Abstract
The SLC6 transporter family serves as an important source of therapeutic targets, particularly in CNS disorders. Although advances in structural biology have rapidly expanded our knowledge of these transporters, rational drug design remains challenging due to their dynamic nature and a highly conserved orthosteric binding pocket. Consequently, in silico methods have become irreplaceable tools in the design of SLC6 inhibitors. This review summarizes computational methods applied to the discovery of SLC6 transporter inhibitors between January 2020 and August 2026. Particular attention is paid to approaches that have enabled the identification of novel chemotypes or helped address key challenges in the rational design of SLC6-targeting drugs, including ligand selectivity, transporter conformational dynamics, or allosteric modulation. The authors identified literature using the search tools PubMed, Embase, and Web of Science. Future SLC6 drug discovery will likely rely on integrated computational workflows combining LBDD, SBDD, and AI to enable the design of selective and conformation-specific inhibitors. These advances may accelerate early-stage drug discovery by improving hit identification, hopefully facilitating the development of safer and more effective SLC6 inhibitors.