Google Introduces AI Tool for Highly Accurate Molecular Structure Prediction

Alphabet, the parent company behind the market-leading search engine, Google, is well known for its products such as Search, Gmail, and YouTube. An often overlooked element of Alphabet’s business model is its work in other fields – such as artificial intelligence (AI) and self-driving cars.

Recently, researchers from Alphabet subsidiary, DeepMind, well known for its innovations in beating complex games with computer servers, announced the release of a highly accurate, AI-powered tool. AlphaFold 3 is a new tool that could supercharge medical research, through AI-assisted simulations.

Many new nursing graduates, such as those who have completed family nurse practitioner programs online, may be among the first to witness their impacts at a clinical level. Could AI models be an answer for some of the more complicated medical challenges of our time?

How is AI Shaping Science?

Science has changed a lot from the days of Ancient Egypt. The invention of the computer in the 20th century – from Turing’s groundbreaking work in theoretical computer science to the unveiling of ENIAC, computers have supercharged research in a world where data was previously stored in paperbacks and filing cabinets.

Why, you might ask. Simply put, massive advancements in science are largely due to the amazing ability of machines to handle increasingly large volumes of data. Take, for example, a professor from Oxford University visiting colleagues at MIT. While a short trip over the Atlantic with a full laptop of research may only take a half day these days, a century ago, this was not the case.

Computers, when designed well, have an incredible ability to process substantial volumes of data. Whether that be through the storage of information, to the summarization and collation of that data through platforms, computers have been remarkably useful in scientific fields, especially when collating, sharing, and contributing towards research.

In recent years, a great deal of research has gone into using computers to support and enhance human tasks. Take, for example, games such as chess – could a model be developed that could match, or surpass even the greatest of chess masters? 

What if that concept could be applied to modern scientific theory? Scientists in fields such as biology and manufacturing often spend a great deal of time experimenting with different compositions and compounds to determine an optimal solution. Could AI be used to streamline and simplify the process of testing tens, or even hundreds of thousands of unique molecular compositions? Identifying even a few candidate molecules that are unlikely to succeed could help to rapidly accelerate research in some medical fields.

DeepMind’s New Model: AlphaFold 3

Coming fresh off their latest board game demolition derby, researchers at Google DeepMind have released a brand new AI model, known as AlphaFold 3, that looks to predict the structure and interactions that occur when creating small molecules.

AlphaFold was first released in 2018 – as an attempt to predict protein structures. At the time, it was an internationally-leading innovation. It allowed researchers to spend less time working on specialized computer simulations, instead using the data provided by AlphaFold to support the identification of potential candidates for testing. In research conducted on AlphaFold 2 in 2021, it was found to be highly accurate in determining molecular structures, even for molecules that had a large number of components.

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The Importance of Empirical Evidence

It’s critical to recognize that while models such as AlphaFold can help to streamline the number of potential candidates that may be likely to succeed when creating new medications, it’s important to recognize that proper testing and research need to be done before any new drug hits the market.

It’s well documented that clinical research can take years – from research to licensing, it can sometimes take a decade or more for a well-researched and safe product to hit pharmacy shelves. The innovations that AlphaFold brings to drug-making research look set to reduce the amount of time it takes for new products to enter the trial phase – but they will not be a total replacement for the complex protocols of clinical research.

Medical researchers have learned a lot since the thalidomide scandal that swept the globe during the 1950s and 1960s – and these safety protocols exist to make medicines safe for as many people as possible. While models like AlphaMind have incredible potential, it’s unlikely that they will completely upend the medical research industry – there is a need for safety, over simulation.

A Hopeful Future for Medical Research

Google’s new AlphaFold model has incredible potential for an industry that spends a long time trying to understand the interactions between particles. It’s entirely possible that we could see a new generation of medications, supported through the use of AI in a way that helps cut down the time spent on inefficient or inadequate solutions in drug design.

In the last century, the advent of computers has provided an incredible opportunity for research and innovation to take giant leaps forward. With new AI models leading the charge, the years ahead look to be an exciting time for medical innovation.