TY - JOUR
T1 - Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery
AU - Nagarajan, Nagasundaram
AU - Yapp, Edward K.Y.
AU - Le, Nguyen Quoc Khanh
AU - Kamaraj, Balu
AU - Al-Subaie, Abeer Mohammed
AU - Yeh, Hui Yuan
N1 - Publisher Copyright:
© 2019 Nagasundaram Nagarajan et al.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Artificial intelligence (AI) proves to have enormous potential in many areas of healthcare including research and chemical discoveries. Using large amounts of aggregated data, the AI can discover and learn further transforming these data into "usable" knowledge. Being well aware of this, the world's leading pharmaceutical companies have already begun to use artificial intelligence to improve their research regarding new drugs. The goal is to exploit modern computational biology and machine learning systems to predict the molecular behaviour and the likelihood of getting a useful drug, thus saving time and money on unnecessary tests. Clinical studies, electronic medical records, high-resolution medical images, and genomic profiles can be used as resources to aid drug development. Pharmaceutical and medical researchers have extensive data sets that can be analyzed by strong AI systems. This review focused on how computational biology and artificial intelligence technologies can be implemented by integrating the knowledge of cancer drugs, drug resistance, next-generation sequencing, genetic variants, and structural biology in the cancer precision drug discovery.
AB - Artificial intelligence (AI) proves to have enormous potential in many areas of healthcare including research and chemical discoveries. Using large amounts of aggregated data, the AI can discover and learn further transforming these data into "usable" knowledge. Being well aware of this, the world's leading pharmaceutical companies have already begun to use artificial intelligence to improve their research regarding new drugs. The goal is to exploit modern computational biology and machine learning systems to predict the molecular behaviour and the likelihood of getting a useful drug, thus saving time and money on unnecessary tests. Clinical studies, electronic medical records, high-resolution medical images, and genomic profiles can be used as resources to aid drug development. Pharmaceutical and medical researchers have extensive data sets that can be analyzed by strong AI systems. This review focused on how computational biology and artificial intelligence technologies can be implemented by integrating the knowledge of cancer drugs, drug resistance, next-generation sequencing, genetic variants, and structural biology in the cancer precision drug discovery.
UR - http://www.scopus.com/inward/record.url?scp=85075710497&partnerID=8YFLogxK
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U2 - 10.1155/2019/8427042
DO - 10.1155/2019/8427042
M3 - Review article
C2 - 31886259
AN - SCOPUS:85075710497
SN - 2314-6133
VL - 2019
JO - BioMed Research International
JF - BioMed Research International
M1 - 8427042
ER -
