A Brief Review on Quantum Drug Design

Appropriate drug design is essential for the survival of human civilization. The predominance of dreadful viruses and bacteria has prompted biochemists to find new molecular structures for drugs. However, the design of drugs is not a very simple process. A drug has to satisfy the needs of its synthesis as well, and it must not be toxic to the target organ of the individuals for better efficacy. The cost and social acceptability are also important factors of drug design for the commercialization of a particular drug. Since its onset in the twentieth century, the drug design process has involved a series of complex chemical calculations. Hence, computer-aided drug design (CADD) has gained importance in solving these complex chemical equations. However, due to the complexity of the equations, many chemical equations related to drug design could not be solved by classical computational techniques. In this context, there was a need for the incorporation of new and efficient computational methodologies for the design and development of drugs. Of late, the quantum computing paradigm has rapidly evolved as an efficient and robust alternative to the classical computing paradigm. Quantum computing methodologies have thus gained popularity in computer-aided drug design since 2018. Quantum computing methodologies help in the reduction of quantum error and the determination of the appropriate molecular structures. Quantum computing-based machine learning methods have also gained popularity in drug design. The quantum-based swarm intelligence algorithms have become popular in solving multi-criteria optimization problems related to drug design. Among other algorithms, FTQC and NIQC algorithms also gained popularity in terms of efficient drug design.