Quantum Computing: The Future of Accelerated Drug Discovery and Protein Sequencing

Quantum computing (QC) could significantly accelerate drug discovery, a currently complex, time-consuming, and expensive process. The pharmaceutical industry, focusing on molecular formations, is a prime candidate for QC. Currently, the industry uses computer-aided drug discovery (CADD), but classical computers have limitations. QC could improve the scope of biological mechanisms suitable for CADD and reduce the length of the empirical development cycle. Quantum algorithms in drug discovery aim to decrease the cost and time of the research and development process of new drugs. When fully developed, QC could add value to the entire drug value chain.

How Can Quantum Computing Accelerate Drug Discovery?

The Covid-19 pandemic has highlighted the need for accelerated drug discovery. Pharmaceutical companies are increasingly turning to computational approaches to speed up this process, which is typically complex, time-consuming, and resource-intensive. The development of a new drug can take over a decade and cost around a billion dollars. Computational methods are used in the early phases of drug discovery to decipher disease-related biology, prioritize drug targets, and identify and optimize new chemical entities for therapeutic intervention. However, more than 50% of drug discovery candidates fail in early phase clinical trials.

The pharmaceutical industry is a prime candidate for Quantum Computing (QC) due to its focus on molecular formations. Molecules, including potential drugs, are quantum systems, i.e., systems based on quantum physics phenomena. Currently, the industry uses non-QC tools in a methodology known as computer-aided drug discovery (CADD). However, classical computers have limitations, and basic calculations like predicting the behavior of medium-sized drug molecules can be time-consuming. CADD on quantum computers could improve the scope of biological mechanisms suitable for CADD and reduce the length of the empirical development cycle by eliminating some research dead ends.

Quantum algorithms in drug discovery aim to decrease the cost and time of the research and development process of new drugs. Quantum computing is also being explored in other fields such as communication, physics simulations, machine learning, and finance. Some quantum methods have already been developed to represent molecule systems. When fully developed, QC could add value to the entire drug value chain, from discovery through development to registration and post-marketing.

What Role Does Protein Similarity Play in Drug Discovery?

The authors of the study propose a quantum method to generate random sequences based on the occurrence in a protein database and another quantum process to compute a similarity rate between proteins. The goal is to find proteins that are closest to the generated protein and to have an ordering of these proteins. The authors present the construction of a quantum generator of proteins, which defines a protein called the test protein. The aim is to have a randomly defined amino acids sequence according to a proteins database given. The authors then describe two different methods to compute the similarity rate between the test protein and each protein of the database.

The identification and development of small molecules and macromolecules that can help cure diseases is the core business of pharmaceutical companies. The structure of a protein is determined by the sequence of amino acids that make it up and how the protein folds into more complex shapes. The protein sequences present in a living organism are encoded by DNA and more specifically by its nucleotides.

How Can Quantum Computing Generate Protein Sequences?

In the study, two main lines of work are identified: the construction of the structure of a protein by its amino acids sequence and the computation of a similarity rate between proteins. The first idea is to construct an amino acids sequence with a quantum algorithm and more precisely with the measurement of a quantum system. The second idea is to compute the similarity rate between two amino acids sequences. The authors build three quantum algorithms with different methods and compare them afterward.

Amino acids are the components that build proteins and thus provide their structure. The distinction between the different amino acids is made by more or less complex side chain which gives them different physicochemical properties. Amino acids can be linked together by a peptide bond to form chains containing from two to several thousand amino acids. This chain, called a polypeptide chain, is the ordering of the amino acids in the sequence.

What is the Potential Impact of Quantum Computing on the Pharmaceutical Industry?

The impact of quantum computing on the pharmaceutical value chain could be significant. Quantum algorithms in the field of drug discovery aim to decrease the cost and time of the research and development process of new drugs. Quantum computing is being explored in other fields such as communication, physics simulations, machine learning, and finance. Some quantum methods have already been developed to represent molecule systems. Thus, when fully developed, QC could add value to the entire drug value chain, from discovery through development to registration and post-marketing.

The pharmaceutical industry is a prime candidate for Quantum Computing (QC) due to its focus on molecular formations. Molecules, including potential drugs, are quantum systems, i.e., systems based on quantum physics phenomena. Currently, the industry uses non-QC tools in a methodology known as computer-aided drug discovery (CADD). However, classical computers have limitations, and basic calculations like predicting the behavior of medium-sized drug molecules can be time-consuming. CADD on quantum computers could improve the scope of biological mechanisms suitable for CADD and reduce the length of the empirical development cycle by eliminating some research dead ends.

The main interest of quantum algorithms in the field of drug discovery is to decrease the cost and time of the research and development process of new drugs, as it is true in other fields. Quantum computing is being explored in other fields such as communication, physics simulations, machine learning, and finance. Today, some quantum methods have already been developed to represent molecule systems. Thus, when fully developed, QC could add value to the entire drug value chain, from discovery through development to registration and post-marketing.