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Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedi...

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Autores principales: Vatansever, Sezen, Schlessinger, Avner, Wacker, Daniel, Kaniskan, H. Ümit, Jin, Jian, Zhou, Ming‐Ming, Zhang, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8043990/
https://www.ncbi.nlm.nih.gov/pubmed/33295676
http://dx.doi.org/10.1002/med.21764
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author Vatansever, Sezen
Schlessinger, Avner
Wacker, Daniel
Kaniskan, H. Ümit
Jin, Jian
Zhou, Ming‐Ming
Zhang, Bin
author_facet Vatansever, Sezen
Schlessinger, Avner
Wacker, Daniel
Kaniskan, H. Ümit
Jin, Jian
Zhou, Ming‐Ming
Zhang, Bin
author_sort Vatansever, Sezen
collection PubMed
description Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML‐driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML‐powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state‐of‐the‐art of AI/ML‐guided CNS drug discovery, focusing on blood–brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
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spelling pubmed-80439902021-07-02 Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions Vatansever, Sezen Schlessinger, Avner Wacker, Daniel Kaniskan, H. Ümit Jin, Jian Zhou, Ming‐Ming Zhang, Bin Med Res Rev Review Articles Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML‐driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML‐powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state‐of‐the‐art of AI/ML‐guided CNS drug discovery, focusing on blood–brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties. John Wiley and Sons Inc. 2020-12-09 2021-05 /pmc/articles/PMC8043990/ /pubmed/33295676 http://dx.doi.org/10.1002/med.21764 Text en © 2020 The Authors. Medicinal Research Reviews published by Wiley Periodicals LLC https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Articles
Vatansever, Sezen
Schlessinger, Avner
Wacker, Daniel
Kaniskan, H. Ümit
Jin, Jian
Zhou, Ming‐Ming
Zhang, Bin
Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
title Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
title_full Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
title_fullStr Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
title_full_unstemmed Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
title_short Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
title_sort artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: state‐of‐the‐arts and future directions
topic Review Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8043990/
https://www.ncbi.nlm.nih.gov/pubmed/33295676
http://dx.doi.org/10.1002/med.21764
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