Cheminformatics methods use computational and informational tools to solve chemical problems and enhance the discovery process of new drugs. Combining data from chemistry, biology, and pharmacology, cheminformatics helps to handle, examine, and present large datasets efficiently ( ). Accelerating the identification of potential pharmacological candidates, improving their features, and predicting their performance in biological systems depend on this multidisciplinary field ( ). Among the various advantages cheminformatics provides include the ability to examine large chemical databases rapidly, lower the cost and length of experimental procedures, and improve target identification and lead optimisation accuracy. Ultimately, these approaches enable drug research and development procedures' success and efficiency, generating fresh and creative treatments. Three chemogenomic techniques include (i) machine learning-based, (ii) graph methods and (iii) network models approaches ( ).