Read-across　has　become　a　primary　approach　to　fill　data　gaps　for　chemical　safety　assessments.　Chemical　similarity　based　on　structure,　reactivity,　and　physic-chemical　property　information　is　a　traditional　approach　applied　for　read-across　toxicity　studies.　However,　toxicity　mechanisms　are　usually　complicated　in　a　biological　system,　so　only　using　chemical　similarity　to　perform　the　read-across　for　new　compounds　was　not　satisfactory　for　most　toxicity　endpoints,　especially　when　the　chemically　similar　compounds　show　dissimilar　toxicities.　This　study　aims　to　develop　an　enhanced　read-across　method　for　chemical　toxicity　predictions.　To　this　end,　we　used　two　large　toxicity　datasets　for　read-across　purposes.　One　consists　of　3979　compounds　with　Ames　mutagenicity　data,　and　the　other　contains　7332　compounds　with　rat　acute　oral　toxicity　data.　First,　biological　data　for　all　compounds　in　these　two　datasets　were　obtained　by　querying　thousands　of　PubChem　bioassays.　The　PubChem　bioassays　with　at　least　five　compounds　from　either　of　these　two　datasets　showing　active　responses　were　selected　to　generate　comprehensive　bioprofiles.　The　read-across　studies　were　performed　by　using　chemical　similarity　search　only　and　also　by　using　a　hybrid　similarity　search　based　on　both　chemical　descriptors　and　bioprofiles.　Compared　to　traditional　read-across　based　on　chemical　similarity,　the　hybrid　read-across　approach　showed　improved　accuracy　of　predictions　for　both　Ames　mutagenicity　and　acute　oral　toxicity.　Furthermore,　we　could　illustrate　potential　toxicity　mechanisms　by　analyzing　the　bioprofiles　used　for　this　hybrid　read-across　study.　The　results　of　this　study　indicate　that　the　new　hybrid　read-across　approach　could　be　an　applicable　computational　tool　for　chemical　toxicity　predictions.　In　this　way,　the　bottleneck　of　traditional　read-across　studies　can　be　overcome　by　introducing　public　biological　data　into　the　traditional　process.　The　incorporation　of　bioprofiles　generated　from　the　additional　biological　data　for　compounds　can　partially　solve　the　＂activity　cliff＂　issue　and　reveal　their　potential　toxicity　mechanisms.　This　study　leads　to　a　promising　direction　to　utilize　data-driven　approaches　for　computational　toxicology　studies　in　the　big　data　era.