Disruptive power of quantum

Quantum computing to accelerate pharma research

The disruptive powers of quantum computing will drive and reshape the life sciences industry, especially in the areas of drug discovery and development. The laws of quantum mechanics have enormous potential in terms of speed and in solving complex problems that were unsolvable till now. To that end, the life sciences industry is focusing on identifying appropriate use cases for quantum computing.

This paper discusses some of the key life sciences applications that quantum computing can revolutionize and discusses the way forward. 

Applying quantum computing in life sciences

In traditional computing, bits denote 0 or 1, whereas quantum bits or qubits in quantum computing can denote both 0 and 1 at the same time. While bits function independently, qubits interact with one another leading to exponential growth of states. That essentially means that quantum computers are not restricted to perform stepwise calculations but can compute a vast number of calculations simultaneously at a speed unimaginable with classic supercomputers. 

Given the incredible potential for innovation, quantum computing is expected to significantly disrupt the pharma and life sciences value chain (see Figure 1). Conventionally, research on small and large molecule properties and their interactions form the basis of rational drug discovery. Quantum-inspired algorithms are expected to predict biochemical reactions better, as atomic and sub-atomic particles follow quantum principles.

Most compute-intense problems fall into three major categories – artificial intelligence (AI) and machine learning (ML), simulation, and optimization – where applying quantum computing returns immense value.

Apart from this, there is research focus on quantum technologies in imaging and cryptographic keys. Here we explore the potential use cases of quantum computing in these five areas of the life sciences industry.

Simulating drug interactions 

Simulating chemical reactions and interactions at molecular and sub-molecular levels is currently based on several approximations and assumptions due to compute infrastructure limitations. Industry segments like chemicals, materials, and pharmaceuticals, expect quantum developments to enable better predictive models of chemical or molecular synthesis and reactions based on simulations at molecular, atomic, and sub-atomic levels. Quantum computers can handle problems in estimating reaction rates and mechanisms better than classical computers.

Humans have more than 20,000 genes, which are responsible for coding nearly 20,000 proteins. Further, each protein has nearly 100-200 variants. The Protein Data Bank currently has up to 150,000 protein structures, of which around 46,000 are human proteins. Any chemical used as a drug can potentially bind to one or more of these 20,000 proteins, causing either beneficial or adverse reactions.

The multiple molecular interactions between protein-protein, enzyme-substrate, protein-nucleic acid, drug-protein, and drug-nucleic acid play important roles in biological processes like signal transduction, transport, cell regulation, gene expression control, enzyme inhibition, and antibody-antigen recognition. Besides human proteins, there are proteins on pathogens and commensal microbes with which drugs can interact in human bodies. Quantum computers of the future are expected to accurately simulate these interactions. 

While today’s quantum computers can simulate small molecules like hydrogen, lithium hydride, and beryllium hydride, this is a fairly smaller achievement in terms of molecule size and structural complexity. However, considering today’s low-scale quantum computers, this is quite an accomplishment. 

Addressing optimization problems

Quantum computing is expected to solve the classic traveling salesman problem (TSP) faster and with higher accuracy. Although the salesman would not have to visit thousands of cities, this problem is relevant for solving several optimization issues across travel and transportation, supply chain, network infrastructure, air traffic control, work scheduling, financial services, and protein folding. Today’s computers find it difficult to understand how real proteins fold into the shapes that help give them their function. 

In 2012, a Harvard research team reported the usage of quantum annealing to solve protein folding problems. Quantum computing can address other optimization problems like molecular recognition, protein design, and sequence alignment. Researchers at the University of Southern California recently demonstrated the use of quantum processors to predict the binding of gene regulatory proteins to DNA. 

The healthcare industry also faces operational-level optimization challenges like scheduling healthcare providers and deciding therapy regimens for cancer and other diseases. Minimizing damages to surrounding healthy tissues and organs during radiotherapy is a highly complex optimization problem with thousands of variables. Quantum computers have the potential to identify the most optimized and precise radiation and therapy plans after comparing all possible approaches.