Mining　functional　modules　in　Protein-Protein　Interaction　(PPI)　networks　is　a　very　important　research　for　revealing　the　structure-functionality　relationships　in　biological　processes.　More　recently,　some　swarm　intelligence　algorithms　have　been　successfully　applied　in　the　field.　This　paper　presents　a　new　nature-inspired　approach,　ACC-FMD,　which　is　based　on　ant　colony　clustering　to　detect　functional　modules.　First,　some　proteins　with　the　higher　clustering　coefficients　are,　respectively,　selected　as　ant　seed　nodes.　And　then,　the　picking　and　dropping　operations　based　on　ant　probabilistic　models　are　developed　and　employed　to　assign　proteins　into　the　corresponding　clusters　represented　by　seeds.　Finally,　the　best　clustering　result　in　each　generation　is　used　to　perform　the　information　transmission　by　updating　the　similarly　function.　Experimental　results　on　some　benchmarked　datasets　show　that　ACC-FMD　outperforms　the　CFinder　and　MCODE　algorithms　and　has　comparative　performance　with　the　MINE,　COACH,　DPClus　and　Core　algorithms　in　terms　of　the　general　evaluation　metrics.