A network-based protocol to prioritize compounds for biological testing: discovery of Anti-Staphylococcus aureus cacalol derivatives

The integration of computational tools into early drug discovery has facilitated the rational prioritization of bioactive compounds. In this study, we present a general semi-automated, network-based protocol designed to guide the biological testing of in-house compounds by leveraging chemical similarity and reported bioactivity data. As a proof of concept, we constructed a curated database of 127,134 compounds with documented activity against Staphylococcus aureus strains. We used this resource to map a biologically relevant chemical space that includes cacalol derivatives, which allowed the identification of two in-house cacalol analogues with predicted anti- S. aureus activity. These compounds were selected based on pairwise similarity analysis using the ECFP4 fingerprint and the Tanimoto coefficient as the molecular descriptor and similarity metric, respectively. The selected compounds were subsequently validated in vitro using disk diffusion and resazurin-based microdilution assays, confirming consistent antibacterial activity (20 mM; 7–10 mm of inhibition zones and 1–2 fold reduction in metabolism activity, respectively). This study aims to bridge the gap between in silico and wet-lab approaches, enhancing rapid and intelligent screening to prioritize the biological evaluation of in-house compounds.