Recognizing and generating knotted molecular structures by machine learning

TL;DR

This paper introduces machine learning models, including a Transformer-based neural network for fast knot recognition and a diffusion-based model for generating knotted conformations, advancing the understanding and design of knotted molecules.

Contribution

It presents the first neural network models capable of recognizing and generating knotted molecular structures efficiently, surpassing traditional mathematical methods in speed and accuracy.

Findings

Transformer NN achieves >99% accuracy in knot recognition

Recognition speed is 4500 times faster than mathematical methods

Generated conformations match desired knot types and physical distributions

Abstract

Knotted molecules occur naturally and are designed by scientists to gain special biological and material properties. Understanding and utilizing knotting require efficient methods to recognize and generate knotted structures, which are unsolved problems in mathematics and physics. Here, we solve these two problems using machine learning. First, our Transformer-based neural network (NN) can recognize the knot types of given chain conformations with an accuracy of $>99\%$. We can use a single NN model to recognize knots with different chain lengths, and our computational speed is about 4500 times faster than the most popular mathematical method for knot recognition: the Alexander polynomials. Second, we for the first time design a diffusion-based NN model to generate conformations for given knot types. The generated conformations satisfy not only the desired knot types, but also the correct physical distributions of the radii of gyration and knot sizes. The results have several implications. First, the Transformer is suitable for handling knotting tasks, probably because of its strength in processing sequence information, a key component in knotting. Second, our NN can replace mathematical methods of knot recognition for faster speed on many occasions. Third, our models can facilitate the design of knotted protein structures. Lastly, analyzing how NN recognizes knot types can provide insight into the principle behind knots, an unsolved problem in mathematics. We provide an online website (http://144.214.24.236) for using our models.