Predictive machine learning models for rational permeability design in de novo macrocycle engineering: a review

Small molecule drug discovery has been highly successful across many therapeutic areas over decades of progress; however, many disease-relevant proteins remain difficult to target. In particular, intracellular proteins with large, shallow, or flexible interaction surfaces are poorly addressed by classical drug-like compounds. For these reasons, drug discovery efforts have shifted toward alternative molecular classes. This has led to growing interest in macrocyclic compounds in recent years, which have emerged as an important class of therapeutic molecules, particularly for targets that are out of reach for conventional small molecules. These compounds operate within the chemical space beyond Lipinski's Rule of Five (bRo5) and offer new opportunities for modulating difficult intracellular targets. At the same time, their size, flexibility, and structural complexity introduce significant challenges, among which the accurate prediction of membrane permeability remains one of the persistent limitations in their rational design, particularly for orally bioavailable candidates.