Progress of machine learning in the application of small molecule druggability prediction.

Machine learning (ML) has become an important tool for predicting the pharmaceutical properties of small molecules. Recent advancements in ML algorithms enable the rapid and accurate evaluation of solubility, activity, toxicity, pharmacokinetics, and other molecular properties through ML-based models. By conducting virtual screening of drug targets and elucidating drug-target protein interactions, researchers can conduct preliminary evaluations of the activity and safety of compounds from the ultra-large drug compound libraries, thereby accelerating the screening process for lead compounds. Moreover, ML leverages existing experimental data to train and generate new datasets, addressing the challenge of limited compounds and protein target data. This review provided a concise overview of ML applications in predicting small molecule properties, focusing on model construction principles, molecular feature selection, and other essential aspects. It also discussed the potential applications of ML in the screening of pharmaceutical small molecules.