2009/05/11 by Chao Song, Shen Jean Lim, J. Tong · 1 citation
Computer Science · Biochemistry, Genetics and Molecular Biology · #Computational Drug Discovery Methods #Genetics, Bioinformatics, and Biomedical Research #Protein Structure and Dynamics #Workflow #Virtual screening #Drug discovery #Computer science #Computer-aided #Data science #Selection (genetic algorithm) #Computational biology #Bioinformatics #Artificial intelligence #Database #Biology
paper · doi:10.1093/bib/bbp023
openalex publication_date 2009/05/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
Modern drug discovery is characterized by the production of vast quantities of compounds and the need to examine these huge libraries in short periods of time. The need to store, manage and analyze these rapidly increasing resources has given rise to the field known as computer-aided drug design (CADD). CADD represents computational methods and resources that are used to facilitate the design and discovery of new therapeutic solutions. Digital repositories, containing detailed information on drugs and other useful compounds, are goldmines for the study of chemical reactions capabilities. Design libraries, with the potential to generate molecular variants in their entirety, allow the selection and sampling of chemical compounds with diverse characteristics. Fold recognition, for studying sequence-structure homology between protein sequences and structures, are helpful for inferring binding sites and molecular functions. Virtual screening, the in silico analog of high-throughput screening, offers great promise for systematic evaluation of huge chemical libraries to identify potential lead candidates that can be synthesized and tested. In this article, we present an overview of the most important data sources and computational methods for the discovery of new molecular entities. The workflow of the entire virtual screening campaign is discussed, from data collection through to post-screening analysis.