Challenges and limitations of computer-aided drug design.

Abstract

Molecular Modelling in Drug Designing or Computer Aided Drug Designing (CADD) plays a significant role in new drug identification in the current world. However, it has sensitivity challenges and limitation because theoretical models involve assumption and approximations Computational models are not very accurate, some of the major challenges that face these models include the following. These include, for instance, molecular-docking or molecular-dynamics-simulation models which may not represent an accurate biological system and thus the predictions will be wrong. CADD depends on the availability of accurate, high-quality structural information for target proteins and ligand. Unfortunately, there are instances when experimental structures are not available, and homology models are employed, which can be imprecise. The computational cost is another drawback; only high accuracy simulations call for huge amounts of computational power and time well-suited for screening a multitude of agents. Moreover, they have weaknesses in determining pharmacokinetic and toxicity patterns of compounds that influence drug performance and effectiveness. In other words, even though CADD greatly helps drug discovery, it is still constrained by experimental validation to solve its drawbacks and optimize its foretelling.