Machine learning techniques in cheminformatics transform the drug development process by utilising sophisticated algorithms to analyse intricate chemical and biological data ( ). These techniques utilise patterns and correlations in data to forecast the characteristics and behaviours of possible drug candidates, expediting the process of identifying and refining new therapeutic substances. The significance of machine learning in drug discovery is its capacity to manage extensive information, reveal concealed insights, and enhance the precision of predictions in contrast to conventional methods. The benefits encompass improved efficacy in analysing extensive chemical libraries, the capability to simulate complex biological interactions, and the possibility to decrease expenses and durations linked to medication development ( ).