Conversely, receptor-based drug design uses molecular docking simulations with machine learning. Under these conditions, machine learning techniques use simulations to predict the degree of attraction between a small molecule ligand and a target receptor from their spatial and electrostatic compatibility. These models help to identify potential therapy candidates with excellent specificity and potency by using known ligandreceptor interactions and structural data to forecast the most favourable binding locations and strengths ( ).