Machine　intelligence,　which　is　normally　presented　as　artificial　intelligence,　refers　to　the　intelligence　exhibited　by　computers.　In　the　history　of　rational　drug　discovery,　various　machine　intelligence　approaches　have　been　applied　to　guide　traditional　experiments,　which　are　expensive　and　time-consuming.　Over　the　past　several　decades,　machine-learning　tools,　such　as　quantitative　structure　activity　relationship　(QSAR)　modeling,　were　developed　that　can　identify　potential　biological　active　molecules　from　millions　of　candidate　compounds　quickly　and　cheaply.　However,　when　drug　discovery　moved　into　the　era　of　＇big＇　data,　machine　learning　approaches　evolved　into　deep　learning　approaches,　which　are　a　more　powerful　and　efficient　way　to　deal　with　the　massive　amounts　of　data　generated　from　modern　drug　discovery　approaches.　Here,　we　summarize　the　history　of　machine　learning　and　provide　insight　into　recently　developed　deep　learning　approaches　and　their　applications　in　rational　drug　discovery.　We　suggest　that　this　evolution　of　machine　intelligence　now　provides　a　guide　for　early-stage　drug　design　and　discovery　in　the　current　big　data　era.