AI-ENABLED MOLECULAR MODELLING OF MACROCYCLIC COMPOUNDS IN DRUG DISCOVERY – USAGE AND CHALLENGES

Despite macrocyclic compounds having great potential in drug discovery, their designs still very challenging both synthetically and computationally. Molecular modelling of macrocycles poses significant challenges due to the flexibility of their ring structure, necessitating advantage beyond standard small-molecule Docking approaches to adequately address conformational variability. To manage this complexity, researchers often generate conformers before Docking or employ flexible Docking methods to explore bioactive conformations and predict interactions with protein targets comprehensively. Although these challenges persist, such methodology remains essential for structure-based drug development because it enhances stability, permeability and binding affinity in the design of new therapeutic agents. Software like AutoDock, Glide, and GOLD effectively simulate most organic molecules, whether rigid or flexible. Here we are generating a basic roadmap for the docking of macrocyclic compounds.