CaviDAC: Computational Prediction of Cavity Volumes in Calixarenes via Tessellation and Divide‐and‐Conquer Algorithms

Abstract Macrocyclic compounds enable diverse applications utilizing their cavity for guest encapsulation. Current approaches to cavity volume calculation in tunnel‐like cavities suffer from weak predictability due to mouth opening ambiguity. Here tessellation and divide‐and‐conquer approaches to the cavity volume prediction in calixarene macrocycles featuring convex‐hull based determination of boundaries of tunnel‐like cavities and a user‐friendly CaviDAC tool for academic researchers and specialists in chemo(bio)informatics are presented. The cavity volumes of the basket‐, barrel‐, and sandglass‐shaped calixarenes calculated by triangular tessellation and quickhull algorithms show the divergence of less than 3.0%. CaviDAC software outperforms common cavity volume calculation software in terms of accuracy in the cavitands with ill‐defined cavity opening, which is promising for the computational screening of novel materials with molecular‐level porosity.