A new computational cross-structure-activity relationship (C-SAR) approach applies to a selective HDAC6 inhibitor dataset for accelerated structure development.

Several structure-activity relationship (SAR) methodologies have been developed for the research community to improve the potential activity of prototype structures. To accomplish this, Topliss proposed the Topliss tree and the Topliss Batchwise scheme for structure development. Structure development necessitates tactics beyond traditional SAR procedures when handling issues, such as rapid structural inactivation during development. SAR data is vital for altering chemical structures and addressing compound problems. Obtaining unique SAR data that provides strategic options for structure transformation relevant to every chemotype and not limited to a specific parent structure, as the Topliss approach does, is challenging. In this context, we present the C-SAR strategy, which addresses these issues and accelerates structural development. The C-SAR method provides insights into converting an inactive compound into an active one. We used cheminformatics and molecular docking tools to study a chemical library of diverse chemotypes targeting HDAC6, arranging it in matched molecular pairs (MMPs) with high structural activity landscape index (SALI) values of 820880 and a diversity index of 0.5827 and identifying C-SAR highlights based on repetitive pharmacophoric substitution patterns across different MMP chemotypes that resulted in activity cliffs. C-SAR is beneficial for SAR expansion when high-quality structural data are available to study a dataset of various MMPs from a specific class of compounds and allows using the obtained C-SAR highlights to design compounds of novel chemotypes beyond the investigated dataset. Data imputation using deep-learning predictive models may address the issue of data availability for C-SAR.