How Machine Learning is Transforming Life Science

One of the biggest and most disruptive innovations in recent times is that of Artificial Intelligence (AI). In every sphere of modern life AI is having an impact. While some uses of AI, such as automating or improving efficiency in labour-intensive tasks have been welcomed, others have been met with less enthusiasm due to privacy concerns and the fear of job losses.

A subset of AI, known as Machine learning (ML), is predicted to revolutionise the life science industry. Machine learning is focused on building algorithms and models that enable computers to learn from data and make accurate predictions or decisions without being explicitly programmed for each task. Over time, machine learning systems improve their performance as they are exposed to more data, requiring minimal human intervention.

Discovering new antibiotics

Machine learning is a powerful tool that can be harnessed to analyse complex biological data, predict medical outcomes and accelerate scientific discoveries. Researchers at the Massachusetts Institute of Technology (MIT) have developed a machine learning algorithm capable of screening millions of drug compounds in a matter of days. The model is designed to select drug candidates with bactericidal activity against E. coli. They trained their model on around 2,500 molecules, then used the trained model to screen a library of 6,000 compounds. A promising new candidate from that screen, named halicin, has been shown to kill antibiotic resistant strains of bacteria including Clostridium difficile, Acinetobacter baumannii, and Mycobacterium tuberculosis in laboratory tests. Following this, a drug named abaucin was identified which selectively targets Acinetobacter baumanni, a species of bacteria often found in hospitals, which causes serious illness and displays considerable drug resistance. These compounds have different chemical structures to any existing antibiotics and novel mechanisms of action, in the hope of avoiding antimicrobial resistance. Both antibiotics are at the pre-clinical testing stage.

In addition to drug screening which could lead to the development of life saving medicines. Machine learning can be used to assist in clinical settings where medical imaging is employed to detect abnormalities and diagnose conditions. Both radiology and pathology departments could benefit from faster and more accurate screening of x-rays and scans. ML is currently being used as a support tool in some settings and has not replaced standard methodologies. However, it’s application in research for the early detection of some cancers and Alzheimer’s disease has shown promising results.

Predicting protein structures

In biological research, Google DeepMind has developed an AI system called AlphaFold which can predict the 3D structure of a protein from its amino acid sequence. Determining the 3D structure of a protein in a laboratory can take from months to years, AlphaFold takes minutes and has been found to be highly accurate. In partnership with EMBL’s European Bioinformatics Institute (EMBL-EBI), over 200 million predicted protein structures have been placed in a free to access database. Non-commercial researchers can also sign up to the AlphaFold Server and get access to modelled predictions of a protein’s molecular interactions throughout cells.

On a global scale, ML can impact public health by helping to model disease spread in pandemics such as the COVID-19 outbreak. Future outbreaks can be forecast and interventions planned to reduce their severity. Analysis of patterns or trends in large-scale health datasets could improve population health strategies by predicting at-risk populations, allowing targeted interventions. On an individual basis, machine learning makes personalised medicine possible, allowing tailored treatments based on a patient’s genetics, medical history, and lifestyle. Wearables and real-time health monitoring tools are now widespread. Powered by AI, these health apps and devices that track heart rate, glucose levels, or sleep patterns are providing valuable data to those who wish to monitor their health and wellbeing. In the case of glucose monitoring for diabetes patients, these tools can be a lifesaver.

Conclusion

Machine learning’s ability to analyse vast and complex datasets, detect patterns and make data-driven predictions far exceeds what could have previously been achieved. It’s impact in areas such as drug discovery, genomics, personalised medicine and disease prediction will be vast. However, it is not without downsides; high-quality datasets can often be required which are difficult to obtain, and there are ethical and privacy challenges to be overcome. Nevertheless, if machine learning can be applied responsibly, it could lead to groundbreaking advancements in life science.

References used:
https://news.mit.edu/2020/artificial-intelligence-identifies-new-antibiotic-0220
https://news.mit.edu/2023/using-ai-scientists-combat-drug-resistant-infections-0525
https://deepmind.google/technologies/alphafold/
https://alphafold.ebi.ac.uk/?utm_source=deepmind.google&utm_medium=referral&utm_campaign=gdm&utm_content=
https://www.ebi.ac.uk

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