On November 11th of 2018, the SKLEOC invited Prof. Luhua Lai of Peking University for a visit as
part of the academic activities in honor of the 100th birth
anniversary of Prof. Ruyu Chen, a prestigious orgnaophosphorus chemist. Prof.
Lai gave a speech entitled “Applications and Insight of Big Data and Artificial
Intelligence in Chemical Research and Drug Discovery”. The lecture was hosted
by Prof. Zhengming Li.
Prof. Lai first introduced
the background that big data and artificial intelligence (AI) have grown to be
a new motivation for scientific developments. She then thoroughly discussed
various cases in chemical studies and drug discoveries where AI played a vital
role. She then gave further insight into the opportunities and challenges that
AI can bring to the field of scientific research. At the end of her talk, Prof.
Lai pointed out a new scientific revolution brought about by the combination of
deep learning and big data, and that chemical research must begin to appreciate
the opportunities that AI can have. She further emphasized the importance of
raising related talent in this field. After the talk, the audience had in depth
discussion with Prof. Lai.
Prof. Luhua Lai is currently professor of the
college of chemistry of Peking University, director of the institute of
physical chemistry and deputy director of the center for Quantitative Biology.
She received her bachelor degree in 1984 from Peking University and further
obtained her Ph.D. in the same university in 1989. She took an academic
position in Peking University by July of 1989, and was promoted to full
professor from lecturer. She was once supported by the National Science Fund
for Distinguished Young Scholars, and she was a part of the Chang Jiang
Scholars Program. In 1998-1999 she was a visiting scholar at UC Berkeley. Prof.
Lai’s main research interest is in the interdisciplinary field of physical
chemistry and life sciences. Her current areas include: 1) design of functioned
proteins; 2) design and applications of drug designs based on structure and
systems biology; 3) structure modification of proteins and modifications of
intrinsically disordered proteins; 4) mechanisms of metabolic network of cancer
cells and inflammation networks; 5) application of deep learning in
chemoinformatics and computational biology.
