• Neha sharma
• Deepak kumar

The changes that occur are exciting and more challenging in our industry. There has been a massive increase in the amount of data in health informatics in the last decade. Over the years, deep learning is raising with its extraordinary success in the research areas of artificial intelligence. If we form larger neural network and then we train it with more available data and fast enough computers their performance then continues to arise. Earlier, machine-learning tools such as QSAR (quantitative structure-activity relationship) modeling have been used to identify potential biological active molecules from millions candidate compounds for drug discovery. But, in this era of big data machine learning approaches lack efficiency. Hence, deep learning evolved as a solution to the problem of big data. In this paper we discuss various deep learning approaches studied for various applications of health informatics with special reference to drug discovery.

"Deep learning aiding Health Informatics in Drug discovery", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.76-84, June 2019, Available :http://www.jetir.org/papers/JETIR1907M12.pdf

"Deep learning aiding Health Informatics in Drug discovery", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp76-84, June 2019, Available at : http://www.jetir.org/papers/JETIR1907M12.pdf