AI and Machine Learning Help In Developing New Drugs

Artificial intelligence (AI) and Machine Learning (ML) have validated the effort and resources being invested in the development of various kinds of new products and solutions for different industries. One of the most complex industries where AI ML applications have made a significant impact is pharmaceutical research, specifically identified with drug discovery, drug development, and protein synthesis for re-engineered drugs and vaccines. In the last 5 years, researchers have been increasingly focused on building a real time model for drug development programs that has significant participation from trainers and students with AI ML certifications. In this article, we have tried to highlight the role of AI ML research in the development of new drugs and vaccines, and if these can be sustained by the current ecosystem of AI ML data science academics. 

Let’s find out.

Protein Synthesis using AI ML

Back in 2020, Google’s Deep Mind AI project had developed one of the world’s foremost AI technology for biology. This technology was piqued to favor the understanding of 3d structures of protein molecules. Since then, many exercises have been conducted inside and outside of Google’s AI labs that potentially state that advanced drug discovery is now possible with deep learning dimensions of AI modalities. These approaches have a starling accuracy of above 95%, dramatically referring to the real world impact of having AI ML at the center of protein synthesis in drug development and redesigning studies.

Lab researchers have developed a new AI algorithm that governs the use of new ingredients for protein synthesis. There are many ways to develop a new drug, and a majority of these are hypothetically designed in the lab. In the commercial world, the nature of drug development might vastly differ from the lab ecosystem. This gap in expectations and conditions affects the pace and quality of the final drug. Now, AI engineers have come up with a new model to help drug development companies zero on to the exact ingredients that would be useful in the creation of new medicines and vaccines with much better efficacy in the wider context of healthcare management, pandemic control, and above all, health safety against side effects.

Drug identification 

AI has really amped the scope of having automation for drug identification techniques, which majorly depends on how different domains related to deep learning, knowledge representation, and artificial neural networks come together. In drug discovery, “perceptrons” are used to create newer AI ML models to identify patterns and optimizers for classification techniques. The new AI ML models used in the creation of perceptrons guarantees that the drug identification process goes on in an unsupervised manner based on a synthetic pathway that more or less can be trained for biological system classification and clinical data analytics. Since drug identification also requires the participation of human samples and more or less depends on the existing reputation and market positioning of the drug maker, it is very important for the researchers to bestow a certain level of QA/ QC benchmarks on their ongoing pharmaceutical product development, even if it means taking advantage of AI ML models for automated manufacturing, DL based drug identification with protein synthesis, personalized drug engineering, and AI based anomaly detection, and so on. All of these can help stimulate the overall commercial level drug identification process in a perfect lab conditions.

Drug Re-engineering 

There are more than 8000 different types of diseases in the world that inflict humans with minor or major symptoms, and for these diseases, there are no drugs or preventive lines of vaccination. However, AI researchers are working at the highest levels to develop new medicines that have been actually re-engineered from the current crop of medicines. 

Re-engineering is a fairly new concept in the pharmaceutical industry, but it has gained massive popularity due to its novel applications. Drug makers use AI and other inventive methods to develop new dimensions of applications for the existing drug families. The science is called “molecule optimization” which allows pharmaceutical researchers to identify certain molecular properties to build newer blocks of proteins for new drugs. It’s called re-engineering because these ingredients, mostly remodeled proteins, are actually synthesized from older templates that actually served as markers for the reproduction of new medicines. 

According to researchers, re-engineered drugs have 51% higher efficacy and potency compared to newer drugs, and these drugs also bind better with existing immunity systems in stopping a disease from spreading in the body and population. With vaccination, these re-engineered drugs can act on certain targeted diseases with far better outcomes.

otency compared to newer drugs, and these drugs also bind better with existing immunity systems in stopping a disease from spreading in the body and population. With vaccination, these re-engineered drugs can act on certain targeted diseases with far better outcomes.