Computational Approaches to Drug Discovery in the Era of Precision Medicine
Event details
| Date | 09.11.2023 |
| Hour | 11:15 › 13:30 |
| Speaker | Prof. Dr. Andrea Cavalli |
| Location | |
| Category | Inaugural lectures - Honorary Lecture |
| Event Language | English |
Abstract
Computational drug discovery has become an increasingly important tool in the search for new drugs to treat a wide range of diseases. One of the critical challenges in this field is simulating the complex behavior of molecules at the atomic level, which can be computationally very expensive and time-consuming. To overcome this challenge, physics-based computational approaches (e.g., enhanced sampling methods) have been developed to accelerate the exploration of the conformational space of molecules. These methods also enable researchers to compute the free energy differences between different states of a molecule and estimate thermodynamic and kinetics properties such as binding free energies, residence time, etc. By using enhanced sampling methods, researchers can more efficiently search for potential drug candidates, screen databases of compounds, and optimize the properties of existing drug molecules. This talk overviews various enhanced sampling methods in computational drug discovery, their advantages, and limitations. The discussion then focuses on using these methods for free energy and kinetics estimations, reporting on the significant limitations toward accurate estimates for large datasets of compounds.
The second part of the discussion deals with recent applications in anticancer drug discovery, focusing on the computational approaches used to identify synthetic lethality targets and design drugs that can exploit this paradigm. Particular attention will be given to genomics analysis, computational methods, and drug repurposing/discovery for personalized and precision treatments. We also discuss the challenges and limitations, including the need for comprehensive data on genetic alterations in cancer cells and the optimization of drug delivery for next-generation therapeutics. In conclusion, the talk illustrates how enhanced sampling methods can significantly improve the efficiency and effectiveness of computational drug discovery and, along with genomics analysis, the development of new therapeutics to treat cancer via precision medicine strategies.
References
Online event: https://epfl.zoom.us/j/64852867309
Computational drug discovery has become an increasingly important tool in the search for new drugs to treat a wide range of diseases. One of the critical challenges in this field is simulating the complex behavior of molecules at the atomic level, which can be computationally very expensive and time-consuming. To overcome this challenge, physics-based computational approaches (e.g., enhanced sampling methods) have been developed to accelerate the exploration of the conformational space of molecules. These methods also enable researchers to compute the free energy differences between different states of a molecule and estimate thermodynamic and kinetics properties such as binding free energies, residence time, etc. By using enhanced sampling methods, researchers can more efficiently search for potential drug candidates, screen databases of compounds, and optimize the properties of existing drug molecules. This talk overviews various enhanced sampling methods in computational drug discovery, their advantages, and limitations. The discussion then focuses on using these methods for free energy and kinetics estimations, reporting on the significant limitations toward accurate estimates for large datasets of compounds.
The second part of the discussion deals with recent applications in anticancer drug discovery, focusing on the computational approaches used to identify synthetic lethality targets and design drugs that can exploit this paradigm. Particular attention will be given to genomics analysis, computational methods, and drug repurposing/discovery for personalized and precision treatments. We also discuss the challenges and limitations, including the need for comprehensive data on genetic alterations in cancer cells and the optimization of drug delivery for next-generation therapeutics. In conclusion, the talk illustrates how enhanced sampling methods can significantly improve the efficiency and effectiveness of computational drug discovery and, along with genomics analysis, the development of new therapeutics to treat cancer via precision medicine strategies.
References
- Decherchi S, Cavalli A. Chem Rev. 2020, 120, 12788-12833.
- Bernetti M, Masetti M, Rocchia W, Cavalli A. Annu Rev Phys Chem. 2019, 70, 143-171.
Online event: https://epfl.zoom.us/j/64852867309
Practical information
- General public
- Free
Organizer
- Institute of Chemical Sciences and Engineering – ISIC