Kurisaki, I.; Hamada, M.

RNA plays vital roles in diverse biological processes, thus drawing much attention as potential drug target. Structure Based Drug Design (SBDD) for RNA is a promising approach but available RNA-ligand complex tertiary structures are substantially limited so far. Then, theoretical RNA-ligand docking simulations play central roles. However, success of SBDD highly depends on use of RNA structure sufficiently close to ligand-boundable conformations, which do not necessarily appear in experimentally-resolved and theoretically sampled apo RNA conformations. To overcome such difficulty in SBDD for RNA, we leverage efficiently sampling ligand-boudable apo RNA conformations by using a generative deep learning model (DL). We succeeded to generate HIV-1 Transactivation Response Element (TAR) conformations with a cryptic MV2003 binding cavity without a priori knowledge of the cavity. These conformations bind to MV2003 with binding scores similar to those calculated for experimentally-resolved TAR-MV2003 complexes, illustrating new application of DL to promote SBDD for RNA.