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• W2016089558 abstract "The unprecedented availability of chemical and biological structure-property data is creating new opportunities for cheminformatics and QSAR. This major shift in the perception of QSAR is fostered by both scientific and societal causes. From a scientific standpoint, massive amounts of data requiring interpretation and modelling have accumulated due to high throughput biology and chemistry. At the societal level, the open-access movement has facilitated the production of large-scale databases such as PubChem and ChEMBL, which are complemented by for-fee databases. As access to massive amounts of data is no longer a hindrance, the demand for predictive models based on molecular structures has increased. Until recently, QSAR technologies have primarily been utilized retrospectively (e.g., the QSAR equations), yet now the emphasis is on prospective use (e.g., virtual screening). Their accuracy relies on deep knowledge mining, increased data completeness and advances in machine learning. Paralleled by significant improvements in hardware and computational power, these technologies have resulted in increasingly more efficient approaches to drug discovery and active compound design. Our ability to understand molecular determinants and their contribution to the design of therapeutic agents, agrochemicals, and flavor and fragrance modifiers has witnessed dramatic improvements in the five decades of QSAR formalism. Such improvements, as well as practical applications of cheminformatics and QSAR technologies were discussed at the 18th European Symposium on Quantitative Structure Activity Relationships in Rhodes, Greece (September 2010), where the main theme was “Discovery Informatics in Drug Design”. From systems biology and its disease chemical biology application, ChemProt, to biologically-oriented chemical synthesis (BIOS), and from in silico pharmacology to interactive, efficient navigation and visualisation tools, the field of cheminformatics has evolved significantly in the past decade. We have the ability to identify novel targets and to identify their binding site(s) and to identify and suggest novel chemical scaffolds in a rational manner. In silico tools for drug discovery are becoming a necessity, one that begins to extend into clinical, in addition to chemical and biological spaces. Such evolving technologies are designed to assist translational scientists, chemists and biologists with seemingly intractable problems: From medical informatics rooted data mining of adverse events to structure- and ligand- based virtual screening, and from physico-chemical and early pharmacokinetic properties to drug safety and toxicology, cheminformatics and QSAR technologies are a living example of how necessity, adaptive developments and creativity, all aspects of scientific endeavour, converge in order to achieve a balanced, streamlined effort in our quest for novel therapeutics. The future of QSAR has never looked brighter. 1 Tudor I. Oprea Member of Organizing & Scientific Committees 18th European Symposium on Quantitative Structure-Activity Relationships Chair The Cheminformatics & QSAR Society Professor and Chief Division of Biocomputing, UNM School of Medicine Guest Professor Center for Biological Sequence Analysis, Technical University of Denmark" @default.
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• W2016089558 date "2011-03-14" @default.
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• W2016089558 title "Next-generation QSAR" @default.
• W2016089558 doi "https://doi.org/10.1002/minf.201180001" @default.
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