Thesis supervisor: Balázs Harangi
Location of studies (in Hungarian): University of Debrecen Faculty of Informatics Abbreviation of location of studies: ITDT
Description of the research topic:
In the pharmaceutical industry, many companies spend significant amount of money for research, development and production of new drugs. One of the first steps in the process of making a drug is to test already known compounds and to study their effects and reactions. The primary goal of the research is to be able to extend certain deep learning methods, especially convolutional neural networks, to the chemical discipline. It is expected that these methods will lead to significant advances and results in, for example, the prediction of molecular reactions or, in the field of drug discovery of great importance in the pharmaceutical industry.
Literature:
Ahmed Abdelaziz és tsai. “Consensus Modeling for HTS Assays UsingIn silico Descriptors Calculates the Best Balanced Accuracy in Tox21Challenge”. Frontiers in Environmental Science4 (2016), Jayme L Dahlin és Michael A Walters. “The essential roles of chemistryin high-throughput screening triage”.Future Med Chem6 (2014.),1265–190. Okan Köpüklü és tsai. “Resource Efficient 3D Convolutional NeuralNetworks”.ArXivabs/1904.02422 (2019).
Deadline for application: 2021-01-16
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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