Integrative computational approaches in pharmaceuticals: Driving innovation in discovery and delivery

In recent years, the pharmaceutical industry has increasingly emphasized the role of lead compound identification in developing new therapeutic agents. Lead compounds show promising pharmacological activity against specific targets and are critical in drug development. Integrative computational approaches streamline this process by efficiently screening chemical libraries and designing potential drug candidates. This chapter highlights various computational techniques for lead compound discovery, including molecular modeling, cheminformatics, ligand- and structure-based drug design, molecular dynamics simulations, ADMET prediction, drug-target interaction analysis, and high-throughput screening. These methods improve drug discovery's efficiency, cost-effectiveness, and target-specific focus. Computational pharmaceutics has gained popularity due to the longer formulation development time which in turn increases the cost as well as decrease in the drug discovery production. Conventionally, formulation development relied on costly and unpredictable trial-and-error methods. However, analyzing the big data, artificial intelligence, and multi-scale modeling in computational pharmaceutics is transforming drug delivery. This chapter provides valuable insights throughout pre-formulation, formulation screening, in vivo predictions, and personalized medicine applications. Multiscale computational modeling is advancing drug delivery systems, enabling targeted treatments with multifunctional nanoparticles. Although in its early stages, this approach helps understand complex interactions between drugs, delivery systems, and patients.