Rational　nanomaterial　design　is　urgently　demanded　for　new　nanomaterial　development　with　desired　properties.　However,　computational　nanomaterial　modeling　and　virtual　nanomaterial　screening　are　not　applicable　for　this　purpose　due　to　the　complexity　of　nanomaterial　structures.　To　address　this　challenge,　a　new　computational　workflow　is　established　in　this　study　to　virtually　profile　nanoparticles　by　(1)　constructing　a　structurally　diverse　virtual　gold　nanoparticle　(GNP)　library　and　(2)　developing　novel　universal　nanodescriptors.　The　emphasis　of　this　study　is　the　second　task　by　developing　geometrical　nanodescriptors　that　are　suitable　for　the　quantitative　modeling　of　GNPs　and　virtual　screening　purposes.　The　feasibility,　rigor　and　applicability　of　this　novel　computational　method　are　validated　by　testing　seven　GNP　datasets　consisting　of　191　unique　GNPs　of　various　nano-bioactivities　and　physicochemical　properties.　The　high　predictability　of　the　developed　GNP　models　suggests　that　this　workflow　can　be　used　as　a　universal　tool　for　nanomaterial　profiling　and　rational　nanomaterial　design.