A New Method to Estimate Ligand-Receptor Energetics

Abstract

In the discovery of new drugs, lead identification and optimization have assumed critical importance given the number of drug targets generated from genetic, genomics, and proteomic technologies. High-throughput experimental screening assays have been complemented recently by "virtual screening" approaches to identify and filter potential ligands when the characteristics of a target receptor structure of interest are known. Virtual screening mandates a reliable procedure for automatic ranking of structurally distinct ligands in compound library databases. Computing a rank score requires the accurate prediction of binding affinities between these ligands and the target. Many current scoring strategies require information about the target three-dimensional structure. In this study, a new method to estimate the free binding energy between a ligand and receptor is proposed. We The process of developing a new drug involves seven major steps (1). (i) First, a disease is identified, and then (ii) drug targets (usually proteins), the activation or inhibition of which is thought to alter the disease state, within the cell are hypothesized. Once targets are hypothesized, the next task is to (iii) identify potential lead compounds that will bind to the target. These leads are subsequently (iv) optimized with respect to their structural characteristics in the context of the target binding site and then subjected to (v) preclinical and (vi) clinical trials to determine their bioavailability and therapeutic potential. The final step is to (vii) optimize efficacy, toxicity, and pharmacokinetic properties. This may involve the use of pharmacogenomic techniques to tailor compounds to a subset of the patient population that is predisposed to a disease.Pharmaceutical companies are exposed to great financial risk in the course of identifying viable drugs to treat a certain condition or disease. There are also tremendous direct and indirect (opportunity) costs associated with delaying the removal of non-viable drugs from this drug discovery "pipeline" until the latest stages of the process.A huge number of drug targets have been generated from genetic, genomic, and proteomic technologies. Accordingly, the lead identification and optimization steps have assumed critical importance. High-throughput experimental screening assays (30) have been complemented recently by computational ("virtual screening") approaches to identify and filter potential ligands when the characteristics of the target receptor structure of interest are known (3,35). In virtual screening, databases of compound libraries are searched, and scoring or discrimination functions are used to select the "best" candidate compounds for biological activity analysis (32).The scoring of ligands in virtual screening is often associated with computational docking simulations that mate receptor and cognate small molecule ligand in three-dimensional space. To provide broad generalization in "chemical diversity" space, computing this score requires the accurate prediction of bind...

Screening of selective histone deacetylase inhibitors by proteochemometric modeling

“…Support vector machine (SVM) is a non-linear modeling technique applied multiple times in PCM [33,45-50]. We created PCM models using support vector regression (SVR) built in Weka suit (Weka implementation “SMOreg”).…”

Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents

“…23,25 Support vector machine (SVM) 39,40 is used in this work as the statistical learning method for the prediction of the three pharmacokinetic and toxicological properties of chemical agents. SVM has been applied to a wide range of pharmacological and biomedical problems including drug-likeness, 9-11 drug blood-brain barrier penetration prediction, 41 drugreceptor binding, 14 and drug metabolism. 17 In many cases SVM has been found to be consistently superior to other supervised learning methods 10,12,[42][43][44] and less sensitive to overfitting.…”

“…[1][2][3][4][5][6][7] These descriptors were initially developed for the construction of quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) of structurally related compounds. 8 They have been extensively used for the statistical-learning-based prediction of pharmacodynamic, pharmacokinetic, and toxicological properties of chemical agents including drug-likeness, [9][10][11] blood-brain barrier penetration, 12,13 human intestinal absorption, 4 drug-receptor binding, [14][15][16] drug metabolism, 17 cellular membrane partitioning, 18 chemical space navigation, 19 and antibacterial activity. 20,21 Some of these molecular descriptors are developed for the study of a particular type of properties of a group of structurally related chemical agents.…”

Proteomics Approach to Illustrate Drug Action Mechanisms

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