̽��ֱ�� of Cambridge - Pfizer

Accelerating how new drugs are made with machine learning

sc604 — Mon, 15 Jan 2024 10:05:29 +0000

Predicting how molecules will react is vital for the discovery and manufacture of new pharmaceuticals, but historically this has been a trial-and-error process, and the reactions often fail. To predict how molecules will react, chemists usually simulate electrons and atoms in simplified models, a process that is computationally expensive and often inaccurate.

Now, researchers from the ̽��ֱ�� of Cambridge have developed a data-driven approach, inspired by genomics, where automated experiments are combined with machine learning to understand chemical reactivity, greatly speeding up the process. They’ve called their approach, which was validated on a dataset of more than 39,000 pharmaceutically relevant reactions, the chemical ‘reactome’.

Their results, reported in the journal Nature Chemistry, are the product of a collaboration between Cambridge and Pfizer.

“ ̽��ֱ��reactome could change the way we think about organic chemistry,” said Dr Emma King-Smith from Cambridge’s Cavendish Laboratory, the paper’s first author. “A deeper understanding of the chemistry could enable us to make pharmaceuticals and so many other useful products much faster. But more fundamentally, the understanding we hope to generate will be beneficial to anyone who works with molecules.”

̽��ֱ��reactome approach picks out relevant correlations between reactants, reagents, and performance of the reaction from the data, and points out gaps in the data itself. ̽��ֱ��data is generated from very fast, or high throughput, automated experiments.

“High throughput chemistry has been a game-changer, but we believed there was a way to uncover a deeper understanding of chemical reactions than what can be observed from the initial results of a high throughput experiment,” said King-Smith.

“Our approach uncovers the hidden relationships between reaction components and outcomes,” said Dr Alpha Lee, who led the research. “ ̽��ֱ��dataset we trained the model on is massive – it will help bring the process of chemical discovery from trial-and-error to the age of big data.”

In a related paper, published in Nature Communications, the team developed a machine learning approach that enables chemists to introduce precise transformations to pre-specified regions of a molecule, enabling faster drug design.

̽��ֱ��approach allows chemists to tweak complex molecules – like a last-minute design change – without having to make them from scratch. Making a molecule in the lab is typically a multi-step process, like building a house. If chemists want to vary the core of a molecule, the conventional way is to rebuild the molecule, like knocking the house down and rebuilding from scratch. However, core variations are important to medicine design.

A class of reactions, known as late-stage functionalisation reactions, attempts to directly introduce chemical transformations to the core, avoiding the need to start from scratch. However, it is challenging to make late-stage functionalisation selective and controlled – there are typically many regions of the molecules that can react, and it is difficult to predict the outcome.

“Late-stage functionalisations can yield unpredictable results and current methods of modelling, including our own expert intuition, isn't perfect,” said King-Smith. “A more predictive model would give us the opportunity for better screening.”

̽��ֱ��researchers developed a machine learning model that predicts where a molecule would react, and how the site of reaction vary as a function of different reaction conditions. This enables chemists to find ways to precisely tweak the core of a molecule.

“We trained the model on a large body of spectroscopic data – effectively teaching the model general chemistry – before fine-tuning it to predict these intricate transformations,” said King-Smith. This approach allowed the team to overcome the limitation of low data: there are relatively few late-stage functionalisation reactions reported in the scientific literature. ̽��ֱ��team experimentally validated the model on a diverse set of drug-like molecules and was able to accurately predict the sites of reactivity under different conditions.

“ ̽��ֱ��application of machine learning to chemistry is often throttled by the problem that the amount of data is small compared to the vastness of chemical space,” said Lee. “Our approach – designing models that learn from large datasets that are similar but not the same as the problem we are trying to solve – resolves this fundamental low-data challenge and could unlock advances beyond late-stage functionalisation.”

̽��ֱ��research was supported in part by Pfizer and the Royal Society.

References:
Emma King-Smith et al. ‘Predictive Minisci Late Stage Functionalization with Transfer Learning.’ Nature Communications (2023). DOI: 10.1038/s41467-023-42145-1

Emma King-Smith et al. ‘Probing the Chemical "Reactome" with High Throughput Experimentation Data.’ Nature Chemistry (2023). DOI: 10.1038/s41557-023-01393-w

Researchers have developed a platform that combines automated experiments with AI to predict how chemicals will react with one another, which could accelerate the design process for new drugs.

A deeper understanding of the chemistry could enable us to make pharmaceuticals and so many other useful products much faster.

Emma King-Smith

BlackJack3D via Getty Images

Digital Molecular Structure Concept

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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Machine learning algorithm helps in the search for new drugs

sc604 — Mon, 11 Feb 2019 20:00:00 +0000

̽��ֱ��researchers, led by the ̽��ֱ�� of Cambridge, used their algorithm to identify four new molecules that activate a protein which is thought to be relevant for symptoms of Alzheimer’s disease and schizophrenia. ̽��ֱ��results are reported in the journal PNAS.

A key problem in drug discovery is predicting whether a molecule will activate a particular physiological process. It’s possible to build a statistical model by searching for chemical patterns shared among molecules known to activate that process, but the data to build these models is limited because experiments are costly and it is unclear which chemical patterns are statistically significant.

“Machine learning has made significant progress in areas such as computer vision where data is abundant,” said Dr Alpha Lee from Cambridge’s Cavendish Laboratory, and the study’s lead author. “ ̽��ֱ��next frontier is scientific applications such as drug discovery, where the amount of data is relatively limited but we do have physical insights about the problem, and the question becomes how to marry data with fundamental chemistry and physics.”

̽��ֱ��algorithm developed by Lee and his colleagues, in collaboration with biopharmaceutical company Pfizer, uses mathematics to separate pharmacologically relevant chemical patterns from irrelevant ones.

Importantly, the algorithm looks at both molecules known to be active and molecules known to be inactive and learns to recognise which parts of the molecules are important for drug action and which parts are not. A mathematical principle known as random matrix theory gives predictions about the statistical properties of a random and noisy dataset, which is then compared against the statistics of chemical features of active/inactive molecules to distil which chemical patterns are truly important for binding as opposed to arising simply by chance.

This methodology allows the researchers to fish out important chemical patterns not only from molecules that are active but also from molecules that are inactive – in other words, failed experiments can now be exploited with this technique.

̽��ֱ��researchers built a model starting with 222 active molecules and were able to computationally screen an additional six million molecules. From this, the researchers purchased and screened the 100 most relevant molecules. From these, they identified four new molecules that activate the CHRM1 receptor, a protein that may be relevant for Alzheimer’s disease and schizophrenia.

“ ̽��ֱ��ability to fish out four active molecules from six million is like finding a needle in a haystack,” said Lee. “A head-to-head comparison shows that our algorithm is twice as efficient as the industry standard.”

Making complex organic molecules is a significant challenge in chemistry, and potential drugs abound in the space of yet-unmakeable molecules. ̽��ֱ��Cambridge researchers are currently developing algorithms that predict ways to synthesise complex organic molecules, as well as extending the machine learning methodology to materials discovery.

̽��ֱ��research was supported by the Winton Programme for the Physics of Sustainability.

Reference:
Alpha A. Lee et al. ‘Ligand biological activity predicted by cleaning positive and negative chemical correlations.’ PNAS (2019). DOI: 10.1073/pnas.1810847116

Researchers have designed a machine learning algorithm for drug discovery which has been shown to be twice as efficient as the industry standard, which could accelerate the process of developing new treatments for disease.

̽��ֱ��ability to fish out four active molecules from six million is like finding a needle in a haystack

Alpha Lee

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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