Drug　discovery　is　a　costly　process　which　usually　takes　more　than　10　years　and　billions　of　dollars　for　one　successful　drug　to　enter　the　market.　Despite　all　the　safety　tests,　drugs　may　still　cause　adverse　reactions　and　be　restricted　in　use　or　even　withdrawn　from　the　market.　Drug-induced　liver　injury　(DILI)　is　one　of　the　major　adverse　drug　reactions,　and　computational　models　may　be　used　to　predict　and　reduce　it.　To　assess　the　computational　prediction　performance　of　DILI,　we　curated　DILI　endpoints　from　three　databases　and　prepared　drug　features　including　chemical　descriptors,　therapeutic　classifications,　gene　expressions,　and　binding　proteins.　We　trained　machine-learning　models　to　predict　the　various　DILI　endpoints　using　different　drug　features.　Using　the　optimal　feature　sets,　the　top-performing　models　obtained　areas　under　the　receiver　operating　characteristic　curve　(AUC)　around　0.8　for　some　DILI　endpoints.　We　found　that　some　features,　including　therapeutic　classifications　and　proteins,　have　good　prediction　performance　towards　DILI.　We　also　discovered　that　the　severity　of　DILI　endpoints　as　well　as　the　selection　of　negative　samples　may　significantly　affect　the　prediction　results.　Overall,　our　study　provided　a　comprehensive　collection,　curation,　and　prediction　of　DILI　endpoints　using　various　drug　features,　which　may　help　the　drug　researchers　to　better　understand　and　prevent　DILI　during　the　drug　discovery　process.