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, Physicsbased 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 provides an overview of various enhanced sampling methods in computational drug discovery, their advantages, and limitations. The talk then focuses on the use of these methods for free energy and kinetics estimations, reporting on the major limitations toward accurate estimations for large datasets of compounds. Finally, the talk describes recent applications in anticancer drug discovery, with a particular focus on the computational approaches used to identify synthetic lethality targets and design drugs that can exploit this innovative paradigm. 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. In conclusion, the talk illustrates how enhanced sampling methods have the potential to significantly improve the efficiency and effectiveness of computational drug discovery and accelerate the development of new therapeutics for the treatment of cancer and other unmet medical needs.
Nicola Marzari and Michel Steinmetz
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