Structure-based　drug　design　(SBDD)　accelerates　drug　discovery　but　traditionally　relies　on　labor-intensive,　simulation-based　methods.　Deep　generative　models　offer　a　data-driven　alternative,　but　the　currently　prevalent　ligand-based　models　are　often　constrained　by　the　availability　of　active　compounds.　Here,　we　present　a　new　SO(3)-equivariant　generative　model　for　SBDD,　using　a　pseudo-ligand　point-cloud　representations　of　protein　cavities　to　optimize　ligands　and　generate　stable　3D　molecules.　Our　model　accurately　models　the　chemical　space　of　the　protein-binding　compounds.　We　evaluated　our　model　on　three　therapeutic　targets:　Janus　kinase　2　(JAK2),　peptidylprolyl　isomerase　(hPin1),　and　Mycobacterium　tuberculosis　malate　synthase.　Our　model　successfully　rediscovered　moieties　involved　in　key　interaction　with　the　proteins　and　proposed　alternative　moieties　with　bioactivity　supported　by　the　literature　among　the　highly　ranked　generated　samples.　This　work　approach　offers　a　new　way　to　ligand　optimization　and　drug　discovery,　advancing　the　field　of　public　health　science　by　enhancing　the　precision　and　efficiency　of　molecular　design　in　therapeutic　development.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.