Transferring Chemical and Energetic Knowledge Between Molecular Systems
with Machine Learning
- URL: http://arxiv.org/abs/2205.03339v1
- Date: Fri, 6 May 2022 16:21:00 GMT
- Title: Transferring Chemical and Energetic Knowledge Between Molecular Systems
with Machine Learning
- Authors: Sajjad Heydari, Stefano Raniolo, Lorenzo Livi, Vittorio Limongelli
- Abstract summary: We propose a novel methodology for transferring knowledge obtained from simple molecular systems to a more complex one.
We focus on the classification of high and low free-energy states.
Our results show a remarkable AUC of 0.92 for transfer learning from tri-alanine to the deca-alanine system.
- Score: 5.27145343046974
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Predicting structural and energetic properties of a molecular system is one
of the fundamental tasks in molecular simulations, and it has use cases in
chemistry, biology, and medicine. In the past decade, the advent of machine
learning algorithms has impacted on molecular simulations for various tasks,
including property prediction of atomistic systems. In this paper, we propose a
novel methodology for transferring knowledge obtained from simple molecular
systems to a more complex one, possessing a significantly larger number of
atoms and degrees of freedom. In particular, we focus on the classification of
high and low free-energy states. Our approach relies on utilizing (i) a novel
hypergraph representation of molecules, encoding all relevant information for
characterizing the potential energy of a conformation, and (ii) novel message
passing and pooling layers for processing and making predictions on such
hypergraph-structured data. Despite the complexity of the problem, our results
show a remarkable AUC of 0.92 for transfer learning from tri-alanine to the
deca-alanine system. Moreover, we show that the very same transfer learning
approach can be used to group, in an unsupervised way, various secondary
structures of deca-alanine in clusters having similar free-energy values. Our
study represents a proof of concept that reliable transfer learning models for
molecular systems can be designed paving the way to unexplored routes in
prediction of structural and energetic properties of biologically relevant
systems.
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