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A multitude of efforts have been undertaken to leverage the capabilities of innovative artificial intelligence and extensive language models (LLMs) to forecast the results of novel chemical reactions. These attempts have encountered limited achievements, partly because they have not been anchored in a comprehension of fundamental physical concepts, such as the principles of mass conservation. Recently, a group of scientists at MIT has devised a method to integrate these physical limitations into a reaction prediction model, significantly enhancing the precision and dependability of its results.
The recent study was published on Aug. 20 in the journal Nature, authored by recent postdoctoral researcher Joonyoung Joung (currently an assistant professor at Kookmin University, South Korea); former software developer Mun Hong Fong (now at Duke University); chemical engineering graduate student Nicholas Casetti; postdoc Jordan Liles; physics undergraduate student Ne Dassanayake; and lead author Connor Coley, who holds the Class of 1957 Career Development Professorship in the MIT departments of Chemical Engineering and Electrical Engineering and Computer Science.
“The forecasting of reaction results is a crucial task,” Joung elaborates. For instance, if one aims to create a new medication, “it is essential to understand how to synthesize it. This necessitates knowing which products are likely” to emerge from a particular combination of chemical inputs in a reaction. However, prior attempts at such forecasts primarily focus on a set of inputs and corresponding outputs, neglecting the intermediate phases or the constraints required to ensure that no mass is added or subtracted during the process, which is unfeasible in real reactions.
Joung emphasizes that while extensive language models like ChatGPT have excelled in various research domains, these models fail to constrain their outputs to physically plausible options, such as adhering to mass conservation principles. These models utilize computational “tokens,” which in this context correspond to individual atoms, but “if you do not preserve the tokens, the LLM model begins to create new atoms or remove atoms during the reaction.” Rather than being anchored in genuine scientific knowledge, “this resembles alchemy,” he remarks. While many attempts at predicting reactions merely consider the end products, “we aim to monitor all the chemicals, as well as how they transform” throughout the reaction sequence, he states.
To tackle this issue, the team applied a technique developed in the 1970s by chemist Ivar Ugi, which employs a bond-electron matrix to depict the electrons in a reaction. They utilized this framework as the foundation for their novel program, dubbed FlowER (Flow matching for Electron Redistribution), which enables them to meticulously track all the electrons in the reaction, ensuring that none are incorrectly added or removed throughout the process.
The system employs a matrix to symbolize the electrons in a reaction, using nonzero values to signify bonds or lone electron pairs, while zeros indicate their absence. “This allows us to conserve both atoms and electrons simultaneously,” states Fong. This representation, he notes, was pivotal to incorporating mass conservation into their prediction system.
The system they developed remains in an early phase, Coley mentions. “As it stands, the system serves as a demonstration — a proof of concept that this generative methodology of flow matching is particularly well-suited for predicting chemical reactions.” Although the team is enthusiastic about this promising approach, he adds, “we recognize that it does have specific constraints regarding the variety of different chemistries it has encountered.” Despite being trained on data from over a million chemical reactions sourced from a U.S. Patent Office database, these datasets do not include certain metals and specific types of catalytic reactions, he notes.
“We are extremely thrilled about the capability to obtain such dependable predictions of chemical mechanisms” from the existing system, he mentions. “It preserves mass, it preserves electrons, but we acknowledge that there is considerable room for expansion and enhancement in the years to come as well.”
Yet, even in its current form, which is available for free through the online platform GitHub, “we believe it will deliver accurate predictions and serve as a useful tool for evaluating reactivity and mapping reaction pathways,” Coley states. “While we are looking toward the future to significantly advance the mechanistic understanding and assist in the invention of new reactions, we’re not quite there yet. However, we hope this will serve as a steppingstone toward that goal.”
“Everything is open source,” asserts Fong. “The models, the data, all of it is accessible,” including a previous dataset created by Joung that comprehensively outlines the mechanistic steps of known reactions. “I consider us to be one of the pioneering teams in generating this dataset, making it available as open-source, and ensuring it is usable for everyone,” he states.
The FlowER model matches or exceeds existing methods in identifying conventional mechanistic pathways, the team claims, and allows for generalization to previously unseen types of reactions. They suggest that the model could be applicable for anticipating reactions in medicinal chemistry, materials discovery, combustion, atmospheric chemistry, and electrochemical systems.
In their comparisons with current reaction prediction systems, Coley asserts, “utilizing the architectural choices we’ve implemented results in a significant boost in validity and conservation, and we achieve comparable or slightly better accuracy in terms of performance.”
He continues to explain that “what distinguishes our approach is that while we employ these textbook principles of mechanisms to generate this dataset, we anchor the reactants and products in experimentally verified data drawn from patent literature.” They are deducing the fundamental mechanisms, he states, rather than merely fabricating them. “We are inferring them from experimental data, and this is not something that has been executed and shared onsuch a scale previously.”
The next phase, he emphasizes, is “we are quite eager to enhance the model’s comprehension of metals and catalytic cycles. We’ve only begun to explore this initial publication,” and most of the reactions included so far do not feature metals or catalysts, “thus, this is a direction we are very interested in.”
In the long run, he mentions, “a large portion of the enthusiasm lies in using this type of system to facilitate the discovery of new complex reactions and to help elucidate novel mechanisms. I believe that the long-term potential impact is substantial, but this is, of course, just a preliminary step.”
The research was funded by the Machine Learning for Pharmaceutical Discovery and Synthesis consortium and the National Science Foundation.
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