With a big assist from artificial intelligence and a heavy dose of human touch, Tim Cernak’s lab at the University of Michigan made a discovery that dramatically speeds up the time-consuming chemical process of building molecules that will be tomorrow’s medicines, agrichemicals, or materials.

The discovery, published in the Feb. 3 issue of Science, is the culmination of years of chemical synthesis and data science research by the Cernak Lab in the College of Pharmacy and Department of Chemistry.

The goal of the research was to identify key reactions in the synthesis of a molecule, ultimately reducing the process to as few steps as possible. In the end, Cernak and his team achieved the synthesis of a complex alkaloid found in nature in just three steps. Previous syntheses took between seven and 26 steps. 

“Making a chemical structure that has atoms in just the right place to give you efficacious and nontoxic medicines, for instance, is tricky,” said Cernak, assistant professor of medicinal chemistry and chemistry. “It requires a chemical synthesis strategy grounded in the chemical building blocks you can actually buy and then stitch together using chemical reactions.”

The accomplishment has powerful implications for speeding up the development of medicines.

Cernak compared the construction of these complex molecules to playing chess. You need to orchestrate a series of moves to get to the end of the game. While there’s a near-infinite number of possible moves, there’s a logic that can be followed.

“We developed a logic here, based on graph theory, to get to the end as quickly as possible,” he said.

Cernak and colleagues used SYNTHIA Retrosynthesis Software, which provides scientists with a database of pathways, or steps, and formulas for millions of molecular structures. This gave the team an enormous amount of computational synthesis data to play with.

Using an algorithm they developed to curate the data, the researchers identified the steps along the pathway that was high impact, or key steps, and the steps that were making progress toward completing the synthesis but ultimately inefficient for the whole process.

“We hope this research can lead to better medicines,” Cernak said. “So far, we have been limited in the molecular structures we can quickly access with chemical synthesis.”

Co-authors include Yingfu Lin, a senior research fellow in pharmacy; Rui (Sam) Zhang, a doctoral student in chemistry; and Di Wang, a doctoral student in pharmacy.

Players and coaches for the Philadelphia Eagles and Kansas City Chiefs will spend hours and hours in film rooms this week in preparation for the Super Bowl. They’ll study positions, plays, and formations, trying to pinpoint what opponent tendencies they can exploit while looking to their film to shore up weaknesses.

New artificial intelligence technology being developed by engineers at Brigham Young University could significantly cut down on the time and cost that goes into film study for Super Bowl-bound teams (and all NFL and college football teams), while also enhancing game strategy by harnessing the power of big data.

BYU professor D.J. Lee, master’s student Jacob Newman and Ph.D. students Andrew Sumsion and Shad Torrie are using AI to automate the time-consuming process of analyzing and annotating game footage manually. Using deep learning and computer vision, the researchers have created an algorithm that can consistently locate and label players from game film and determine the formation of the offensive team — a process that currently requires a slew of video assistants.

“We were having a conversation about this and realized, whoa, we could probably teach an algorithm to do this,” said Lee, a professor of electrical and computer engineering. “So we set up a meeting with BYU football to learn their process and immediately knew, yeah, we can do this a lot faster.”

While still early in the research, the team has already obtained better than 90% accuracy on player detection and labeling with their algorithm, along with 85% accuracy on determining formations. They believe the technology could eventually eliminate the need for the inefficient and tedious practice of manual annotation and analysis of recorded video used by NFL and college teams.

Lee and Newman first looked at real game footage provided by BYU’s football team. As they started to analyze it, they realized they needed some additional angles to properly train their algorithm. So they bought a copy of Madden 2020, which shows the field from above and behind the offense, and manually labeled 1,000 images and videos from the game.

They used those images to train a deep-learning algorithm to locate the players, which then feeds into a Residual Network framework to determine what position the players are playing. Finally, their neural network uses the location and position information to determine what formation (of more than 25 formations) the offense is using — anything from the Pistol Bunch TE to the I Form H Slot Open.

Lee said the algorithm can accurately identify formations at 99.5% when the player location and labeling information is correct. The I Formation, where four players are lined up one in front of the next — center, quarterback, fullback, and running back — proved to be one of the most challenging formations to identify.

Lee and Newman said the AI system could also have applications in other sports. For example, in baseball, it could locate player positions on the field and identify common patterns to assist teams in refining how they defend against certain batters. Or it could be used to locate soccer players to help determine more efficient and effective formations.

The BYU algorithm is detailed in a journal article “Automated Pre-Play Analysis of American Football Formations Using Deep Learning,” recently published in a special issue of Advances of Artificial Intelligence and Vision Applications in Electronics.

“Once you have this data there will be a lot more you can do with it; you can take it to the next level,” Lee said. “Big data can help us know the strategies of this team or the tendencies of that coach. It could help you know if they are likely to go for it on 4th Down and 2 or if they will punt. The idea of using AI for sports is really cool, and if we can give them even 1% of an advantage, it will be worth it.”