Modern Tools and Techniques in Computer-Aided Drug Design

Computer-aided drug design (CADD) has become an effective tool for the development of therapeutics. CADD approaches parallelly assist the main drug discovery pipeline in many ways and also at different stages. The rapid advancement in the high-performance computational resource as well as the introduction of the different new in silico approaches has reduced the time and money required by many folds. The current advancements in CADD are highly enriched with different sets of computational methodologies, which allows overcoming the individual tool/technique limitation by integrating the different tools/techniques. CADD approaches can be implemented with molecular docking and virtual screening for drug discovery and optimization. Some very recent advancements in the CADD approaches include de novo drug design, receptor-based ab initio pharmacophore modeling, water pharmacophores from the dynamic trajectory, free energy perturbation calculation, polypharmacology, big data, development of many protocols involving machine learning (ML)/deep learning methodologies, and application of artificial intelligence. Advancement in the experimental techniques have also scaled up the rate of the biological data generation and this has enabled the integration of different levels of biological information especially in the case of ML approaches to infer the biologically meaningful outcome. Many approaches like scaffold hopping, activity cliff, molecular-match pair, and SAR matrix were introduced more than two decades ago; however, due to the outpaced growth of the bioassay information, these methodologies are now becoming more useful in finding and optimizing the novel lead molecules. A decade-old multiproteins targeted approach is emerging as a promising strategy to fight against growing resistance and complex diseases. In this chapter, advanced tools and techniques in the CADD will be discussed with relevant case studies.