Developing　new　drugs　is　too　expensive　and　time　-consuming.　Accurately　predicting　the　interaction　between　drugs　and　targets　will　likely　change　how　the　drug　is　discovered.　Machine　learning-based　protein-ligand　interaction　prediction　has　demonstrated　significant　potential.　In　this　paper,　computational　methods,　focusing　on　sequence　and　structure　to　study　protein-ligand　interactions,　are　examined.　Therefore,　this　paper　starts　by　presenting　an　overview　of　the　data　sets　applied　in　this　area,　as　well　as　the　various　approaches　applied　for　representing　proteins　and　ligands.　Then,　sequence-based　and　structure-based　classification　criteria　are　subsequently　utilized　to　categorize　and　summarize　both　the　classical　machine　learning　models　and　deep　learning　models　employed　in　protein-ligand　interaction　studies.　Moreover,　the　evaluation　methods　and　interpretability　of　these　models　are　proposed.　Furthermore,　delving　into　the　diverse　applications　of　protein-ligand　interaction　models　in　drug　research　is　presented.　Lastly,　the　current　challenges　and　future　directions　in　this　field　are　addressed.　©　2024　American　Chemical　Society.