In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

Abstract

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semiquantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.

The impact of accelerator processors for high-throughput molecular modeling and simulation

“…Despite the considerable growth of biotechnologies in the last ten years, the practical consequences of these techniques on the number of approved drugs has failed to meet expectation [2]. Nonetheless, the discovery process greatly benefits from the use of computational modeling [3], at least in the initial stages of compound discovery, screening and optimization [4].…”

“…There is great interest in methods for supporting and optimising experimental high-throughput screening [4,38] in order to identify, characterise and optimise possible leads for a given target out of the vast number of viable chemical compounds. It is, however, very difficult for such methods to account correctly for the many phenomena involved in complex formation, such as the subtle interplay between entropy and enthalpy, conformational changes of the ligand or the substrate, presence of water molecules in the binding sites and limited resolution of structures [39,40].…”

“…Despite the considerable growth of biotechnologies in the last ten years, the practical consequences of these techniques on the number of approved drugs has failed to meet expectation [2]. Nonetheless, the discovery process greatly benefits from the use of computational modeling [3], at least in the initial stages of compound discovery, screening and optimization [4].Among the techniques available, force-based molecular modeling methods (briefly, molecular modeling) such as molecular dynamics are particularly useful in studying molecular processes at the atomistic level, providing accurate information on macromolecular dynamics and thermodynamic properties. Bridging from the molecular-atomistic (femtosecond) to biological (micromillisecond) timescales is still an unaccomplished feat in computational biology: the complexity of the modeling impedes sufficient sampling of the evolution of the system, even on expensive high performance computing (HPC) resources.…”

“…For instance, cost and sampling constraints have so far limited a routine molecular dynamics run over a PC cluster to a single protein system for tens of nanoseconds, barely sufficient to compute the binding free energy using an exact thermodynamic method [5]. This information is of great importance in the discovery process for virtual screening, lead optimization and in silico drug discovery [4], but needs to be computed for hundreds of protein-ligand systems efficiently and economically in order to open the way for molecular simulations to become a routine tool in the discovery workflows used in the biotechnology industry.In light of the significant changes that have occurred lately in microprocessor design, we review the first cost-effective accelerator processors (APs) -the Cell processor and Nvidia graphics processing units (GPUs) -and examine results of their early adoption in stand-alone and grid-computing scenarios in the context of high-throughput molecular simulation for biomolecular applications. …”

“…For instance, cost and sampling constraints have so far limited a routine molecular dynamics run over a PC cluster to a single protein system for tens of nanoseconds, barely sufficient to compute the binding free energy using an exact thermodynamic method [5]. This information is of great importance in the discovery process for virtual screening, lead optimization and in silico drug discovery [4], but needs to be computed for hundreds of protein-ligand systems efficiently and economically in order to open the way for molecular simulations to become a routine tool in the discovery workflows used in the biotechnology industry. [11].…”