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Khan MK, Raza M, Shahbaz M, Hussain I, Khan MF, Xie Z, Shah SSA, Tareen AK, Bashir Z, Khan K. The recent advances in the approach of artificial intelligence (AI) towards drug discovery. Front Chem 2024; 12:1408740. [PMID: 38882215 PMCID: PMC11176507 DOI: 10.3389/fchem.2024.1408740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 04/26/2024] [Indexed: 06/18/2024] Open
Abstract
Artificial intelligence (AI) has recently emerged as a unique developmental influence that is playing an important role in the development of medicine. The AI medium is showing the potential in unprecedented advancements in truth and efficiency. The intersection of AI has the potential to revolutionize drug discovery. However, AI also has limitations and experts should be aware of these data access and ethical issues. The use of AI techniques for drug discovery applications has increased considerably over the past few years, including combinatorial QSAR and QSPR, virtual screening, and denovo drug design. The purpose of this survey is to give a general overview of drug discovery based on artificial intelligence, and associated applications. We also highlighted the gaps present in the traditional method for drug designing. In addition, potential strategies and approaches to overcome current challenges are discussed to address the constraints of AI within this field. We hope that this survey plays a comprehensive role in understanding the potential of AI in drug discovery.
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Affiliation(s)
- Mahroza Kanwal Khan
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, China
| | - Mohsin Raza
- Additive Manufacturing Institute, Shenzhen University, Shenzhen, China
| | - Muhammad Shahbaz
- Additive Manufacturing Institute, Shenzhen University, Shenzhen, China
| | - Iftikhar Hussain
- Department of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China
- A. J. Drexel Nanomaterials Institute and Department of Materials Science and Engineering, Drexel University, Philadelphia, PA, United States
| | - Muhammad Farooq Khan
- Department of Electrical Engineering, Sejong University, Seoul, Republic of Korea
| | - Zhongjian Xie
- Shenzhen Children's Hospital, Clinical Medical College of Southern University of Science and Technology, Shenzhen, China
| | - Syed Shoaib Ahmad Shah
- Department of Chemistry, School of Natural Sciences, National University of Sciences and Technology, Islamabad, Pakistan
| | - Ayesha Khan Tareen
- School of Mechanical Engineering, Dongguan University of Technology, Dongguan, China
| | - Zoobia Bashir
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, China
| | - Karim Khan
- Additive Manufacturing Institute, Shenzhen University, Shenzhen, China
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Khorsandi D, Rezayat D, Sezen S, Ferrao R, Khosravi A, Zarepour A, Khorsandi M, Hashemian M, Iravani S, Zarrabi A. Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like? J Mater Chem B 2024; 12:4584-4612. [PMID: 38686396 DOI: 10.1039/d4tb00310a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
The application of three- and four-dimensional (3D/4D) printing in cancer research represents a significant advancement in understanding and addressing the complexities of cancer biology. 3D/4D materials provide more physiologically relevant environments compared to traditional two-dimensional models, allowing for a more accurate representation of the tumor microenvironment that enables researchers to study tumor progression, drug responses, and interactions with surrounding tissues under conditions similar to in vivo conditions. The dynamic nature of 4D materials introduces the element of time, allowing for the observation of temporal changes in cancer behavior and response to therapeutic interventions. The use of 3D/4D printing in cancer research holds great promise for advancing our understanding of the disease and improving the translation of preclinical findings to clinical applications. Accordingly, this review aims to briefly discuss 3D and 4D printing and their advantages and limitations in the field of cancer. Moreover, new techniques such as 5D/6D printing and artificial intelligence (AI) are also introduced as methods that could be used to overcome the limitations of 3D/4D printing and opened promising ways for the fast and precise diagnosis and treatment of cancer.
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Affiliation(s)
- Danial Khorsandi
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90024, USA
| | - Dorsa Rezayat
- Center for Global Design and Manufacturing, College of Engineering and Applied Science, University of Cincinnati, 2901 Woodside Drive, Cincinnati, OH 45221, USA
| | - Serap Sezen
- Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla 34956 Istanbul, Türkiye
- Nanotechnology Research and Application Center, Sabanci University, Tuzla 34956 Istanbul, Türkiye
| | - Rafaela Ferrao
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90024, USA
- University of Coimbra, Institute for Interdisciplinary Research, Doctoral Programme in Experimental Biology and Biomedicine (PDBEB), Portugal
| | - Arezoo Khosravi
- Department of Genetics and Bioengineering, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Istanbul 34959, Türkiye
| | - Atefeh Zarepour
- Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai - 600 077, India
| | - Melika Khorsandi
- Department of Cellular and Molecular Biology, Najafabad Branch, Islamic Azad University, Isfahan, Iran
| | - Mohammad Hashemian
- Department of Cellular and Molecular Biology, Najafabad Branch, Islamic Azad University, Isfahan, Iran
| | - Siavash Iravani
- Independent Researcher, W Nazar ST, Boostan Ave, Isfahan, Iran.
| | - Ali Zarrabi
- Department of Biomedical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul 34396, Türkiye.
- Graduate School of Biotechnology and Bioengineering, Yuan Ze University, Taoyuan 320315, Taiwan
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Gombolay GY, Silva A, Schrum M, Gopalan N, Hallman-Cooper J, Dutt M, Gombolay M. Effects of explainable artificial intelligence in neurology decision support. Ann Clin Transl Neurol 2024; 11:1224-1235. [PMID: 38581138 PMCID: PMC11093252 DOI: 10.1002/acn3.52036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/20/2024] [Accepted: 02/27/2024] [Indexed: 04/08/2024] Open
Abstract
OBJECTIVE Artificial intelligence (AI)-based decision support systems (DSS) are utilized in medicine but underlying decision-making processes are usually unknown. Explainable AI (xAI) techniques provide insight into DSS, but little is known on how to design xAI for clinicians. Here we investigate the impact of various xAI techniques on a clinician's interaction with an AI-based DSS in decision-making tasks as compared to a general population. METHODS We conducted a randomized, blinded study in which members of the Child Neurology Society and American Academy of Neurology were compared to a general population. Participants received recommendations from a DSS via a random assignment of an xAI intervention (decision tree, crowd sourced agreement, case-based reasoning, probability scores, counterfactual reasoning, feature importance, templated language, and no explanations). Primary outcomes included test performance and perceived explainability, trust, and social competence of the DSS. Secondary outcomes included compliance, understandability, and agreement per question. RESULTS We had 81 neurology participants with 284 in the general population. Decision trees were perceived as the more explainable by the medical versus general population (P < 0.01) and as more explainable than probability scores within the medical population (P < 0.001). Increasing neurology experience and perceived explainability degraded performance (P = 0.0214). Performance was not predicted by xAI method but by perceived explainability. INTERPRETATION xAI methods have different impacts on a medical versus general population; thus, xAI is not uniformly beneficial, and there is no one-size-fits-all approach. Further user-centered xAI research targeting clinicians and to develop personalized DSS for clinicians is needed.
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Affiliation(s)
- Grace Y Gombolay
- Department of Pediatrics, Division of Neurology, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, GA, USA
| | - Andrew Silva
- Georgia Institute of Technology, Atlanta, GA, USA
| | | | | | - Jamika Hallman-Cooper
- Department of Pediatrics, Division of Neurology, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, GA, USA
| | - Monideep Dutt
- Department of Pediatrics, Division of Neurology, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, GA, USA
| | - Matthew Gombolay
- Department of Pediatrics, Division of Neurology, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, GA, USA
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Yilmaz F, Brickman A, Najdawi F, Yakirevich E, Egger R, Resnick MB. Advancing Artificial Intelligence Integration Into the Pathology Workflow: Exploring Opportunities in Gastrointestinal Tract Biopsies. J Transl Med 2024; 104:102043. [PMID: 38431118 DOI: 10.1016/j.labinv.2024.102043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024] Open
Abstract
This review aims to present a comprehensive overview of the current landscape of artificial intelligence (AI) applications in the analysis of tubular gastrointestinal biopsies. These publications cover a spectrum of conditions, ranging from inflammatory ailments to malignancies. Moving beyond the conventional diagnosis based on hematoxylin and eosin-stained whole-slide images, the review explores additional implications of AI, including its involvement in interpreting immunohistochemical results, molecular subtyping, and the identification of cellular spatial biomarkers. Furthermore, the review examines how AI can contribute to enhancing the quality and control of diagnostic processes, introducing new workflow options, and addressing the limitations and caveats associated with current AI platforms in this context.
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Affiliation(s)
- Fazilet Yilmaz
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Arlen Brickman
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Fedaa Najdawi
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Evgeny Yakirevich
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | | | - Murray B Resnick
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island.
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Dombrowski AK, Gerken JE, Muller KR, Kessel P. Diffeomorphic Counterfactuals With Generative Models. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024; 46:3257-3274. [PMID: 38055368 DOI: 10.1109/tpami.2023.3339980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
Counterfactuals can explain classification decisions of neural networks in a human interpretable way. We propose a simple but effective method to generate such counterfactuals. More specifically, we perform a suitable diffeomorphic coordinate transformation and then perform gradient ascent in these coordinates to find counterfactuals which are classified with great confidence as a specified target class. We propose two methods to leverage generative models to construct such suitable coordinate systems that are either exactly or approximately diffeomorphic. We analyze the generation process theoretically using Riemannian differential geometry and validate the quality of the generated counterfactuals using various qualitative and quantitative measures.
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Klauschen F, Dippel J, Keyl P, Jurmeister P, Bockmayr M, Mock A, Buchstab O, Alber M, Ruff L, Montavon G, Müller KR. [Explainable artificial intelligence in pathology]. PATHOLOGIE (HEIDELBERG, GERMANY) 2024; 45:133-139. [PMID: 38315198 DOI: 10.1007/s00292-024-01308-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/19/2024] [Indexed: 02/07/2024]
Abstract
With the advancements in precision medicine, the demands on pathological diagnostics have increased, requiring standardized, quantitative, and integrated assessments of histomorphological and molecular pathological data. Great hopes are placed in artificial intelligence (AI) methods, which have demonstrated the ability to analyze complex clinical, histological, and molecular data for disease classification, biomarker quantification, and prognosis estimation. This paper provides an overview of the latest developments in pathology AI, discusses the limitations, particularly concerning the black box character of AI, and describes solutions to make decision processes more transparent using methods of so-called explainable AI (XAI).
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Affiliation(s)
- Frederick Klauschen
- Pathologisches Institut, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland.
- Institut für Pathologie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland.
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Deutschland.
- Deutsches Krebsforschungszentrum (DKTK/DKFZ), Partnerstandort München, München, Deutschland.
| | - Jonas Dippel
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Deutschland
- Machine Learning Group, Fachbereich Elektrotechnik und Informatik, Technische Universität Berlin, Berlin, Deutschland
| | - Philipp Keyl
- Pathologisches Institut, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland
| | - Philipp Jurmeister
- Pathologisches Institut, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland
- Deutsches Krebsforschungszentrum (DKTK/DKFZ), Partnerstandort München, München, Deutschland
| | - Michael Bockmayr
- Institut für Pathologie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland
- Pädiatrische Hämatologie und Onkologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
- Forschungsinstitut Kinderkrebs-Zentrum Hamburg, Hamburg, Deutschland
| | - Andreas Mock
- Pathologisches Institut, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland
- Deutsches Krebsforschungszentrum (DKTK/DKFZ), Partnerstandort München, München, Deutschland
| | - Oliver Buchstab
- Pathologisches Institut, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland
| | - Maximilian Alber
- Institut für Pathologie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland
- Aignostics GmbH, Berlin, Deutschland
| | | | - Grégoire Montavon
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Deutschland
- Machine Learning Group, Fachbereich Elektrotechnik und Informatik, Technische Universität Berlin, Berlin, Deutschland
- Fachbereich Mathematik und Informatik, Freie Universität Berlin, Berlin, Deutschland
| | - Klaus-Robert Müller
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Deutschland.
- Machine Learning Group, Fachbereich Elektrotechnik und Informatik, Technische Universität Berlin, Berlin, Deutschland.
- Department of Artificial Intelligence, Korea University, Seoul, Südkorea.
- Max-Planck-Institut für Informatik, Saarbrücken, Deutschland.
- Machine Learning/Intelligent Data Analysis (IDA), Technische Universität Berlin, Marchstr. 23, 10587, Berlin, Deutschland.
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