Research Project Title:
Uncertainty Quantification for Molecular Property Prediction
abstract:In Professor Barzilay’s lab, I have worked closely with Chemprop, a molecular property prediction tool that uses machine learning to offer improved performance when compared to traditional methods, such as circular fingerprints. Last semester, I worked to produce a survey of uncertainty quantification methods, which aim to estimate a model’s accuracy on any particular input. This semester, I will be finishing up the survey and transitioning into work on mechanism prediction for antibiotics. Discovering exactly how an antibiotic kills bacteria is necessary for drug approval and remains a huge bottleneck in the research process. We believe that Chemprop could greatly accelerate this step.
"I am very excited to further develop my research skills by participating in this SuperUROP. I am especially interested in the work done in Professor Barzilay's lab because I believe it to be both high impact and technically challenging. Beyond expanding my knowledge of deep learning, an area I've focused on, this research will also help boost the efficiency of workers in the pharmaceutical industry."