Role of computational methods in the era of precision medicine

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 biomolecules at the atomic level, which can be computationally very expensive and time-consuming. The thermodynamics and kinetics of drug-target binding play a crucial role in understanding and optimizing the interactions between drugs and their target molecules. To address this challenge, molecular dynamics (MD) and related approaches combined with machine learning and artificial intelligence play a crucial role in accelerating 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 and residence time. This talk overviews various enhanced sampling methods combined with machine learning in computational drug discovery, as well as their advantages and limitations. The talk focuses on using these methods for free energy and kinetics estimations, reporting on the significant limitations toward accurate estimations for large datasets of compounds. The talk then describes recent applications in drug discovery and discusses the challenges and limitations of these methodologies when applied to biological systems of increasing complexity. In conclusion, the talk illustrates how MD, enhanced sampling methods, and machine learning can improve the efficiency and effectiveness of computational drug discovery and accelerate the development of new therapeutics to treat significant unmet medical needs in the era of precision medicine.