title: Applications and limitations of in silico models in drug discovery.
abstract: Drug discovery in the late twentieth and early twenty-first century has witnessed a myriad of changes that were adopted to predict whether a compound is likely to be successful, or conversely enable identification of molecules with liabilities as early as possible. These changes include integration of in silico strategies for lead design and optimization that perform complementary roles to that of the traditional in vitro and in vivo approaches. The in silico models are facilitated by the availability of large datasets associated with high-throughput screening, bioinformatics algorithms to mine and annotate the data from a target perspective, and chemoinformatics methods to integrate chemistry methods into lead design process. This chapter highlights the applications of some of these methods and their limitations. We hope this serves as an introduction to in silico drug discovery.
authors: School of Biomedical Engineering, Drexel University, Philadelphia, PA, USA.
authors: Ahmet
authors: A
authors: Sacan
authors: Sean
authors: S
authors: Ekins
authors: Sandhya
authors: S
authors: Kortagere
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D018360:Crystallography, X-Ray; D016208:Databases, Factual; D055808:Drug Discovery / Q000379:methods*; D057166:High-Throughput Screening Assays; D008958:Models, Molecular; D011506:Proteins / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship
chemical_list: D011506:Proteins