Free Energy Calculations to Estimate Ligand-Binding Affinities in Structure-Based Drug Design
TL;DR: Results using a recently developed Quantum Mechanics (QM)/Molecular Mechanics (MM) based Free Energy Perturbation (FEP) method, which has the potential to provide a very accurate estimation of binding affinities to date has been discussed.
Abstract: Post-genomic era has led to the discovery of several new targets posing challenges for structure-based drug design efforts to identify lead compounds. Multiple computational methodologies exist to predict the high ranking hit/lead compounds. Among them, free energy methods provide the most accurate estimate of predicted binding affinity. Pathway-based Free Energy Perturbation (FEP), Thermodynamic Integration (TI) and Slow Growth (SG) as well as less rigorous end-point methods such as Linear interaction energy (LIE), Molecular Mechanics-Poisson Boltzmann./Generalized Born Surface Area (MM-PBSA/GBSA) and λ-dynamics have been applied to a variety of biologically relevant problems. The recent advances in free energy methods and their applications including the prediction of protein-ligand binding affinity for some of the important drug targets have been elaborated. Results using a recently developed Quantum Mechanics (QM)/Molecular Mechanics (MM) based Free Energy Perturbation (FEP) method, which has the potential to provide a very accurate estimation of binding affinities to date has been discussed. A case study for the optimization of inhibitors for the fructose 1,6- bisphosphatase inhibitors has been described.
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TL;DR: An overview of computational methods used in different facets of drug discovery and highlight some of the recent successes is presented, both structure-based and ligand-based drug discovery methods are discussed.
Abstract: The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.
371 citations
TL;DR: Approaches involving explicit QM energies to calculate binding affinities, with an emphasis on the methods, rather than on specific applications, are reviewed.
Abstract: One of the largest challenges of computational chemistry is calculation of accurate free energies for the binding of a small molecule to a biological macromolecule, which has immense implications in drug development. It is well-known that standard molecular-mechanics force fields used in most such calculations have a limited accuracy. Therefore, there has been a great interest in improving the estimates using quantum-mechanical (QM) methods. We review here approaches involving explicit QM energies to calculate binding affinities, with an emphasis on the methods, rather than on specific applications. Many different QM methods have been employed, ranging from semiempirical QM calculations, via density-functional theory, to strict coupled-cluster calculations. Dispersion and other empirical corrections are mandatory for the approximate methods, as well as large basis sets for the stricter methods. QM has been used for the ligand, for a few crucial groups around the ligand, for all the closest atoms (200–1000...
205 citations
TL;DR: The current perspective on the concept of automated molecule generation is presented by highlighting chemocentric methods that may capture druglike chemical space, consider ligand promiscuity for hit and lead finding, and provide fresh ideas for the rational design of customized screening of compound libraries.
Abstract: Computational medicinal chemistry offers viable strategies for finding, characterizing, and optimizing innovative pharmacologically active compounds. Technological advances in both computer hardware and software as well as biological chemistry have enabled a renaissance of computer-assisted “de novo” design of molecules with desired pharmacological properties. Here, we present our current perspective on the concept of automated molecule generation by highlighting chemocentric methods that may capture druglike chemical space, consider ligand promiscuity for hit and lead finding, and provide fresh ideas for the rational design of customized screening of compound libraries.
113 citations
TL;DR: Docking-based virtual screening will never have chance to play an irreplaceable role in drug discovery and development until its success rate is essentially improved.
Abstract: It is usually the first step to find active compounds from existing chemicals for a drug discovery and development project. Although many pharmaceutical companies have their own libraries that may have millions of compounds, it is costly to maintain the library and to perform high-throughput screening. Virtual screening provides an alternative approach to fulfill the screening of millions of compounds within a few days [1]. Molecular docking is one of the most applied virtual screening methods, especially, when the 3D structure of target protein is available. This method could predict both the binding affinity between ligand and protein and the structure of protein–ligand complex, which is useful information for lead optimization. Indeed, molecular docking has been applied for more than three decades and a great number of new drugs have been discovered and developed accordingly [2]. Although there is no doubt that it will continue to play important role, molecular docking is still far from full success in terms of success rate. This is also the reason why high-throughput screening is still widely applied nowadays in many pharmaceutical companies. Docking-based virtual screening will never have chance to play an irreplaceable role in drug discovery and development until its success rate is essentially improved.
76 citations
TL;DR: The results demonstrate that RNV66 efficiently inhibits breast cancer cell proliferation both in vitro and in vivo and introduces the first computational model of the LNA aptamer-VEGF complex blocking its interaction with VEGF-receptor.
47 citations