A Review on Artificial Intelligence and Machine Learning to Improve Cancer Management and Drug Discovery
DOI:
https://doi.org/10.31033/ijrasb.9.3.26Keywords:
AI, Cancer, drug discovery, computational method, therapeutic target medicationsAbstract
Typical pharmacological effect screening techniques use diluted natural ingredients that do not segregate active components. For the last two decades, contemporary medicine has identified and isolated potent active isomeric compounds. Multi-target therapies were a novel notion in the mid-2000s, but they will be one of the more critical advancements in developing drugs in 2021. Target-based drug development for effective natural anticancer therapeutics based on well-defined fragments is being researched instead of naturally occurring combinations. This paper highlights computer-aided/fragment-based structure deconstruction and an inter method for natural anticancer medicines. The use of computer-aided drug development has increased (CADD). This study focused on antitumor agents and computer-aided drug development.
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