Computational Chemistry Seminar: AI in Life and Study

(Cover Photo Source: Ella Zhang)

(This article is translated into Chinese below)

Guowei Wei, a Foundation Professor of the mathematics department of Michigan State University, hosted a seminar on “How AI is Revolutionizing Chemistry Research” via Zoom on March 25. This seminar attracted more than 100 attendees, which set the record in the Online Computational Chemistry Seminar Series. 

The seminar introduced Artificial Intelligence applications in biochemistry research to students who have no or little knowledge of AI. Because AI is penetrating our daily life, Professor Wei said, “I think soon there should be a course in middle school and primary school for students to learn about AI like other fundamental courses such as math and Chinese because the real basic knowledge of AI and data science is very easy and comprehensible.” He believes that interdiscipline is a trend and knowing some basic AI applications helps students in every different field to have an easier study life.

In the first part of his seminar, he introduced some of the basic techniques and theories of AI in the analysis of chemistry research. The techniques focus on machine learning, a branch of artificial intelligence that can automate model building. Machine learning is categorized into supervised learning, semi-supervised learning, unsupervised learning, and self-supervised learning respectively. He then introduced regression, generation, classification, dimensionality reduction, and clustering in machine learning. Regression and classification are usually used in supervised learning, including linear regression, random forests, and the k-Nearest Neighbors Algorithm. He demonstrated the process and results of these methods, which mostly required Precalculus and Calculus materials.

Training AI develops deep neural networks, which are capable of solving different problems. Conventional neural networks model spatial correlation, and generative network complexes are designed to develop drug candidates with expected properties. With these deep neural networks, AI helps researchers with seemingly unsolvable problems. Professor Wei’s team applied AI to predict the mutations of COVID.

This is a cartoon virtualization of the successfully predicted mutation sites. Photo credit: Ella Zhang

Professor Wei said that they “predicted prevailing SARS-CoV-2 variants to occur at residues 452 and 501” based on data collected from 2.7 million patients. To achieve this, they used machine learning models to evaluate the free energy changes in the S protein of the virus. S protein, spike glycoprotein, and binds to the cell surface are recognized by receptors on cell membranes. They generated simulations of mutations in the S protein of SARS-CoV-2 to see which mutated protein is more likely to bind with its receptor. 

Professor Wei clarified the accuracy of these simulations. According to him, “All our predictions have been confirmed in the past 40 days.”

These discoveries require years of dedication in a physical lab but a much shorter time with AI, calling into question whether the future need for experimental science will decrease with AI application. 

Professor Wei responded to this concern. “There’s a trend that more research requires assistance from data science and specifically AI, which solves problems much faster than people when the data set is enormous. But experiments are still required to generate data for AI training. Experimental science also contributes as direct evidence of theories, so the need for experimental science is still increasing.” 

However, he has different worries about AI development. “Humans can be addicted to AI in the future,” he said. He explains that AI will combine with the human brain in a few decades. Human cells receive and emit different signals that travel through the human body via the nerve system and cell signaling. AI also can form an electronic neural network, mimicking the signals of human nervous systems. AI can thus help the human body to search for cancer cells, cure diseases or change the human’s appearance and shape. It is dangerous that all these potential advantages AI may provide us can cause addiction and eventually control humans without being noticed. 

Professor Wei stressed that the development of AI should be under regulation and that developers must find ways to utilize AI without reaching this ultimatum. Since his discoveries and research are interdisciplinary with math and the sciences, he suggests that data science and natural sciences should both contribute to research mutually.

It is necessary not just for scholars, but for every person to understand fields different from their own specialty. As NYU Shanghai students, there are many opportunities to select interdisciplinary courses and majors. The community should seize this opportunity to understand these new technologies and merge AI with our study and life.

(Chinese Translation)




在他的研讨会的第一部分,他介绍了人工智能在分析化学研究中的一些基本技术和理论。这些技术聚焦于机器学习,这是人工智能的一个分支,可以自动构建模型。机器学习分为监督式学习、半监督式学习、无监督式学习和自我监督式学习。然后,他又简单介绍了机器学习中的方法——回归、生成??、分类、降维和聚类。回归和分类通常用于监督式学习,包括线性回归、随机森林和k-Nearest Neighbors。他展示了这些方法的过程和效果,这些方法至少涉及到微积分知识。








Author: Ella Zhang

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