)

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