Computational methods in drug discovery

• Sumudu P. Leelananda and
• Steffen Lindert

Beilstein J. Org. Chem. 2016, 12, 2694–2718, doi:10.3762/bjoc.12.267

• ; machine learning; pharmacophore; QSAR; SBDD; scoring; target flexibility; Introduction Bringing a pharmaceutical drug to the market is a long term process that costs billions of dollars. In 2014, the Tufts Center for the Study of Drug Development estimated that the cost associated with developing and

• therapeutics [3][4][5][6]. CADD methods can be broadly classified into two groups, namely structure-based (SB) and ligand-based (LB) drug discovery (Figure 1). The CADD method used depends on the availability of target structure information. In order to use SBDD tools, information about target structures needs

• structure prediction tools that are routinely used in structure-based drug discovery, widely used docking algorithms, scoring functions, virtual high-throughput screening, lead optimization and methods of assessment of ADME properties of drugs. Review Structure-based drug discovery (SBDD) If the three