Alan McCarthy, also known as the father of Artificial Intelligence coined the term back in 1955. Since then AI has seen steady growth until recent years. In the past few years, businesses have started incorporating AI more and more in their operations for ease of functioning. We can see it being used in a myriad of business activities such as building personalized customer experiences, cyber-security, predictive service and maintenance, and smart assistants, to name a few. Machine Learning and Deep Learning, which form the subsets of Artificial Intelligence, have also opened several avenues in the research community.
Let’s Focus On Bioinformatics – Connoting our niche focus area of Bioinformatics and its aspect of Clinical Research and Drug Development, let’s look at AI applications that have reshaped the sector.
AI, Computing & COVID 19
Integration of AI models into supercomputers is enabling them to run complex calculations in epidemiology, bioinformatics, and molecular modeling. We have seen IBM leverage this with IBM Summit to enable the experts at Oak Ridge National Laboratory and the University of Tennessee to screen 8,000 compounds to bind the main spike protein of COVID and come up with 77 possible drug compounds which could be experimentally tested. These procedures, if carried out in the traditional ways, could have taken months or even years.
Machine Learning And Bio-sciences
Machine Learning can be leveraged in decrypting large datasets such as DNA sequencing, Protein classification, and Gene expression.
For example, AI ingestion allows easy analysis of voluminous patient data for DNA sequencing, to predict and personalize medicines. Researchers are also extracting protein data with advanced techniques in Natural Language Processing (NLP). Neural network applications such as Bidirectional Long Short-Term Memory (BLSTM) is helping in the study of critical features of protein sequences. Deep learning tools such as ProDec BLSTM, DeepSea, and Basset are proving to be useful.
Data Collation & Analysis For Drug
Data collection is a crucial part of drug development, which can be done with the help of AI tools. Natural Language Processing (NLP) uses machine learning to analyze large data sets and recognize biologically applicable terms. It helps scrutinize the copious amount of data and identify previously missed patterns or relationships concerned with a disease and its drugs. The ability of NLP-mined data can be enhanced by compiling results from multiple sources. We can see pharmaceutical companies identifying trends in diseases and compiling data sets that can garner better and quicker results in drug discovery.
Enterprises Are Looking For Activation Partners
While the future holds a lot of scope for advances in the bioinformatics industry, with AI, ML, and Analytics, stakeholders still need experienced implementation partners. What also matters is their industry know-how and the ability to streamline the research timelines and in many ways make it cost-efficient as well.
DNA analysis, new drug development, artificial organ generation are picking up the pace. Here, the right AI solution provider can be a game-changer providing insights that would have taken a lot of years or to generate if done the traditional way.