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Quantum Computing and Drug Development: An Overview of Target Studies

The drug development process is highly cumbersome and entails a great deal of processes related to design synthesis, efficacy studies and manufacturing. The study of drug interactions and their efficacy across the entire domain of compounds is in itself a challenging procedure which has seen a series of advancements from mathematical optimization methods to computerized algorithms.

Looking At The Present & Past

The time period for drug development, all the way from discovery to launch, is approximately estimated at more than 2 billion dollars for pharmaceutical companies. Most drugs undergo a wide array of selection and filtering procedures to match the required regulatory and industry standards. Despite the immense resources and financial backing, the success rates of most drugs tend to be less than 10% between entry into clinical development and launch of the new drug.

This creates an impetus for pharmaceutical companies to constantly reinvent and invest research on new drugs. Quantum computing arrives into this mix as a potential solution to speed up the process for determining compound properties, testing target identifications across sample populations and more importantly, modeling drug-target interactions.

The Technicalities Of The Drug Design Process

The use of computing base methods have already been explored in great detail through platforms for bioinformatics, which emphasize research on biological interactions, molecular level data and biochemical pathways. Machine learning has been touted as a great addition to fasten the development of algorithms to understand how drug synthesis can be made better. In the same line of research, quantum computing can provide the necessary edge for accurate modeling of drug-target interactions and more efficient screening of very large virtual libraries. All this leads to reduced expenses related to in vitro testing.

Quantum computing can also be seen as a solution to identify new use cases for pre-approved drugs. Quantum computing for pharmaceuticals makes it possible to perform hundreds of millions of computations for biochemical pathways at the same time. This can also be extended to study the impacts of new therapeutic approaches to a general population of people.

Present Players and Potential Solutions

Some of the larger names that are creating their versions of platforms for drug design empowered by quantum computing, include Rahko, ProteinQure, GTN Ltd, Menten AI and so on. The industry is also seeing pledged support from long-established players such as Google, IBM and Honeywell. In the hardware space, there is also work being conducted by larger research centers from D-Wave, Rigetti and Xanadu Quantum Technologies.,

Quantum Computing For Target Identification and Validation

Quantum computing is compatible for target identification which is crucial in several aspects of the drug development process. Computations can be used to predict the 3-D structures of proteins reliably during target identification. Obtaining such high-quality structural data is a time-consuming procedure that often results in poor or futile results that require revamped algorithms. One such success story that countered this trend is AlphaFold, created by Google's DeepMind, which achieved proper predictions for AI-driven protein folding.

Quantum computing still has a long way to go to address some of the other problems associated with traditional computing-based simulations such as protein complex creation, protein-protein interactions, and protein-ligand interactions. Since quantum computing can actually treat electrons as individual entities with acute interest, it takes the interaction modeling sphere to new heights.


As new explorations and algorithms take center stage for more complex drugs and interactions, quantum chemistry will find more relevance as a means of developing solutions to pressing issues in the research industry. The larger the pathways that the drug discovery research process gets, the more complex the data that can be processed. Quantum computing can even fill data for missing points throughout the study process, generating fake data using machine learning algorithms.

Tune in next time as we take a deeper dive into a more specific use case related to quantum computing in drug design for novo synthesis.

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