PhD, Harvard University
Abstract:Light-activated drugs are a promising way to treat localized diseases for which existing treatments have severe side effects. However, their development is complicated by the set of photophysical and biological properties that must be simultaneously optimized. For example, photoactive drugs based on trans–cis isomerization must isomerize under light, absorb in the near-IR, have reasonably long cis lifetimes, and have differential cis–trans binding to a protein target. To accelerate the design of photoactive drugs, we develop new computational methods for predicting their properties. These techniques combine atomistic simulation with machine learning based on quantum chemistry. They enable the prediction of the isomerization efficiency, absorption spectrum, thermal half-life, and binding affinity. We use these tools to screen 5 million hypothetical ligands for the photoactive inhibition of the PARP1 cancer target. We identify several compounds with redshifted absorption spectra, ideal thermal half-lives, and differential protein binding under illumination. These results show that computation can help address the difficult optimization problem that is central to photoactive drug design.
Speaker Bio: Simon Axelrod received his BSc at Queen’s University in 2016, with a major in Physics. He received his MSc in Physics at the University of Toronto in 2017. He worked under Prof. Paul Brumer in the Chemistry department, developing theoretical models for understanding quantum effects in biology. He received his PhD in Chemical Physics from Harvard University in 2023. He worked under Prof. Eugene Shakhnovich (Chemistry) and Prof. Rafael Gomez-Bombarelli (Materials Science and Engineering, MIT), combining atomistic simulation with machine learning to accelerate drug discovery. In his spare time, Simon likes playing basketball, joking around with friends, and writing short bios.
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