About this Research Topic
Current pharmaceutical R&D has faced outstanding challenges as scientific breakthroughs achieved in the past two decades have revolutionized the field. Core approaches such as high-throughput screening (HTS) have increasingly been used in combination with emerging strategies relying on genomics, chemical biology and molecular modeling. These forefront approaches have promoted substantial progress in our understanding of key biological processes, in addition to fostering critical advances in the armamentarium available for drug research. Along with synthetic strategies such as combinatorial chemistry, which has supported a consistent expansion of the chemical space explored in drug discovery, these state-of-the-art technologies are shaping the future of pharmaceutical industry.
The integration of these methodologies to the drug discovery enterprise has led to an exponential growth of chemical and biological data, in addition to a sharp increase in the complexity of the R&D process. As a result, current players in drug discovery have invested unprecedentedly in the development of computational methods to extract meaning from these data and simulate critical phenomena related to drug efficacy, pharmacokinetics and toxicity. The value of using in silico strategies has been demonstrated by the increasing number of publications reporting campaigns that have resulted in the discovery of promising lead compounds; many of them undergoing clinical development and reaching the marketplace.
Usually, these computer-assisted efforts combine ligand- and structure-based drug design strategies (LBDD and SBDD, respectively) with a plethora of experimental techniques. Broadly used SBDD approaches, molecular docking, homology modeling, molecular dynamics and structure-based virtual screening have provided relevant insights into ligand-receptor interactions. Equally important, LBDD methods such as pharmacophore modeling, quantitative structure-activity relationships (QSAR) and ligand-based virtual screening have been actively used to explore small-molecule databases and produce correlations between chemical features and pharmacological activity. Also a hot-topic in LBDD, quantitative structure-property relationship models (QSPR) are central for predicting pharmacokinetics and toxicity-related characteristics. This Research Topic features recent applications of LBDD and SBDD to the study of drug activity as well as drug absorption, distribution, metabolism, excretion and toxicity (ADME/Tox). Including original and review articles it also reports up-to-date developments in novel software, tools, resources and algorithms in drug discovery.
The integration of these methodologies to the drug discovery enterprise has led to an exponential growth of chemical and biological data, in addition to a sharp increase in the complexity of the R&D process. As a result, current players in drug discovery have invested unprecedentedly in the development of computational methods to extract meaning from these data and simulate critical phenomena related to drug efficacy, pharmacokinetics and toxicity. The value of using in silico strategies has been demonstrated by the increasing number of publications reporting campaigns that have resulted in the discovery of promising lead compounds; many of them undergoing clinical development and reaching the marketplace.
Usually, these computer-assisted efforts combine ligand- and structure-based drug design strategies (LBDD and SBDD, respectively) with a plethora of experimental techniques. Broadly used SBDD approaches, molecular docking, homology modeling, molecular dynamics and structure-based virtual screening have provided relevant insights into ligand-receptor interactions. Equally important, LBDD methods such as pharmacophore modeling, quantitative structure-activity relationships (QSAR) and ligand-based virtual screening have been actively used to explore small-molecule databases and produce correlations between chemical features and pharmacological activity. Also a hot-topic in LBDD, quantitative structure-property relationship models (QSPR) are central for predicting pharmacokinetics and toxicity-related characteristics. This Research Topic features recent applications of LBDD and SBDD to the study of drug activity as well as drug absorption, distribution, metabolism, excretion and toxicity (ADME/Tox). Including original and review articles it also reports up-to-date developments in novel software, tools, resources and algorithms in drug discovery.
Keywords: Ligand-Based Drug Design, Structure-Based Drug Design, Molecular Modeling, Drug Discovery, Medicinal Chemistry, Pharmaceutical Chemistry, Chemoinformatics
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