Activity cliffs are generally defined as pairs of structurally similar compounds having large differences in potency. The analysis of activity cliffs is of general interest because structure-activity relationship (SAR) determinants can often be deduced from them. Critical questions for the study of activity cliffs include how similar compounds should be to qualify as cliff partners, how similarity should be assessed, and how large potency differences between participating compounds should be. Thus far, activity cliffs have mostly been defined on the basis of calculated Tanimoto similarity values using structural descriptors, especially 2D fingerprints. As any theoretical assessment of molecular similarity, this approach has its limitations. For example, calculated Tanimoto similarities might often be difficult to reconcile and interpret from a chemical perspective, a point of critique frequently raised in medicinal chemistry. Herein, we have explored activity cliffs by considering well-defined substructure replacements instead of calculated similarity values. For this purpose, the matched molecular pair (MMP) formalism has been applied. MMPs were systematically derived from public domain compounds, and activity cliffs were extracted from them, termed MMP-cliffs. The frequency of cliff formation was determined for compounds active against different targets, MMP-cliffs were analyzed in detail, and re-evaluated on the basis of Tanimoto similarity. In many instances, chemically intuitive activity cliffs were only detected on the basis of MMPs, but not Tanimoto similarity.

Activity cliffs are formed by pairs of structurally similar compounds that act against the same target but display a significant difference in potency. Such activity cliffs are the most prominent features of activity landscapes of compound data sets and a primary focal point of structure-activity relationship (SAR) analysis. The search for activity cliffs in various compound sets has been the topic of a number of previous investigations. So far, activity cliff analysis has concentrated on data mining for activity cliffs and on their graphical representation and has thus been descriptive in nature. By contrast, approaches for activity cliff prediction are currently not available. We have derived support vector machine (SVM) models to successfully predict activity cliffs. A key aspect of the approach has been the design of new kernels to enable SVM classification on the basis of molecule pairs, rather than individual compounds. In test calculations on different data sets, activity cliffs have been accurately predicted using specifically designed structural representations and kernel functions.

In this study, Sr-doped lanthanum manganite perovskites (LaSrMnO, LSM) as electrode materials for supercapacitors were prepared via an improved sol-gel method. Among LSM, the x = 0.15 sample shows superior electrochemical performance, delivering the highest specific capacitance of 102 F g at a current density of 1 A g and lower intrinsic resistance in 1 M KOH. The effective charge storage of LSM is due to the redox reaction of the anion intercalation corresponding to the surface redox processes of Mn/Mn and Mn/Mn occurring within the electroactive materials. The maximum energy density of 3.6 W h kg was achieved at a power density of 120 W kg for the symmetric supercapacitor with a long cycling life after 5000 charging and discharging cycles. With the increase of cycle times, the element leaching phenomenon leads to the decrease of electrochemical performance. Our work indicated that the perovskite manganite LaSrMnO shows potential applications in the field of pseudocapacitance electrode materials and is worthy of further investigation.