Estimating pH-dependent rates by machine learning techniques S23
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Description

Molecular Dynamics (MD) simulations are a powerful tool to study the behavior of molecules at the atomic level. A wide range of computational methods have been developed to analyze MD simulations and to extract meaningful information such as transition rates. The purpose of the seminar is to study and extend these methods to determine how transition rates of molecular systems are affected by the pH of the environment [1,2]. The results of this project will be relevant in studying the μ-opioid receptor system, whose activation and the emergence of adverse effects by opioids depend on the pH of the cell membrane within which it resides [3].
The project is highly interdisciplinary and students from physics, chemistry, and mathematics are encouraged to participate.

Website https://www.zib.de/userpage/donati/

 

Literatur

 

1. L. Donati, and M. Weber. In: J. Chem. Phys. 157.22 (2022), p. 224103.
2. R. J. Rabben, S. Ray, and M. Weber. In: J. Chem. Phys. 153.11 (2020), p. 114109.
3. G. Del Vecchio, et al. In: Sci. Rep. 9 (2019), p. 19344.

Basic Course Info

Course No Course Type Hours
19247911 Seminar 2

Time Span 28.04.2023 - 21.07.2023
Instructors
Luca Donati

Study Regulation

0089c_MA120 2014, MSc Informatik (Mono), 120 LPs
0280b_MA120 2011, MSc Mathematik (Mono), 120 LPs
0280c_MA120 2018, MSc Mathematik (Mono), 120 LP

Estimating pH-dependent rates by machine learning techniques S23
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Main Events

Day Time Location Details
Friday 10-12 2023-04-28 - 2023-07-21

Estimating pH-dependent rates by machine learning techniques S23
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