Témakiírások
AI-Enhanced Tools for Accelerating Drug Discovery
témakiírás címe
AI-Enhanced Tools for Accelerating Drug Discovery
témakiíró
tudományág
témakiírás leírása
"Chemical space is a term coined in 1996 for the collection of compounds that are conceivable based on the known chemical rules of connecting atoms. Driven by the need to discover new chemical material to combat disease, medicinal chemists are exploring larger portions of this vast chemical universe, with the help of state-of-the-art computational modelling, cheminformatics and artificial intelligence workflows. In the Medicinal Chemistry Research Group at the Research Centre for Natural Sciences, we are developing new methods to enable the virtual screening of compound libraries with unprecedented size, to rationalize the time and cost burden of synthesizing and testing new pharmacologically active compounds.
The PhD candidate will participate in these research efforts, particularly in the development of new databases, methods and workflows that can make the vast universe of chemical and biological entities (such as compounds, pharmacological targets and their relevant binding sites) more interconnected and faster to navigate. In particular, new relational databases that can be hosted online to alleviate the computational burden of exploratory cheminformatics, bioinformatics and/or drug design workflows are a key objective of this research. Artificial intelligence (AI) methods will be a key focus in this PhD project, enhancing the efficiency and accessibility of the developed computational tools for drug design. Deploying GPU-accelerated or AI-enhanced algorithms will result in faster execution of complex tasks that will enable hosting a range of functionalities online, hopefully with real-time responsiveness. Ultimately, the developed tools should be user-friendly and accessible to the broader drug design community, enabling researchers with diverse backgrounds to effectively utilize these resources. The PhD research will fit into existing projects, where the diverse research portfolio of our group (CNS and oncological protein targets, protein-protein interactions, etc.) will provide relevant case studies for the experimental validation of the developed methods."
The PhD candidate will participate in these research efforts, particularly in the development of new databases, methods and workflows that can make the vast universe of chemical and biological entities (such as compounds, pharmacological targets and their relevant binding sites) more interconnected and faster to navigate. In particular, new relational databases that can be hosted online to alleviate the computational burden of exploratory cheminformatics, bioinformatics and/or drug design workflows are a key objective of this research. Artificial intelligence (AI) methods will be a key focus in this PhD project, enhancing the efficiency and accessibility of the developed computational tools for drug design. Deploying GPU-accelerated or AI-enhanced algorithms will result in faster execution of complex tasks that will enable hosting a range of functionalities online, hopefully with real-time responsiveness. Ultimately, the developed tools should be user-friendly and accessible to the broader drug design community, enabling researchers with diverse backgrounds to effectively utilize these resources. The PhD research will fit into existing projects, where the diverse research portfolio of our group (CNS and oncological protein targets, protein-protein interactions, etc.) will provide relevant case studies for the experimental validation of the developed methods."
felvehető hallgatók száma
1 fő
helyszín
HUN-REN
jelentkezési határidő
2025-12-07