This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Discovering new molecules for specific uses is a major driver of progress in a wide variety of industries, whether it's pharmaceutical companies looking for new drugs or electronics or energy companies seeking new materials with particular properties. But it's also largely a trial-and-error process, because there is not yet any way to perfectly predict how a given chemical compound will behave in terms of the myriad different properties and responses materials can exhibit.
Recent progress has been made on using artificial intelligence and machine learning to supplement or speed up laborious calculations based on physical principles that can at least provide some initial guidance on which compounds might be worth synthesizing and testing. Now, a new method developed at MIT shows that when there are few data on some technologically relevant property, such as ligand affinities for a recently discovered cellular target for a potential drug, this approach can outperform other existing methods for winnowing down the field of possible molecules that might be worth looking at. The findings were reported in the Journal of Chemical Information and Modeling, in a paper by MIT graduate student Akshat Shirish Zalte and recent graduate Jackson Burns Ph.D. '26, research scientist Charlles Abreu, professor William Green and three researchers from the chemical company BASF.
An earlier version of the paper has been online since last year, and the open-source model the team developed, called CheMeleon, has already found regular use by several pharmaceutical companies. CheMeleon was built using ChemProp, an open-source machine-learning package also developed and maintained by the Green group at MIT, and anyone can download CheMeleon through ChemProp and fine-tune it for their own purposes. Earlier this year, there was a contest organized by OpenADMET for trying to predict the properties of a variety of new molecules that had not previously been synthesized, says Green, who is the Hoyt Hottel Professor of Chemical Engineering.
"It was a big contest; a lot of people participated," he says. When the winners were announced, it turned out that nine of the top 20 entries had used CheMeleon in their process. "So then we started getting all kinds of inquiries from the pharma companies about this, but our paper had just been submitted; it wasn't even peer-reviewed or anything yet." While the paper went through that review process, its influence was already spreading.
At least one pharma company, Green says, "sent a message to all their people and said, 'Anytime you're modeling a property, start with CheMeleon. That's our new company policy'" when making initial decisions about which molecules to pursue. The key to CheMeleon was pretraining a machine-learning model with detailed data about a million different molecules, so that it gets to learn correlations between the structure of a given molecule and how it behaves with respect to a catalog of about 1,600 descriptors that have been developed by chemists over decades.
CheMeleon can then be fine-tuned to predict properties ranging from well-known ones like solubility and viscosity to more esoteric ones such as bioactivity or reactivity with a certain protein or enzyme. Those million molecules are a minuscule fraction of the innumerable ways that atoms can bond together, but they provide enough examples so that once the AI system has been trained on them, it can work out on its own many underlying patterns and relationships, and then apply those to a new, unknown molecule that's presented to it. "Most of today's deep-learning models require more data than traditional methods," says Zalte.
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