Exploring activity landscapes with extended similarity: is Tanimoto enough?

Understanding structure-activity landscapes is essential in drug discovery. Similarly, it has been shown that the presence of activity cliffs in compound data sets can have a substantial impact not only on the design progress but also can influence the predictive ability of machine learning models. With the continued expansion of the chemical space and the currently available large and ultra-large libraries, it is imperative to implement efficient tools to analyze the activity landscape of compound data sets rapidly. The goal of this study is to show the applicability of the n-ary indices to quantify the structure-activity landscapes of large compound data sets using different types of structural representation rapidly and efficiently. We also discuss how a recently introduced medoid algorithm provides the foundation to finding optimum correlations between similarity measures and structure-activity rankings. The applicability of the n-ary indices and the medoid algorithm is shown by analyzing the activity landscape of 10 compound data sets with pharmaceutical relevance using three fingerprints of different designs, 16 extended similarity indices, and 11 coincidence thresholds.

Automated summary

"Understanding structure-activity landscapes" emphasizes the importance of the relationship between molecular structure and activity in drug discovery, and highlights the impact of activity cliffs on design and prediction. This study aims to use n-ary indices and the medoid algorithm to rapidly and efficiently quantify the structure-activity landscapes of large compound data sets, and explore the optimal correlations between similarity measures and structure-activity rankings. The applicability of these methods is demonstrated by analyzing 10 compound data sets with pharmaceutical relevance using different fingerprints, similarity indices, and coincidence thresholds.