Protein Design by Integrating Machine Learning with Quantum Annealing and Quantum-inspired Optimization

• URL: http://arxiv.org/abs/2407.07177v1
• Date: Tue, 9 Jul 2024 18:42:45 GMT
• Title: Protein Design by Integrating Machine Learning with Quantum Annealing and Quantum-inspired Optimization
• Authors: Veronica Panizza, Philipp Hauke, Cristian Micheletti, Pietro Faccioli,
• Abstract summary: The protein design problem involves finding polypeptide sequences folding into a given threedimensional structure. Recent machine learning breakthroughs have enabled accurate and rapid structure predictions. We introduce a general protein design scheme where algorithmic and technological advancements in machine learning and quantum-inspired algorithms can be integrated.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The protein design problem involves finding polypeptide sequences folding into a given threedimensional structure. Its rigorous algorithmic solution is computationally demanding, involving a nested search in sequence and structure spaces. Structure searches can now be bypassed thanks to recent machine learning breakthroughs, which have enabled accurate and rapid structure predictions. Similarly, sequence searches might be entirely transformed by the advent of quantum annealing machines and by the required new encodings of the search problem, which could be performative even on classical machines. In this work, we introduce a general protein design scheme where algorithmic and technological advancements in machine learning and quantum-inspired algorithms can be integrated, and an optimal physics-based scoring function is iteratively learned. In this first proof-of-concept application, we apply the iterative method to a lattice protein model amenable to exhaustive benchmarks, finding that it can rapidly learn a physics-based scoring function and achieve promising design performances. Strikingly, our quantum-inspired reformulation outperforms conventional sequence optimization even when adopted on classical machines. The scheme is general and can be easily extended, e.g., to encompass off-lattice models, and it can integrate progress on various computational platforms, thus representing a new paradigm approach for protein design.

Related papers

• Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization [44.356888079704156]
  Protein engineering is a daunting task due to the vast sequence space of any given protein. Protein engineering is typically conducted through an iterative process of adding mutations to the wild-type or lead sequences. We propose a tree search-based bandit learning method, which expands a tree starting from the initial sequence with the guidance of a bandit machine learning model.
  arXiv  Detail & Related papers  (2024-01-08T06:33:27Z)
• Quantum algorithms: A survey of applications and end-to-end complexities [90.05272647148196]
  The anticipated applications of quantum computers span across science and industry. We present a survey of several potential application areas of quantum algorithms. We outline the challenges and opportunities in each area in an "end-to-end" fashion.
  arXiv  Detail & Related papers  (2023-10-04T17:53:55Z)
• Predicting RNA Secondary Structure on Universal Quantum Computer [2.277461161767121]
  It is the first step for understanding how RNA structure folds from base sequences that to know how its secondary structure is formed. Traditional energy-based algorithms are short of precision, particularly for non-nested sequences. Gate model algorithms for universal quantum computing are not available.
  arXiv  Detail & Related papers  (2023-05-16T15:57:38Z)
• The Basis of Design Tools for Quantum Computing: Arrays, Decision Diagrams, Tensor Networks, and ZX-Calculus [55.58528469973086]
  Quantum computers promise to efficiently solve important problems classical computers never will. A fully automated quantum software stack needs to be developed. This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.
  arXiv  Detail & Related papers  (2023-01-10T19:00:00Z)
• Polynomial unconstrained binary optimisation inspired by optical simulation [52.11703556419582]
  We propose an algorithm inspired by optical coherent Ising machines to solve the problem of unconstrained binary optimization. We benchmark the proposed algorithm against existing PUBO algorithms, and observe its superior performance. The application of our algorithm to protein folding and quantum chemistry problems sheds light on the shortcomings of approxing the electronic structure problem by a PUBO problem.
  arXiv  Detail & Related papers  (2021-06-24T16:39:31Z)
• Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms [71.62575565990502]
  We prove that the generalization error of an optimization algorithm can be bounded on the complexity' of the fractal structure that underlies its generalization measure. We further specialize our results to specific problems (e.g., linear/logistic regression, one hidden/layered neural networks) and algorithms.
  arXiv  Detail & Related papers  (2021-06-09T08:05:36Z)
• EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models [53.17320541056843]
  We propose a fully-differentiable approach for protein structure optimization, guided by a data-driven generative network. Our EBM-Fold approach can efficiently produce high-quality decoys, compared against traditional Rosetta-based structure optimization routines.
  arXiv  Detail & Related papers  (2021-05-11T03:40:29Z)
• Tequila: A platform for rapid development of quantum algorithms [0.3248699949578586]
  Tequila is a development package for quantum algorithms in python. It is designed for fast and flexible implementation, prototyping, and deployment of novel quantum algorithms in electronic structure and other fields.
  arXiv  Detail & Related papers  (2020-11-05T19:00:58Z)
• AdaLead: A simple and robust adaptive greedy search algorithm for sequence design [55.41644538483948]
  We develop an easy-to-directed, scalable, and robust evolutionary greedy algorithm (AdaLead) AdaLead is a remarkably strong benchmark that out-competes more complex state of the art approaches in a variety of biologically motivated sequence design challenges.
  arXiv  Detail & Related papers  (2020-10-05T16:40:38Z)
• ACSS-q: Algorithmic complexity for short strings via quantum accelerated approach [1.4873907857806357]
  We present a quantum circuit for estimating algorithmic complexity using the coding theorem method. As a use-case, an application framework for protein-protein interaction based on algorithmic complexity is proposed.
  arXiv  Detail & Related papers  (2020-09-18T14:41:41Z)
• Topological Quantum Compiling with Reinforcement Learning [7.741584909637626]
  We introduce an efficient algorithm that compiles an arbitrary single-qubit gate into a sequence of elementary gates from a finite universal set. Our algorithm may carry over to other challenging quantum discrete problems, thus opening up a new avenue for intriguing applications of deep learning in quantum physics.
  arXiv  Detail & Related papers  (2020-04-09T18:00:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.