Although machine learning offers excellent possibilities to change drug discovery processes, it is not free from challenges and restrictions. The accessibility and quality of data create a significant challenge since the effectiveness of machine learning models depends much on large, diversified, carefully kept datasets ( ). Furthermore, a considerable challenge of machine learning models is their comprehensibility since complex algorithms usually produce predictions without explaining underlying mechanisms. Furthermore, the opaque character of many machine learning models could hinder regulatory approval and acceptance inside the pharmaceutical company ( ). Moreover, the ability of machine learning models to be applied to various datasets and experimental environments is still tricky since models trained on particular datasets could not attain optimal performance on fresh, undiscovered data ( ). Dealing with these challenges calls for cooperation between several fields, advances data organisation and standardising, and generates interpretable and transferable machine learning models specially tailored for the complex character of drug development.