【直播】SELFIES and the future of molecular string representations

直播二维码

本次报告由IOP Publishing主办，于2021年8月13日21:00开始，授权蔻享学术进行网络直播。

Time:

09.00-12.00 EST // 14.00-17.00 BST // 15.00-18.00 CEST // 21.00-00.00 CST

Description:

Less than one year ago, IOP Publishing's Machine Learning: Science &Technology (MLST) journal published SELFIES: a 100% robust molecular string representation. It has since then fuelled numerous Artificial Intelligence (AI) applications in material science and chemistry. To our delight, it has also been the most downloaded and cited paper published in MLST to date.

In partnership with IOP Publishing and the Acceleration Consortium, we want to celebrate this occasion with a mini-workshop. Together with the community, we will look into the future and discuss open challenges and opportunities for string-based representations in chemistry. We will kick-off new exciting applications to advance AI for material science in a number of working groups.

Preliminary Program (in EST)

09.00-10.00

- Introduction to SELFIES -- Prof. Alan Aspuru-Guzik (University of Toronto)

- Tutorial on the application of SELFIES  -- Akshat Nigam (University of Toronto & Stanford University)

10.00-12.00

- Working groups to kick-off new applications and extensions of SELFIES and general molecular string representations

- Short conclusions from working groups and strategy of how to push further

https://iopscience.iop.org/article/10.1088/2632-2153/aba947

https://github.com/aspuru-guzik-group/selfies

Speaker bios

Alán Aspuru-Guzik's research lies at the interface of computer science with chemistry and physics. He works in the integration of robotics, machine learning and high-throughput quantum chemistry for the development of “self-driving laboratories”, which promise to accelerate the rate of scientific discovery. Alán also develops quantum computer algorithms for quantum machine learning and has pioneered quantum algorithms for the simulation of matter. Jointly appointed Professor of Chemistry and Computer Science at the University of Toronto. Previously, full professor at Harvard University. Director of the Acceleration Consortium, a global community dedicated to accelerated molecular and materials discovery. Co-founder of Zapata Computing and Kebotix, two early-stage ventures in quantum computing and self-driving laboratories respectively.
AkshatKumar (Akshat) Nigam is a Computer Science PhD student at Stanford University. Before starting at Stanford, he studied at the University of Toronto, actively doing research in Alan Aspuru-Guzik's MatterLab. There, he helped in the development of SELFIES, along with its applications to inverse molecular design.

编辑：黄琦

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