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Virtual Computational Chemistry Laboratory | ||||||
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Computational in silico methods to predict pKa and pH-dependent aqueous solubility of drug-like molecules at GSF, Institute for Bioinformatics, Neuherberg (Munich) Germany This PhD thesis (in chemoinfomatics) will develop models to predict pKa (ionization microstates) and pH-dependent solubility of drug like molecules. These properties are extremely important in the drug discovery since they determine bioavailability, distribution and clearance of drugs. Both theoretical and empirical approaches (based on quantum chemical and 2D and 3D descriptors) will be used to develop machine-learning models (neural networks, Gaussian processes, etc.) to predict these properties. The developed models will be complemented with an estimation of their accuracy of predictions (see, e.g. Tetko et al, DDT, 2006 available at http://www.vcclab.org for more details).The candidate should be a chemist (primary subject). The preference will be given to a student with a good knowledge of programming languages, such as Java, C/C++, Perl, or the candidate should be strongly motivated to learn them. A basic knowledge of machine learning methods will be also advantageous. Applications should be sent by e-mail to Dr. Igor Tetko before November, 30.
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