Papers
1
Total Citations
11
H-Index
1
About
Yi An is a rising computational chemist whose work sits at the intersection of drug discovery and artificial intelligence. His primary research focus is the development of innovative in silico methods to identify chemical probes for challenging, “undruggable” protein targets. An’s most notable contribution is the creation of FRASE-bot (FRagment-based hit-finding robot), a novel computational platform that integrates fragment-based screening with machine learning to accelerate the discovery of small-molecule inhibitors. In a landmark 2024 study, he applied FRASE-bot to successfully identify CIB1-directed anti-tumor agents, demonstrating the platform’s power to uncover hits against proteins lacking known ligands. This work, already garnering 11 citations in its first year, has been recognized for its potential to transform early-stage drug discovery by dramatically reducing the time and cost of hit identification. An’s research is particularly impactful for students and researchers interested in the convergence of cheminformatics, structural biology, and AI, offering a practical blueprint for tackling some of the most stubborn targets in oncology and beyond.
Research Focus
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Top Papers
- 1