Current Trends in Docking Methodologies

Current Trends in Docking Methodologies

Shubhandra Tripathi (CSIR-Central Institute of Medicinal and Aromatic Plants, India), Akhil Kumar (CSIR-Central Institute of Medicinal and Aromatic Plants, India), Amandeep Kaur Kahlon (International Centre for Genetic Engineering and Biotechnology, India) and Ashok Sharma (CSIR-Central Institute of Medicinal and Aromatic Plants, India)
DOI: 10.4018/978-1-5225-0115-2.ch013


Molecular docking was earlier considered to predict the binding affinity of the receptor and ligand molecules. With the progress in computational power and developing approaches, new horizons are now opening for accurate prediction of molecular binding affinity. In the current book chapter, recent strategies for Computer-Aided Drug Designing (CADD) including virtual screening and molecular docking, encompassing molecular dynamics simulations, and binding free energy calculation methods are discussed. Brief overview of different binding free energy methods MMPBSA, MMGBSA, LIE and TI have also been given along with the recent Relaxed Complex Scheme protocol.
Chapter Preview

2. High Throughput Screening (Hts)

HTS is characterized by taking the ligand-target interactions as the principle, leading to a higher information harvest (Liu et al., 2004). HTS lists contains a large percentage of false positives, making follow-up assays necessary to distinguish active from inactive substances (Jenkins, Kao, & Shapiro, 2003). HTS hit rate can be increased by using advance computational methods. Computational methods have several advantages over HTS as they are less time consuming, cost effective and require minimal compound design or prior knowledge. For example, researchers have used computational methods along with HTS to screen inhibitors of tyrosine phosphatase-1B, an enzyme implicated in diabetes. Virtual screening yielded nearly 35% hit rate as compared to 0.021% hit rate produced with HTS, demonstrating the power of computer aided drug designing (CADD) in drug discovery (Doman et al., 2002). Traditionally, HTS produces poor hit rate but in combination with CADD, its efficiency is greatly enhanced due to removal of false positives in less time and at lower computational cost.

One of the most remarkable uses of CADD in drug discovery process is the discovery of new targets for already existing drugs, a vice-versa approach. A few examples of approved drugs that owe their discovery to CADD includes: carbonic anhydrase inhibitor dorzolamide, approved in 1995 (Vijayakrishnan, 2009); the angiotensin-converting enzyme (ACE) inhibitor captopril, approved in 1981 as an antihypertensive drug (Talele, Khedkar, & Rigby, 2010).

Complete Chapter List

Search this Book: