Researchers and scientists are leveraging the opportunities provided by machine learning (ML) to gain significant advances in drug discovery and development processes.
FREMONT, CA: Artificial Intelligence (AI) is providing a technological edge to various industries. Its ability to deal with massive data sets with significant insights is allowing the enterprises to unlock patterns that were hidden until now. Healthcare and pharmaceutical industry are also getting transformed as a result of these technologies. As per the data from Accenture, key health AI applications can account for $150 billion in annual savings in the U.S. healthcare sector savings by 2026. As per the numbers, the healthcare industry will leverage the opportunities provided by machine learning (ML). Several AI companies are getting involved in various activities in the treatment process, which includes diagnosis, therapy, and drug development.
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AI Boosting Drug Delivery
Enhanced computational solutions in drug discovery reduce the cost of drug introduction in the market. As per the Grand View Research and its new report, global drug discovery informatics market size was projected at $713.4 million in 2016 and is expected to progress at a compound annual growth rate (CAGR) of 12.6 percent by 2025. With the increased usage of AI for drug delivery, the market’s value is growing rapidly. Moreover, the technological shift to ML in the pharmaceutical industry allows researchers to employ novel computational algorithms to enhance the process. ML can optimize several stages of the drug discovery process:
• Preliminary but crucial stages including developing a drug’s chemist store
• Probing the effect of a drug in clinical trials as well as basic, preclinical research where massive biomedical data is generated. ML can facilitate the process of finding patterns in those data.
Challenges for ML in Drug Discovery
Drug safety is a key challenge in the drug discovery process. Information about the known behaviors of drugs and ascertaining their side effects is a tedious task. With ML, scientists and researchers are gaining meaningful information from clinical data generated in clinical trials.
Moreover, clinical trials required during drug development are expensive. It is important to use the experience gained during the previous trials in the early stages of drug development. It can be achieved in the following two steps:
• Data from biomedical research experiments can be analyzed and interpreted via ML to understand a drug’s side effects
• ML can be used to analyze data from clinical trials and support the interpretation of biological data.
Integrating Computational Approaches with Biomedical Data
ML can optimize therapy by integrating clinical and biomedical data with computational models. It can be used to design software to test drugs and combinatorial therapies. Several computational approaches and models can be used to develop software to test combinatorial therapies and drugs. While some of the models and approaches are still under development, there are also a few examples of successful data integration in medicine and biology.
Personalized Medicine and Genetic Data Analysis
Genetic data interpretation and personalized medicine influences various startups and pharmaceutical companies. Comprehending the patient’s genetic profile enables to offer appropriate therapy and drugs. Computational approaches to propose novel therapy and to analyze data can be advanced with ML. There are just a few examples that influence current clinical practice as per ML solutions that offer huge potential to drug discovery and personalized medicine. They comprise ML-based computational tools used in clinical practice and discovering novel biomarkers of drug response.
Another possible approach relies on interpreting the genetic code as one-dimensional image and then using a standard ML algorithm. The data can then be scoured for anomalies and patterns.
Gaining Insights from Datasets and Databases
Scientists deploy public repositories of clinical data to deal with big problems in clinics to help doctors in their work. The repositories can also be useful for drug discovery purposes as they can provide clinical information at an early stage of drug development. Further, data mapped with ML can also be easier to integrate with biomedical data.