Quantum assisted Genetic Algorithm for Sequencing Compatible Amino Acids in Drug Design MINI PROJECT REPORT By GOGULRAM (RA2111031010020) KAVIN D (RA2111031010027) ANTO MICHEAL INFANT S (RA2111029010038) L GIRIDHARAN (RA2111029010035) Under the guidance of DR.R.THILAGAVATHY (Assistant Professor,COMPUTING TECHNOLOGIES Dept) In partial fulfilment for the Course of 18CSE310J –QUANTUM COMPUTATION BACHELOR OF TECHNOLOGY in COMPUTER SCIENCE & ENGINEERING FACULTY OF ENGINEERING AND TECHNOLOGY SCHOOL OF COMPUTING SRM INSTITUTE OF SCIENCE AND TECHNOLOGY KATTANKULATHUR NOVEMBER 2023 SRM INSTITUTE OF SCIENCE ANDTECHNOLOGY (Under Section 3 of UGC Act, 1956) BONAFIDE CERTIFICATE Certified that this minorproject report for the course 18CSE310JQUANTUM COMPUTATION entitled in "Quantum assisted Genetic Algorithm for Sequencing Compatible Amino Acids in Drug Design" is the bonafide work of GOGULRAM(RA2111031010020), KAVIN(RA2111031010027), ANTO MICHEAL INFANT S(RA2111029010038)and L GIRIDHARAN (RA2111029010035)who carried out the work under my supervision. SIGNATURE DR.R.THILAGAVATHY Assistant Professor Computing Technologies Department SRM Institute of Science and Technology Kattankulathur SIGNATURE Dr. Annapurani Panaiyappan k Professor and Head NWC Department SRM Institute of Science and Technology Kattankulathur ABSTRACT Using quantum computing for drug design is among the most promising applications in quantum technologies. Genetic algorithms with their evolutionary iterations make it a propitious approach in various tasks, including drug discovery, gene prediction, docking of ligands to receptors, and the design to combinatorial libraries. As its computational power and methods limits classical computing, quantum computing intends to break these limits with exponential computing capabilities. This application complements both quantum theory and genetic programming as we use true randomness with mutation and fitness function based on information encoded onto qubits storing quantum data. In this article, we present the results of encoding quantum data to our quantum genetic algorithm, which predicts the best possible drug structure to bind onto the target protein. qubits hold the genome structure to perform bit string mutation over quantum gates. These results are later than compared to classical computing with various approaches in the evolutionary algorithm's parameters. TABLE OF CONTENTS CHAPTER NO CONTENTS PAGE NO 1 INTRODUCTION 5 2 LITERATURE SURVEY 6 3 REQUIREMENT 9 ANALYSIS 4 ARCHITECTURE & 11 DESIGN 5 IMPLEMENTATION 12 6 EXPERIMENT RESULTS 13 & ANALYSIS 7 CONCLUSION 15 8 REFERENCES 16 1. INTRODUCTION Meta-heuristic algorithms have gained recent popularity in their ability to help solve a wide range of real-world problems rising from various fields such as engineering, medicine, economics and politics. We base many meta-heuristic algorithms on biological evolutionary processes, population behavior, and physics laws. These Meta-heuristic algorithms are then classified primarily into two categories, population and single-solution based. Single-solution meta- heuristics take a single candidate solution and update it using local search to improve the result. The solution could become stuck in local optima. Simulated annealing, tabu search (TS), guided local search (GLS), and micro canonical annealing are a few single-solution based meta-heuristics (MA). Multiple candidates are used in the search process by population-based This method maintains diversity in thegenerated population and avoids the off springs that get stuck in the local optima space. Few of the well-known meta- heuristics algorithms are genetic algorithm (GA), ant colony optimization (ACO), emperor penguin optimizer (EPO), and seagull optimization (SOA). 2. LITERATURE SURVEY Genetic algorithm (GA) is a meta-heuristic algorithm inspired by evolution. It uses the Darwinian principle of survival of the fittest. The fundamental ingredients are chromosome production, fitness function, and biological operators. GA was proposed by J. H Holland in 1992. He introduced a new element, inversion, to use binary Boolean logic to compute. When implementing the chromosomes into a binary string format, each string has two alleles 0 and 1. These points are considered as solution space, where the genetic operators replace the population iteratively. The fitness function assigns a value for all the generated child chromosomes in the population. The biological operators are then activated, firstly selection, then mutation and finally crossover. In the first segment of the process, selection, the chromosomes from the population are selected based on our specified fitness function for processing. The crossover operator chooses a random point, and it changes the composition between population chromosomes to create off-springs. In the following step, mutation, some of these chromosomes are inverted randomly based on probability, and fitness is reduced. These are the elements of a typical GA. QGA is encoded with quantum data and true randomness as our input. The algorithm is implemented on a target protein to fabricate a drug sequence that can bind the amino acid group onto the predicted binding sites iterated over quantum mutations. Molecular simulation in a CADD process Drug Discovery A three-step process is followed in the development of a new drug. A target, much like a receptor or an enzyme, must first be sampled with a disease state. The target sequence must then be fully characterized, and an amino acid compound must then bind to it. The molecules in our body have a chemical affinity that can either be positive or negative. All protein structures are composed of an amino acid group which acts as the building blocks of the protein sequence. When targeting an enzyme, we must consider its key residues. These are chemical sites where another molecule can easily be bound, which is our target. One might think that because proteins comprise amino acids, they all have binding sites, but this isn't the case. 3. REQUIREMENTS Qubits and Gates Qubit or a quantum bit is the quantum variant to a classical bit. The difference is that while the classical bit can hold two values either 0 or 1, a qubit can exist in infinitely different values. Some of the most commad commad-gates gates used in quantum computing are H gate, Phase shifter gates, controlled gates, and gates. Qubits exist in a Hilbert vector space with a basis of two elements that we denote as 10 and 1>. Qubits stay in |psi>=a0>+b/1> psi>=a0>+b/1> where, a and b are complex numbers such that a^2 + b^2 = 1. The only way to kknow now the value of a qubit is to perform a measurement. However, the result of the measurement is completely true random and while wemeasure, we get only one classical bit of information. As the measurement takes place, this creates an interaction on the qubits its which cause all the quantum properties to be lost. After measurement, we get one classical bit of information. Amino Acid group table QUANTUM GENETIC ALGORITHM A Quantum genetic algorithm (QGA) is proposed with the principle that physical data is stored in a double quanta system called a qubit. The data encoding used in this paper is binary code in qubits. The data stored in qubits is defined within the qubits as probability amplitudes. Chromosomes in the genetic algorithm are operated quantum rotating rota gates which exist in a state of superposition. Quantum architectures perform in a certain way other than its classical counterpart. QGA is self self-adaptive, self-organizing, organizing, and self-learning. self The limitation of genetic algorithms is the premature converg convergence ence due to low diversity in the population generated. We use the quantum mutation operation and quantum disaster operation to our QGA to improve the performance to accurately reach the optimum solution while having a high convergence to the point of the Q Quantum- inspired population algorithm. 4. ARCHITECTURE AND DESIGN Flowchart of QGA Process Architecture Diagram of QGA 5. IMPLEMENTATION To create an optimum binding drug to a target protein, we have to on the data for amino acids onto our qubits. We focus on the binary representation of the datir. As all molecules interact with our body, they hold a chemical affinity, positive and negative reactions. Every protein has a shape formed by its amino acid chain. This shape folds the protein and maintains its volatility. To stretch the protein, force is needed. If we change the associated constitutive chemistry of the protein, we alter the shape and conformation. To target an enzyme, key residues are sites where another molecule is bound with little force needed to apply. Every protein has exposed binding sites. Our genetic algorithmemulates survival of the fittest from the natural selection theory. The target protein holds many kinds of amino acids. Every protein has exposed binding sites. Our genetic algorithm emulates survival of the fittest from the natural selection theory. The target protein holds many kinds of amino acids, polar, non-polar, positively charged and negatively charged amino acids. drug needs to follow certain rules to be the optimum drug bound to the targei protein. A basicoverview of the rules is, the ends of the possible drug need to be charged positively and negatively, respectively on the binding sites. The amino acids which will be converted to quantum data and then used in the QGA approach are shown here. 6. RESULTS AND DISCUSSION : The development of novel drugs requires the identification of molecules that can effectively bind to specific target proteins. This intricate process involves tailoring the drug sequence to complement the structure and binding sites of the target protein. Genetic algorithms (GAs) have emerged as powerful tools for optimizing drug sequences, but their performance can be limited by the inherent constraints of classical computing. To overcome these limitations, quantum genetic algorithms (QGAs) have been proposed, harnessing the unique properties of quantum mechanics to enhance drug sequence prediction. 1. Population Creation and Quantum Gene Encoding The QGA begins with the creation of an initial population of random drug sequences. Each sequence is represented as a binary string, where each bit encodes the characteristics of the corresponding amino acid. To incorporate quantum effects, these binary strings are converted into quantum bits (qubits) using quantum gene encoding. Qubits can exist in a superposition of states, allowing them to represent multiple potential solutions simultaneously, a significant advantage over classical bits. 2. Fitness Function and Selection The fitness function plays a crucial role in evaluating the effectiveness of each drug sequence. It assesses the binding affinity of the sequence to the target protein, assigning higher fitness values to sequences with stronger binding interactions. Based on their fitness values, the top-performing sequences are selected for further processing. This selection process ensures that the QGA focuses on optimizing the most promising drug candidates. 3. Quantum Crossover and Mutation Crossover and mutation are essential genetic operators that introduce variations in the population, enabling the exploration of new and potentially better drug sequences. In the QGA, quantum crossover is employed to combine the genetic information of selected sequences. This process involves entangling the qubits representing the parent sequences, allowing for the exchange of genetic material in a controlled manner. Quantum mutation, on the other hand, utilizes quantum gates to introduce controlled mutations to the qubits. This precise and targeted mutation process helps prevent premature convergence and maintain diversity within the population. 4. Iteration and Convergence The QGA iteratively applies the selection, crossover, and mutation operators, generating new generations of drug sequences. Each generation undergoes fitness evaluation, and the process continues until a desired convergence criterion is met. This criterion typically involves achieving a satisfactory level of binding affinity or reaching a predefined number of iterations. The final nal drug sequence with the highest fitness value is considered the optimal solution. 5. Advantages of the QGA Approach The QGA offers several advantages over classical GAs for drug sequence prediction: Enhanced Exploration: Qubits can represent multiple ppotential otential solutions simultaneously, enabling the QGA to explore a broader search space more efficiently. Faster Convergence: Quantum crossover and mutation operators introduce variations more effectively, leading to faster convergence to optimal drug sequen sequences. Improved Optimization: Precise and targeted quantum mutations help prevent premature convergence and maintain diversity, leading to more optimized solutions. Represents fitness convergence between Classical GA and Quantum GA Result generated when the fitness is maxed and a sequence is generated 7. CONCLUSION This paper has presented a novel approach to drug design utilizing quantum computing to enhance the generation of amino acid sequences that can better virtualize drug interactions. Quantum Genetic Algorithm (QGA) has demonstrated the potential of quantum properties in improving the efficiency and effectiveness of drug discovery. Future research directions include addressing the issue of qubit decoherence and developing effective error correction techniques to ensure the accuracy and reliability of quantum computations. Additionally, more sophisticated fitness functions can be developed that can accurately assess the binding affinity and potential efficacy of drug sequences, incorporating additional factors such as drug stability and toxicity. Quantum gate logic involved in the QGA can be simplified to reduce the number of gate operations and improve computational efficiency. Finally, genetic crossover operators can be optimized to achieve a better balance between exploration and exploitation, leading to more efficient convergence to optimal drug sequences. By addressing these challenges and further developing the QGA, we can harness the power of quantum computing to revolutionize drug discovery and development, leading to the identification of more effective and safer drugs for a wide range of diseases. 8.REFERENCE To find such references, consider searching in academic databases, journals, and preprint archives. Some popular platforms include: PubMed: Search for articles related to quantum computing and drug design. PubMed Google Scholar: Use specific keywords like "quantum computing," "genetic algorithm," and "drug design" to find relevant articles. Google Scholar arXiv.org: This preprint archive often contains cutting-edge research articles in quantum computing. arXiv.org ResearchGate: Researchers often share their publications on ResearchGate, and you might find relevant articles there. ResearchGate IEEE Xplore: Search for articles related to quantum computing applications in drug design. IEEE Xplore ScienceDirect: Explore articles related to quantum computing and drug design on ScienceDirect.
