High throughput in silico methods have offered the tantalizing potential to drastically accelerate the drug discovery process. Yet despite significant efforts expended by academia, national labs and industry over the years, many of these methods have not lived up to their initial promise of reducing the time and costs associated with the drug discovery enterprise, a process that can typically take over a decade and cost hundreds of millions of dollars from conception to final approval and marketing of a drug. Nevertheless structure-based modeling has become a mainstay of computational biology and medicinal chemistry, helping to leverage our knowledge of the biological target and the chemistry of protein-ligand interactions. While ligand-based methods utilize the chemistry of molecules that are known to bind to the biological target, structure-based drug design methods rely on knowledge of the three-dimensional structure of the target, as obtained through crystallographic, spectroscopic or bioinformatics techniques. Here we review recent developments in the methodology and applications of structure-based and ligand-based methods and target-based chemogenomics in Virtual High Throughput Screening (VHTS), highlighting some case studies of recent applications, as well as current research in further development of these methods. The limitations of these approaches will also be discussed, to give the reader an indication of what might be expected in years to come.
Keywords: QSAR, descriptors, model validation, applicability domain, best practices, activity cliffs, site similarity, consensus docking, polypharmacology, chemogenomics, Virtual High Throughput Screening, cheminformatics
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