Artificial intelligence (AI) researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes. Digital molecular representation plays a crucial role in achieving this objective by making molecules 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 molecule databases, followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms, capturing patterns and salient features 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 associated with machine learning (ML) methods for molecular representation and improving downstream task performance. Additionally, the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine (TCM) medicinal substances and facilitating TCM target discovery.
Artificial Intelligence ; Drug Discovery/methods* ; Humans ; Machine Learning ; Medicine, Chinese Traditional ; Small Molecule Libraries/chemistry*

BACKGROUND:Osteoporosis is often accompanied by sarcopenia and an increased risk of fractures from falls.Recent studies have indicated a close relationship between lipid metabolism and sarcopenia.Abnormal lipid metabolism may directly impact muscle physiological function and metabolism. OBJECTIVE:To investigate the relationship between lipid metabolism and sarcopenia and evaluate their causal relationship using Mendelian randomization. METHODS:Mendelian randomization was used to explore the causal relationship between low-density lipoprotein cholesterol,high-density lipoprotein cholesterol,triglycerides,and muscle mass.Research data from genome-wide association studies were used and a sensitivity analysis was conducted to verify the reliability of the results.Approximate indicators of muscle mass,including trunk lean mass and appendicular lean mass,were used as outcome measures. RESULTS AND CONCLUSION:The study found a negative correlation of low-density lipoprotein cholesterol and triglycerides with muscle mass,while no correlation was observed between high-density lipoprotein cholesterol and muscle mass.The results of the sensitivity analysis indicated a robust causal relationship.Using Mendelian randomization,this study provides evidence of a causal relationship between low-density lipoprotein cholesterol and triglycerides and muscle mass.This finding deepens our understanding of the effects of lipids on sarcopenia and has important clinical implications for the prevention and treatment of sarcopenia and osteoporosis.