Machine learning is a section of artificial intelligence, which is defined as the "capability of a machine to imitate human behavior." Machine learning in drug discovery is becoming increasingly popular, with many researchers and pharmaceutical companies using this technology to improve the drug development process. Machine learning algorithms are able to analyze large datasets of chemical compounds, identify patterns and trends, and make predictions about the potential efficacy and safety of new drug candidates. This has the potential to significantly speed up the drug discovery process, while also reducing costs and increasing the likelihood of success.
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One key review done on machine learning discusses recent publications about its vast ability to find cell patterns in RNA sequencing data, identify therapeutic treatment through novel targets, predict metabolic sites, and so much more.
Machine learning algorithms can analyze large databases of existing drugs and chemical compounds, identifying patterns and relationships that can help researchers identify potential new drug targets. This can lead to faster and more efficient drug discovery, as researchers can identify promising candidates for further study more quickly and accurately.
Another benefit of machine learning in drug discovery is its ability to predict the potential efficacy and safety of new drug candidates. Cell line engineers, crucial contributors to modern biotechnology, play a pivotal role in enhancing drug discovery processes. They can predict “ADME” which is well-known in the pharma/healthcare sector as absorption, distribution, metabolism, and excretion, which are all metrics to consider in drug development. Machine learning algorithms can also analyze large datasets of chemical and biological data to predict the likelihood of a particular compound being effective in treating a specific disease. This can save significant time and money in the drug development process, as researchers can focus their efforts on the most promising candidates.
Machine learning can also help improve the efficiency of clinical trials. By predicting the potential efficacy and safety of new drug candidates, machine learning algorithms can help researchers design more efficient and effective clinical trials. This can reduce the number of patients required for clinical trials, as well as the time and resources required to complete the trials.
In conclusion, the use of machine learning in drug discovery has the potential to significantly improve the efficiency and effectiveness of the drug development process. It will be exciting to see all the various discoveries we can make with this technology in our hands.
References
https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained
Dara S, Dhamercherla S, Jadav SS, Babu CM, Ahsan MJ. Machine Learning in Drug Discovery: A Review. Artif Intell Rev. 2022;55(3):1947-1999. doi:10.1007/s10462-021-10058-4
