“For some problems, supercomputers aren’t so great.” — IBM
Drug design and discovery are complex, time-consuming processes – think years and more often decades. They begin by identifying a biological target, protein, or receptor that is thought to be involved in a particular disease. Small molecules that have the potential to interact with the target are then identified and evaluated for safety and efficacy before being optimized to improve their efficacy and reduce their side effects before they can ultimately be tested in preclinical and clinical trials.
In today’s world, drug design often relies on computational techniques to help simulate the behavior of a molecule and its interaction with a target. While physicists and chemists routinely use computers to simulate the behavior of atoms and molecules, such simulations require enormous amounts of computing power because the interactions between 3 or more particles quickly become extremely complex. This complexity is compounded by the fact that electrons obey the laws of quantum mechanics, which allow for strange phenomena such as superposition, i.e. the ability of a quantum system to exist in several states at the same time. A single quantum particle, such as an electron, can surprisingly exist in multiple places at the same time.
Quantum systems can also happen entangledwhich means that the state of one gear in the system is state-dependent All others, although separated by large distances, exhibit quantum fluctuations due to the Heisenberg Uncertainty Principle, which make it difficult to predict the behavior of a quantum system even when its initial state is known.
Thus, the states of quantum systems cannot be described by a single set of classical variables such as position and momentum. Instead, they must be described by a wave function that contains information about all possible states of the system. Classical computers, which follow the laws of Newtonian physics, thus perform abysmally when confronted with the simulation of quantum phenomena.
Pharmaceutical molecules typically contain 50 to 80 atoms, while the proteins with which drugs interact tend to contain thousands.
Modern supercomputers may have impressive computing power, but they lack the sophistication needed to identify complex patterns in large data sets. This is especially true when investigating proteins and their possible folding structures – a question of great importance to biology and medicine since the shape of a protein determines its function. Figuring out how a single chain of 100 amino acids folds is no easy task because it has trillions of potential configurations and requires more than the brute force typically used to simulate protein folding, since no computer has the working memory needed to handle trillions of possible combinations. individual folds.
This is where quantum computers come in. Quantum computers are fundamentally different beasts, able to perform complex calculations exponentially faster and with only a fraction of the total energy required by classical computers, and could therefore greatly speed up the process of drug discovery and development.
However, quantum computers go beyond just being able to speed up the speed at which classical computers perform calculations, and also have the potential to open up entirely new avenues of research in the field in which they are deployed. For example, in the case of our running drug development example, they could also be used to simulate the behavior of enzymes that are key in drug metabolism, helping to identify new drugs that metabolize faster or slower than existing ones to help reduce side effects. -acts and improves efficiency. They could also be used to optimize the structure of drug candidates by simulating the behavior of different conformations of the molecule, thereby helping to identify new drugs that are more stable and have better pharmacokinetic properties than existing ones.
Quantum algorithms are revolutionizing how complex problems and puzzles like protein folding can be solved. They create multidimensional spaces to find patterns that link data points together. In this case of protein folding, these patterns could tell us the minimum energy configuration of the molecule, which also happens to be the solution to the problem.
Classical computers just can’t match – they just can’t do what quantum hardware paired with advanced algorithms can do. As quantum hardware scales and quantum algorithms advance, they could tackle protein folding problems and many others too complex for any supercomputer.
An example was drug design and discovery one application that quantum computers have the potential to revolutionize, but it is certainly not the only one. They can help us solve many other problems as well, including one of the most pressing problems of our time: climate change.
Currently, the impact of global warming is predicted using computer models that simulate the Earth’s climate. These models take into account factors such as greenhouse gas emissions, changes in land use, and the behavior of the oceans and atmosphere, and use complex mathematical equations to simulate the interactions between these factors and provide projections of future climate conditions. [1][2][3]and
Quantum computers have the potential not only to speed up the creation of climate simulations, but also to make them more accurate and efficient, because as we have already stated, coupled with the right algorithms, quantum computers can process large amounts of data faster and better. more precisely than conventional computers.
For example, they can be used to simulate the behavior of Earth’s oceans. Ocean circulation plays a vital role in regulating Earth’s climate, and quantum computers can simulate the behavior of ocean currents to predict how currents, sea level, ocean temperature and acidity will change in the future.
Quantum computers can also be used to simulate the behavior of Earth’s atmosphere by analyzing large amounts of data from weather stations and satellites to accurately predict future changes in temperature and precipitation patterns. Finally – for the purposes of this article only – quantum computers can also be used to optimize the parameters of existing climate models to disabuse us of any delusions we might have about climate change and its long-term effects.
These are just two of several applications of quantum computers that show that quantum computing definitely has a place, which brings us to our next question: how quantum computers actually work and what makes them different from the classical devices we’re all used to. Subscribe and stay tuned as I dive deeper into topics related to quantum computing. I write almost every day mostly about topics related to business, finance, economics and topics in science that affect us. Do you have any thoughts about what you just read? I’d love to hear them in the comments.
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