How AI / ML & Data Labeling are accelerating the Drug Discovery
process?

The FDA (Food and Drug Administration) acknowledges the growing usage of AI/ML throughout the drug development life cycle and in a variety of therapeutic areas. In fact, the FDA has noticed a notable increase in the quantity of medication and biologic application submissions employing AI/ML components in recent years, with more than 100 applications recorded in 2021. These contributions cover the entire spectrum of pharmaceutical development, including drug discovery, clinical research, post-market safety surveillance, and advanced pharmaceutical manufacturing.

Data Labeling in Drug Discovery

The pharmaceutical industry is increasingly utilizing Machine Learning and Data Labeling in several areas, including Drug Discovery, which has improved the sector as a whole. The growing number of businesses whose business models depend heavily on Data Labeling is evidence of the technology’s success. Major pharmaceutical corporations have also looked into using the same techniques for drug development.

The capabilities of Data Labeling and their value in the field of Drug Discovery, they must be incorporated into any future developments in this area. The goal is to apply high-throughput screening technology to minimize the asset and work seriousness of drug disclosure. Data Labeling may one day eliminate, at least substantially reduce, the necessity for live animal testing. These results demonstrate the value of machine learning as a method for discovering novel medications.

Data Labeling throughout a Pharmaceutical product’s lifespan

Data Labeling can help with rational drug design, support decision-making, identify the best course of treatment for a patient, including personalized medicines, manage the clinical data generated, and use it for future drug development. Hence, it is quite reasonable to assume that it will play a big role in the development of pharmaceutical products in the upcoming period.

Future scope of Data Labeling in the Pharma industry

Data Labeling’s key potential in the pharmaceutical sector is to lower costs and boost productivity. Numerous studies have shown that dynamic learning may distinguish Data Labeling models with a high degree of accuracy while using half or less information than conventional AI and information subsampling techniques. 

It seems that less repetition and predisposition, as well as acquiring more significant knowledge to overcome decision restrictions, are critical factors in this greater execution. As a result, screening costs seem to be lowered by as much as 90% without accounting for the anticipated mechanical overhead for actually carrying out dynamic learning activities.

How Data Labeler can help with the best Data Labeling services?

• By creating the datasets required to power your algorithms, Data Labeler aims to provide a
• crucial service that enables businesses to concentrate on their primary AI/ML activities.
• Below are the data labeling services and data annotation provided by us.
• Bounding Boxes for Object Detection
• Polygons for Semantic and Instance Segmentation
• Points for facial recognition and body pose detection
• Texts for image captioning
• Select for image classification
• Multi-Select for more complex image classification

For further information, please contact us.