摘要Artificial intelligence(AI)researchers and cheminformatics specialists strive to identify ef-fective drug precursors while optimizing costs and accelerating development processes.Digit-al molecular representation plays a crucial role in achieving this objective by making mo-lecules machine-readable,thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making.This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations.The research methodology begins with the compilation of small mo-lecule databases,followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms,capturing patterns and salient fea-tures across extensive chemical spaces.The study then examines various drug discovery downstream tasks,including drug-target interaction(DTI)prediction,drug-target affinity(DTA)prediction,drug property(DP)prediction,and drug generation,all based on learned representations.The analysis concludes by highlighting challenges and opportunities associ-ated with machine learning(ML)methods for molecular representation and improving down-stream task performance.Additionally,the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medi-cine(TCM)medicinal substances and facilitating TCM target discovery.