D952 Nucleic Acids Research, 2012, Vol. 40, Database issue search resulting in a number of homologous targets with screening data. The bioactivity data are ﬁltered to obtain the most active compounds (Figure 4B) and the resulting compounds can be viewed and exported (Figure 4C). In the selection of a tool compound, as well as level of activity, molecular diversity and like- ly cross-reactivity are taken into account. Molecular di- versity can be obtained from the Bemis and Murcko frameworks, and cross reactivity can be visualized using the compound bioactivity proﬁle plots (Figure 4D) Identifying potential druggable intervention points from a protein interaction network annotated with chemical and biological data A common problem encountered in drug discovery is iden- tifying likely chemical intervention points in a pathway or Figure 4. Identiﬁcation of tool compounds for ABL1. Panel (A) shows the search results from a sequence similarity search of ABL1. Only homo- logues with percentage identities >50% are selected to increase the chance of ﬁnding an active against ABL1 itself. Panel (B) shows the chemical space of good afﬁnity compounds against ABL1 and selected homologues. Panel (C) displays only sub micromolar afﬁnity compound structures. A search instigated using these compounds allows identiﬁcation of likely selectivity across homologues. Panel (D) shows the bioactivity proﬁle of these compounds. Compound identiﬁers are on the y-axis and target names on the x-axis. A blue dot represents a measured compound bioactivity and the darker the dot the higher the afﬁnity of the compound against the target. This proﬁle can be used to indicate the selectivity of the compounds against measured targets.