Artificial intelligence in cancer target identification and drug discovery

Abstract

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer

“…Over the last 15 years, researchers have made great efforts to develop drug repurposing methods, from early statistics-based chemoinformatics approaches [84] to recent ones using artificial intelligence [85] , [86] , [87] and network-based methods [88] , [89] . These computational methods have been demonstrated to be effective but some limitations remain.…”

Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers

“…As is the case in many other fields of healthcare, the integration of AI in cancer care is expected to reshape the existing scenario in the future [ 10 ]. For example, as a predictive modeling and early detection, AI could be used to analyze data from a variety of sources, such as electronic health records, genetic information, and environmental data, to predict an individual’s risk of developing cancer and to tailor prevention strategies accordingly [ 13 , 14 , 15 , 16 ]. AI-related applications may reduce screening costs [ 17 ], provide more reliable diagnostics [ 13 , 18 , 19 , 20 ], improve prognostics [ 13 , 19 , 21 , 22 , 23 , 24 , 25 ], and aid in the discovery of new drugs [ 14 , 15 ].…”

“…For example, as a predictive modeling and early detection, AI could be used to analyze data from a variety of sources, such as electronic health records, genetic information, and environmental data, to predict an individual’s risk of developing cancer and to tailor prevention strategies accordingly [ 13 , 14 , 15 , 16 ]. AI-related applications may reduce screening costs [ 17 ], provide more reliable diagnostics [ 13 , 18 , 19 , 20 ], improve prognostics [ 13 , 19 , 21 , 22 , 23 , 24 , 25 ], and aid in the discovery of new drugs [ 14 , 15 ]. Several areas of cancer care are expected to benefit from AI-related applications, including cancer radiology and clinical oncology [ 10 ].…”

Integration of artificial intelligence and precision oncology in Latin America

“…This process usually takes months, or even years, and complications can occur in every phase. AI has the potential for the prediction of function, differences, and weaknesses of molecules derived from the transcriptomics database to identify a new therapeutic target ( 36 ). ML biology analysis can shorten the process of target identification modulating complex information related to the heterogeneity of molecules and biochemical interactions.…”