Monthly Archives

April 2021

Artificial Intelligence in Bioinformatics

By | Blog

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.

If you’re looking to develop perfect drug discovery, correct analytical research, and accurate methods for integrating medical science bioinformatics, please check out at Celbridge Science. To connect with us at patrick.hogan@celbridgescience.com or call us anytime on 1636-594-2242.

Advances In Using AI In Drug Development

By | Blog

Artificial Intelligence has transformed biomedical research in terms of Drug Discovery and Design. It has made the process more efficient and has paved a path for the discovery and development of inexpensive and efficient drugs and rational structure-based drug design, with reduced time and costs. Pharmaceutical companies are using Machine Learning Models and Advanced AI Insights to analyze and use complex datasets as a solution to the increased cost of new drug development.

Drug discovery using AI

On average, a new drug coming to the market has to go through phases that involve testing the drug on sample groups for years, and with costs involved up to billions. It also involves the standard approaches of testing how different molecules of the drug composition interact with different targets. The preclinical development phase of drug discovery tests potential drug targets on animal samples.

During this phase, trials can be accelerated with minimal errors using AI models, enabling researchers to easily find the permutations and combinations of the drug interaction with the animal test sample model. AI can also facilitate participant monitoring during trials, generating a larger set of data more quickly, and aid in participant retention by personalizing the trial experience. With advancements in AI, the automatic feature extraction ability of deep learning supports models with better accuracy and delivers more reliable results. Secondly, deep learning models’ generative ability can be utilized to speed up the drug discovery process with better prediction capability and a low failure rate.

Drug design using AI

Drug design is the process of finding new medicinal drugs based on the knowledge of biological targets. How strongly a molecule can bind to a target is the main goal of a drug design phase. With the computational machine learning models, the computational affinity of a compound for synthesis can be predicted, and hence only one compound needs to be synthesized saving enormous time and cost.
Artificial intelligence (AI) proves very efficient in various fields of drug design that include virtual screening, activity scoring, quantitative structure-activity relationship (QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion, and toxicity (ADME/T) properties.

Many pharmaceutical companies are riding the wave of Drug Design and Discovery using AI. For example, AstraZeneca using AI is researching new drugs on chronic kidney diseases. Bristol-Myers Squibb (BMS) has deployed machine learning models in finding data patterns that are associated with CYP450 inhibitors, these patterns help to reduce adverse side effects and interactions of the drug-in-development.

GSK Pharma has its in-house AI unit which has developed a machine-learning algorithm to identify protein crystals. Not only the big pharma players but the mid-sized pharmaceutical companies and startups are also taking advantage of these advancements of AI in drug discovery and design.

The next decade will see a transition from traditional drug design and discovery methodologies to highly intelligent AI-based algorithms that will deal with the new scale and complexity of the data used in drug design and discovery.

please check out at Celbridge Science. To connect with us at patrick.hogan@celbridgescience.com or call us anytime on 636-594-2242.