Molecular modeling in drug discovery

Abstract

With the financial requirements and high time associated with bringing a commercial drug to the market, the application of computer-aided drug design has been recognized as a powerful technology in the drug discovery pipeline. In accelerating drug discovery, molecular modeling techniques have experienced considerable growth in computational capabilities over the last decade. Pharmaceutical companies and academic research organizations are currently using various computational modeling techniques to lower the cost and time required for the discovery of an effective drug. In this article, we focus on reviewing three key components of molecular modeling (Molecular Docking, Molecular Dynamics, and ADMET modeling), their applications, and limitations in small-molecule drug discovery. We discussed the technicalities encircling molecular dynamics and docking, the algorithms used to develop the docking softwares, and the models explored by these algorithms coupled with their scoring functions. We also reviewed the influence of molecular dynamics simulations (all atoms and coarse-grained molecular dynamics simulations) in drug discovery and also elucidated how the ensembles generated from MD simulations could pave the way for novel drug discovery. Furthermore, we briefly explain the role played by pharmacokinetics and pharmacodynamics profiling in discovering new leads for therapeutic efficacy. Besides the computational success of molecular modeling in drug discovery, we highlighted the experimental corroboration of in silico discovered drug candidates. However, as there is hardly a drug in the market discovered primarily with the use of computational modeling, we concluded the review by proposing possible solutions that could foster the advancement and clinical success of drugs.

Keywords

• Drug discovery
• Molecular modeling
• Molecular docking
• MD simulation
• Algorithm
• ADMET