
via HackerEarth Blog
Drug discovery could be significantly accelerated thanks to a new high precision machine-learning model, developed by an international collaboration of researchers, including the University of Warwick.
The algorithm – partly devised by Dr James Kermode from Warwick’s School of Engineering – can accurately predict the interactions between a protein and a drug molecule based on a handful of reference experiments or simulations.
Using just a few training references, it can predict whether or not a candidate drug molecule will bind to a target protein with 99% accuracy.
This is equivalent to predicting with near-certainty the activity of hundreds of compounds after actually testing them – by running only a couple dozen tests. The new method could accelerate the screening of candidate molecules thousands of times over.
The algorithm can also tackle materials-science problems such as modelling the subtle properties of silicon surfaces, and promises to revolutionise materials and chemical modelling – giving insight into the nature of intermolecular forces.
The approach, developed by scientists at the University of Warwick, the École polytechnique fédérale de Lausanne’s Laboratory of Computational Science and Modelling, the University of Cambridge, the UK Science and Technology Facilities Council and the U.S. Naval Research Laboratory, can also identify which parts of the molecules are crucial for the interaction.
Dr James Kermode, from the University of Warwick’s Warwick Centre for Predictive Modelling and the School of Engineering, commented on the research:
“This work is exciting because it provides a general-purpose machine learning approach that is applicable both to materials and molecules.
“The research is expected to lead to a significant increase in the accuracy and transferability of models used for drug design and to describe the mechanical properties of materials.”
The design of this algorithm, which combines local information from the neighbourhood of each atom in a structure, makes it applicable across many different classes of chemical, materials science, and biochemical problems.
The approach is remarkably successful in predicting the stability of organic molecules, as well as the subtle energy balance governing the silicon structures crucial for microelectronic applications, and does so at a tiny fraction of the computational effort involved in a quantum mechanical calculation.
The research illustrates how chemical and materials discovery is now benefitting from the Machine Learning and Artificial Intelligence approaches that already underlie technologies from self-driving cars to go-playing bots and automated medical diagnostics.
New algorithms allow us to predict the behaviour of new materials and molecules with great accuracy and little computational effort, saving time and money in the process.
Learn more: Drug discovery could accelerate hugely with Machine Learning
The Latest on: Drug discovery with machine learning
[google_news title=”” keyword=”drug discovery with machine learning” num_posts=”10″ blurb_length=”0″ show_thumb=”left”]
- Global Artificial Intelligence (AI) In Drug Discovery Market Competitive Landscape And Trends, By The Business Research Companyon February 9, 2023 at 3:30 am
According to The Business Research Company's AI In Drug Discovery Global Market Report 2023, the global artificial intelligence (AI) in ...
- How pharma can incorporate AI into researchon February 8, 2023 at 2:05 am
One way that biopharma companies are incorporating AI into their research is through the use of federated learning. Federated learning allows multiple organisations to work together on a machine ...
- AlphaFold works with other AI tools to go from target to hit molecule in 30 dayson February 7, 2023 at 12:40 am
Aspuru-Guzik explains that self-running labs could combine AI-led drug discovery with automated reactions and drug formulations 2 suggested by machine learning for an accelerated process. ‘I think the ...
- ARTIFICIAL INTELLIGENCE FOR NEW DRUG DISCOVERYon February 6, 2023 at 4:00 pm
The world is making rapid progress in the areas of Big Data, Artificial Intelligence and Machine Learning ... make pharmaceutical research and new drug discovery less expensive and definitely ...
- Encoding creativity in drug discoveryon February 6, 2023 at 2:01 am
Artificial intelligence (AI) has been increasingly employed to support drug discovery over the last decade. Pioneering start-ups have worked to convince traditional firms that AI and machine learning ...
- AI could speed up discovery of new medicineson February 3, 2023 at 7:53 am
Artificial intelligence that could reduce the cost and speed-up the discovery of new medicines has been developed as part of a collaboration between researchers at the University of Sheffield and ...
- DrugBAN AI could cut costs and accelerate drug discoveryon February 2, 2023 at 3:00 pm
New medicines could be delivered more quickly and at less cost with DrugBAN, an AI technology developed as part of a collaboration between Sheffield University and AstraZeneca.
- Existing Drugs That May Help People Quit Smoking Identified by Machine Learningon February 1, 2023 at 2:11 am
Researchers have used machine learning to identify genes related to smoking behaviors and find existing medications that may be able to be repurposed to help people quit smoking.
- The Intelligent Drug Discovery Industry: Report On The Current Situation And Prospects Analysis, Prediction For 2027on January 30, 2023 at 1:39 am
Emergen Research Logo Intelligent Drug Discovery Market Size – USD 258.9 Million in 2019, Market Growth - CAGR of 39.4%, Market trends –A ...
- VYNT: Predicting Drug Discoveryon January 30, 2023 at 1:20 am
VYNT READ THE FULL VYNT RESEARCH REPORT We are initiating coverage of Vyant Bio, Inc. (NASDAQ:VYNT) assigning a valuation of $2.00 per share. Vyant is a drug discovery and development technology ...
via Google News and Bing News