Current State-of-the-art for Virtual Screening and Docking Methods - New Developments in Medicinal Chemistry - page 1

CHAPTER 1
Current State-of-the-art for Virtual Screening and Docking
Methods
Carlton Anthony Taft 1,* and Carlos Henrique Tomich de Paula da Silva 2
1 Brazilian Center for Physics Research, Rua Dr. Xavier Sigaud, 150, Urca,
22290-180, Rio de Janeiro, Brazil and 2 School of Pharmaceutical Sciences of
Ribeirão Preto, University of São Paulo, Av. do Café, s/n, Monte Alegre, 14040-
903, Ribeirão Preto, São Paulo, Brazil
Abstract: The current state of the art for docking and virtual screening methods in drug
design have been reviewed, emphasizing their important contribution in aiding drug
design and discovery. We summarize our contributions during the last decade, with
proposals of novel inhibitors for Cancer, AIDS, Diabetes, Parkinson, Alzheimer and
other diseases. Homology, fragment, consensus, bioisosteric, scaffold, pharmacophore,
induced fit, chemogenomics and knowledge-based protocols have been described. The
basics have been given for binding affinities, molecular dynamics, water and solvation,
QM, QM/MM, free energy simulations, molecular shapes and fields. We also discuss
virtual screening and comment on hotspots (protein docking, stem cells, workflow
pipelines, different types of ligands/targets, cloud, high-performance, grid computing,
chemical libraries, evaluations, benchmarks and validations). We describe the
procedures of fifty programs that use the protocols reviewed.
Keywords: Binding affinity, biosiosteric, chemical libraries, chemogenomics,
confidence, consensus, evaluations benchmarks, fragment, future trends, homology,
induced fit, knowledge based, pharmacophore, recent virtual screening and docking
programs, scaffold, similarity, validations.
INTRODUCTION
Combinatorial chemistry and high-throughput screening are among the early
technologies used in the drug discovery process. With the support of computing
technology/computer-aided drug design, it has been possible to lower cost and
obtain higher efficiency for the drug discovery process. With in silico search and