With the help of ML techniques in cheminformatics, several successful cheminformatics studies have been reported in the scientific literature ( ). The preferable ML model is a supervised model used to study DTIs. For example, the PaDEL descriptor utilised the 1287-dimensional target descriptor and the 1024-dimensional drug descriptor from these datasets to predict DTIs ( ). The standard classification models used in DTI research are random forest, random walk with restart, support vector machines (SVM), and decision trees ( ). In another example, Yu et al. designed a method to indicate drug-target interactions from heterogeneous biological data using Random Forest and SVM ( ). Also, several machine learning models have been built on a structureactivity relationship (SAR) and structure-property relationships (SPR) ( ). An instance of the SAR model application is TargetNet ( ). TargetNet, containing 623 SAR models, is a web service working with Naıve bayes based multi-target SAR models to predict DTIs ( ). The last example of the QSAR model is that Bender et al. ( ) benefit from the Bayesian-based method to build QSAR models. Finally, deep-learning algorithms in chemoinformatics also promise to identify targets for a compound ( ). The approaches comprise unsupervised learning, which exposes hidden patterns and relationships in the data without predefined labels; supervised learning, in which models are trained on labelled datasets to forecast drug-target interactions; and reinforcement learning, in which algorithms acquire optimal strategies for drug design by experimentation and improvement. Furthermore, underlined in the picture is the use of deep learning techniques, including neural networks, to depict complex, non-linear relationships and improve the prediction accuracy. Together, these machine-learning techniques increase the accuracy and efficiency of identifying potential therapeutic candidates ( ).