Deep Learning: Predicts Drug Development to Combat COVID-19 Virus
Deep learning is helping to combat the contagious COVID-19 virus in 2021
The outbreak of the COVID-19 virus in 2019 has stopped the world from physical interaction and transformed it into digital interaction. The year 2020 was very crucial for the healthcare department for a drug development to combat this COVID-19 virus. There were two drug combination models created to protect the global citizens— remdesivir and remdesivir with IQ-1S. Thus, deep learning and neural network can identify the blends of drug synergy for treating harmful viruses such as SARS-CoV-2.
MIT has learnt about new drug combination models through drug-target interaction as well as drug synergy. Deep learning is known for providing the ability to change the scenario layer after layer through the access to appropriate datasets for drug development. The high speed of the COVID-19 virus was intimidating to the healthcare sector and created a very difficult challenge to treat millions of patients at the same time across the world.
Data scientists harness the training datasets from cancer or any other major diseases for a new drug development. But it is difficult for them to use deep learning for a new drug development of a completely new disease. There is no opportunity for drug synergy in this COVID-19 case. But the group of scientists utilizes deep learning neural network with the combination of drug-target interaction as well as drug-drug synergy. This helped them in knowing a set of biological targets related to the specific disease and have a better understanding of anti-viral activities of a new drug development.
The new drug combination models are set to be used in the case of new harmful Delta variant through additional drug combination model with data synergy for these new mutations across the world. The contagious COVID-19 virus has caused over 5 million deaths across the world since its outbreak. Drug synergy with deep learning is the best alternative to seek for new drug developments despite low-quality training data. The new drug combination model with data synergy can provide multiple benefits such as therapeutic potency, efficacy, reduce the required dose, reduce side-effects, and many more. The main problems in this case are the lack of large scale training data with high-quality data synergy for deep learning as well as the drug combination model should have the potential to cover more than 100 different molecules.