Lung cancer is a complex disease that involves multiple types of biological interactions across diverse, physical, temporal, and biological scales. This complexity presents substantial challenges for the characterization of lung cancer, and motivates the study of cancer in the context of molecular, cellular, and physiological systems. Computational models of Lung cancer are being developed to aid both biological discovery and clinical medicine. The development of these in silico models is facilitated by rapidly advancing experimental and analytical tools that generate information-rich, high-throughput biological data. Protein structure prediction by using bioinformatics can involve sequence similarity searches, multiple sequence alignments, identification and characterization of domains, secondary structure prediction, solvent accessibility prediction, automatic protein fold recognition, constructing three-dimensional models to atomic detail, and model validation. Till today technologies like combinatorial chemistry and high-throughput screening (HTS) authorize biological assays of a large number of small molecules against the therapeutically relevant targets. However, the escalating costs highlight the need of developing novel approaches while still allowing one to explore larger chemical diversity. In this respect, Virtual ligand screening (VLS) is established as an attractive approach to handle large sets of compounds and to improve the “hit-rate” of drug discovery programs. Here, we review the main Ligand Screening techniques applied for structure-based drug design and we focus on key concepts in the molecular docking–scoring methodology in lung cancer. These methods, if used appropriately, can provide valuable indicators of protein structure and function for the different type of cancers.
Lung cancer, Molecular modeling, Sequence similarity searches, Multiple sequence alignment, Secondary structure prediction, Virtual screening, Structure-based drug design, Polo like kinase 1, Thrombomodulin, Review.
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