Yehia Amar
Papers
1
Total Citations
139
H-Index
1
About
Yehia Amar is a leading figure at the intersection of machine learning and synthetic chemistry, whose work is pioneering the rational design of catalytic reactions. His research focuses on integrating computational tools—particularly molecular descriptors and multi-objective optimization—to solve complex challenges in asymmetric catalysis. Amar’s most impactful contribution, published in 2019, demonstrates how machine learning can replace trial-and-error solvent selection. In a study of the (acac)/Josiphos-catalyzed asymmetric hydrogenation of a chiral α-β unsaturated γ-lactam, he trained a multi-objective algorithm on just 25 initial solvents to simultaneously optimize for high conversion and high diastereomeric excess. This approach not only identified superior solvents but also revealed key molecular features driving selectivity, a breakthrough that has garnered 139 citations. By showing that data-driven models can outperform traditional screening in a field long dominated by empirical intuition, Amar has opened new pathways for accelerating catalyst discovery. His work stands as a model for how chemists can harness artificial intelligence to make reaction development faster, more sustainable, and more predictive.
Research Focus
Key Achievements
Top Papers
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