Considering interatomic forces, it uses classical bonded and nonbonded potentials (Figure ). Its better performance has resulted in significant developments and has been efficiently applied to handle conformational changes, folding binding penetration, and many other problems ( ). MD has faced two main challenges: first, the computation of interatomic potential tables, sometimes known as force fields, has historically been a laborious process requiring excellent refinement; second, it is computationally demanding despite reasonable efforts and developments in expediting molecular dynamics codes ( ). To overcome these challenges, machine learning (ML) techniques in MD simulations have been enhanced in terms of their value and efficiency in drug development ( ). Machine learning methods can analyse large amounts of simulation data to identify trends and project molecular behaviours. This so accelerates the process of spotting possible drug candidates with promise. ML-driven MD simulations offer a potent mix of accuracy and efficiency by improving force fields, anticipating binding affinities, and maximising sample efficiency. MD simulations and ML streamline the drug development process and allow logical synthesis of more specific drugs ( ).