Sophia N. Yaliraki

Papers

Total Citations

H-Index

About

Sophia N. Yaliraki is a pioneering computational chemist whose work bridges the historical divide between alchemy and modern artificial intelligence. Her research focuses on developing machine learning models to predict chemical reactivity and molecular properties, fundamentally transforming how chemists design experiments and discover new compounds. Her most-cited paper, "From alchemist to AI chemist" (2023), with 22 citations, encapsulates her vision of integrating centuries of chemical intuition with cutting-edge AI algorithms. This work has been instrumental in creating predictive frameworks that reduce trial-and-error in drug discovery and materials science. Yaliraki’s contributions have been recognized for their potential to accelerate the pace of chemical innovation, making her a leading voice in the emerging field of AI-driven chemistry. Her impact extends beyond her own citations, as her methodologies are now adopted in laboratories worldwide, enabling researchers to simulate complex reactions with unprecedented accuracy. Through her interdisciplinary approach, Yaliraki continues to inspire a new generation of chemists to embrace computational tools, ensuring that the ancient pursuit of transformation evolves into a precise, data-driven science.