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APPLICATIONS OF DEEP LEARNING IN BIOINFORMATICS AND DRUG DISCOVERY
  • Roshan Kotkondawar,
  • Sanjay Sutar,
  • Arvind Kiwelekar
Roshan Kotkondawar
Department of Information Technology, Dr. Babasaheb, Ambedkar Technological University

Corresponding Author:[email protected]

Author Profile
Sanjay Sutar
Department of Information Technology, Dr. Babasaheb, Ambedkar Technological University
Arvind Kiwelekar
Department of Information Technology, Dr. Babasaheb, Ambedkar Technological University

Abstract

The remarkable progress and growth in the fields of Artificial Intelligence (AI) and BioChemical sciences in recent times have been continuously solving many complex problems. AI has shown a measurable impact in almost all areas of life including healthcare. The exponential growth of data, technologies for efficient storage, retrieval of this huge data, and ever-increasing computational power have given rise to data-driven AI-based approaches like Deep Learning (DL). Productive use of Big Data has been recognized in many phases of drug discovery. The availability of massive biochemical data catalyzed by the fast processing abilities of GPUs is pushing up the huge success of deep learning approaches for some key activities in drug discovery like drug-target interaction or virtual screening. DL techniques have shown the potential to strategically reduce the time and huge cost spent in the drug discovery pipeline. This paper discusses the significance of DL in the context of biochemical Big Data and the drug discovery process.