EC Neurology

In toxicology, AI tools have been shown to be beneficial in predicting and modelling the neurotoxic outcome. Past neurotoxicity assessments were conducted through laboratory animals and cell cultures. These assessments took a lot of time and were also subject to debate. AI techniques, such as machine learning and deep learning algorithms, can be utilized for integrating high dimensional biological, chemical and omics data to identify predictive neurotoxicity biomarkers and mechanistic pathways. With these methods, predictive models can be built which can simulate the neuronal response to chemicals, evaluate the dose-response, and predict delayed neurotoxicity. AI further facilitates high-throughput screening by lightening experimental burdens while increasing accuracy and reproducibility. Despite having all these benefits, there are still some challenges that need to be faced like data heterogeneity. The future will see more AI used with systems biology, computational neuroscience, population based medicine, and personalized medicine, to assess risk and work on therapy. Artificial intelligence represents a powerful new paradigm in the prediction and modelling of neurotoxic outcomes and could increase the efficiency, efficacy ethical and predictive capabilities of toxicology.

 Keywords: Artificial Intelligence; Neurotoxicity; Predictive Modeling; Machine Learning; Risk Assessment

1. Gadaleta D., et al. “Prediction of the neurotoxic potential of chemicals based on modelling of molecular initiating events upstream of the adverse outcome pathways of (developmental) neurotoxicity”. International Journal of Molecular Sciences6 (2022): 3053.
2. Furxhi I and Murphy F. “Predicting in vitro neurotoxicity induced by nanoparticles using machine learning”. International Journal of Molecular Sciences15 (2020): 5280.
3. Kuusisto F., et al. “Machine learning to predict developmental neurotoxicity with high-throughput data from 2D bio-engineered tissues”. 2019 18^th IEEE international conference on machine learning and applications (ICMLA) (2019): 293-298.
4. Tywabi-Ngeva Z., et al. “A global analysis of research outputs on neurotoxicants from 2011-2020: adverse effects on humans and the environment”. Applied Sciences16 (2022): 8275.
5. Useinovic N and Jevtovic-Todorovic V. “Controversies in anesthesia-induced developmental neurotoxicity”. Best Practice and Research Clinical Anaesthesiology1 (2023): 28-39.
6. Huang Z., et al. “Effect of parental perinatal exposure to L-glutamate monosodium salt monohydrate on developmental neurotoxicity in rat offspring”. Toxicology and Applied Pharmacology 502 (2025): 117450.
7. Spencer PS and Lein PJ. “Neurotoxicity”. Encyclopedia of Toxicology (Third Edition) (2024): 489-500.
8. Bilge S. “Neurotoxicity, types, clinical manifestations, diagnosis and treatment”. Neurotoxicity: New advances (2022).
9. Grisold W and Carozzi VA. “Toxicity in peripheral nerves: an overview”. Toxics9 (2021): 218.
10. Nabi M and Tabassum N. “Role of environmental toxicants on neurodegenerative disorders”. Frontiers in Toxicology 4 (2022): 837579.
11. Rodríguez A., et al. “Pesticides: Environmental stressors implicated in the development of central nervous system disorders and neurodegeneration”. Stresses2 (2025): 31.
12. Armas FV and D'Angiulli A. “Neuroinflammation and neurodegeneration of the central nervous system from air pollutants: A scoping review”. Toxics11 (2022): 666.
13. Tang H., et al. “Predicting presynaptic and postsynaptic neurotoxins by developing feature selection technique”. BioMed Research International (2017): 3267325.
14. Priyadarshanee M., et al. “Mechanism of toxicity and adverse health effects of environmental pollutants”. In microbial biodegradation and bioremediation (2022): 33-53.
15. Nevins S., et al. “Nanotechnology approaches for prevention and treatment of chemotherapy‐induced neurotoxicity, neuropathy, and cardiomyopathy in breast and ovarian cancer survivors”. Small 41 (2024): e2300744.
16. Singh PK., et al. “Critical review on toxic contaminants in surface water ecosystem: sources, monitoring, and its impact on human health”. Environmental Science and Pollution Research45 (2024): 56428-56462.
17. Zubair M., et al. “Application of nanotechnology for targeted drug delivery and nontoxicity”. International Journal of General Practice Nursing 2 (2024): 57-67.
18. Azevedo LF., et al. “Evidence on neurotoxicity after intrauterine and childhood exposure to organomercurials”. International Journal of Environmental Research and Public Health2 (2023): 1070.
19. Campbell MA., et al. “Life course considerations in environmental health: developmental neurotoxicity of domoic acid at doses below acute effect levels in adult humans”. Birth Defects Research12 (2024): e2419.
20. Schneider JS. “Neurotoxicity and outcomes from developmental lead exposure: persistent or permanent?”. Environmental Health Perspectives8 (2023): 85002.
21. Teleanu DM., et al. “An overview of oxidative stress, neuroinflammation, and neurodegenerative diseases”. International Journal of Molecular Sciences11 (2022): 5938.
22. Jurcău MC., et al. “The link between oxidative stress, mitochondrial dysfunction and neuroinflammation in the pathophysiology of Alzheimer’s disease: therapeutic implications and future perspectives”. Antioxidants11 (2022): 2167.
23. Karvandi MS., et al. “The neuroprotective effects of targeting key factors of neuronal cell death in neurodegenerative diseases: The role of ER stress, oxidative stress, and neuroinflammation”. Frontiers in Cellular Neuroscience 17 (2023): 1105247.
24. Lin Z and Chou WC. “Machine learning and artificial intelligence in toxicological sciences”. Toxicological Sciences1 (2022): 7-19.
25. Pérez Santín E., et al. “Toxicity prediction based on artificial intelligence: A multidisciplinary overview”. Wiley Interdisciplinary Reviews: Computational Molecular Science5 (2021): e1516.
26. Yan X., et al. “Converting nanotoxicity data to information using artificial intelligence and simulation”. Chemical Reviews13 (2023): 8575-8637.
27. Zhao X., et al. “Machine learning modeling and insights into the structural characteristics of drug-induced neurotoxicity”. Journal of Chemical Information and Modeling23 (2022): 6035-6045.
28. Wang T., et al. “Developmental toxicity: artificial intelligence-powered assessments”. Trends in Pharmacological Sciences6 (2025): 486-502.
29. Jia X., et al. “Advancing computational toxicology by interpretable machine learning”. Environmental Science and Technology46 (2023): 17690-17706.
30. Bueso-Bordils JI., et al. “Overview of computational toxicology methods applied in drug and green chemical discovery”. Journal of Xenobiotics4 (2024): 1901-1918.
31. Amorim AM., et al. “Advancing drug safety in drug development: bridging computational predictions for enhanced toxicity prediction”. Chemical Research in Toxicology6 (2024): 827-849.
32. Shaki F., et al. “The future and application of artificial intelligence in toxicology”. Asia Pacific Journal of Medical Toxicology1 (2024): 21-28.
33. Sinha K., et al. “A review on the recent applications of deep learning in predictive drug toxicological studies”. Chemical Research in Toxicology8 (2023): 1174-1205.
34. Jeong J and Choi J. “Artificial intelligence-based toxicity prediction of environmental chemicals: future directions for chemical management applications”. Environmental Science and Technology12 (2022): 7532-7543.
35. Cavasotto CN and Scardino V. “Machine learning toxicity prediction: latest advances by toxicity end point”. ACS Omega51 (2022): 47536-47546.
36. Korteling JE., et al. “Human-versus artificial intelligence”. Frontiers in Artificial Intelligence 4 (2021): 622364.
37. Jarrahi MH., et al. “Artificial intelligence, human intelligence and hybrid intelligence based on mutual augmentation”. Big Data and Society2 (2022).
38. Russell S. “Human-compatible artificial intelligence”. Human-like machine intelligence (2022).
39. Garg PK. “Overview of artificial intelligence”. Artificial intelligence (2021).
40. Jaboob A., et al. “Artificial intelligence: An overview”. Engineering Applications of Artificial Intelligence (2024).
41. Mishra S., et al. “Artificial intelligence-based technological advancements in clinical healthcare applications: A systematic review”. Revolutions in product design for healthcare: advances in product design and design methods for healthcare 1 (2022): 207-227.
42. Ahmad Z., et al. “Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review”. Diagnostic Pathology1 (2021): 24.
43. Sarker IH. “Machine learning: Algorithms, real-world applications and research directions”. SN Computer Science3 (2021): 160.
44. Moreno-Indias I., et al. “Statistical and machine learning techniques in human microbiome studies: contemporary challenges and solutions”. Frontiers in Microbiology 12 (2021): 635781.
45. An Q., et al. “A comprehensive review on machine learning in healthcare industry: classification, restrictions, opportunities and challenges”. Sensors 9 (2023): 4178.
46. Taye MM. “Understanding of machine learning with deep learning: architectures, workflow, applications and future directions”. Computers5 (2023): 91.
47. Chiang CW and Yin M. “You’d better stop! Understanding human reliance on machine learning models under covariate shift”. In Proceedings of the 13^th ACM Web Science Conference 2021 (2021): 120-129.
48. Khan P., et al. “Machine learning and deep learning approaches for brain disease diagnosis: principles and recent advances”. IEEE Access 9 (2021): 37622-37655.
49. Lima AA., et al. “A comprehensive survey on the detection, classification, and challenges of neurological disorders”. Biology 3 (2022): 469.
50. Ahsan MM., et al. “Machine-learning-based disease diagnosis: A comprehensive review”. Healthcare3 (2022): 541.
51. Gupta C., et al. “Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases”. Journal of Neurodevelopmental Disorders1 (2022): 28.
52. Surianarayanan C., et al. “Convergence of artificial intelligence and neuroscience towards the diagnosis of neurological disorders—a scoping review”. Sensors 6 (2023): 3062.
53. Limbu S., et al. “Predicting dose-range chemical toxicity using novel hybrid deep machine-learning method”. Toxics11 (2022): 706.
54. Lauriola I., et al. “An introduction to deep learning in natural language processing: Models, techniques, and tools”. Neurocomputing 470 (2022): 443-456.
55. Hao Y., et al. “Development of an interpretable machine learning model for neurotoxicity prediction of environmentally related compounds”. Environmental Science and Technology22 (2025): 11108-11120.
56. Gao Y., et al. “Integrating molecular fingerprints with machine learning for accurate neurotoxicity prediction: an observational study”. Advanced Technology in Neuroscience3 (2025): 109-115.
57. Bai C., et al. “Machine learning‐enabled drug‐induced toxicity prediction”. Advanced Science16 (2025): e2413405.
58. Tan H., et al. “Deep learning in environmental toxicology: current progress and open challenges”. ACS ES and T Water3 (2023): 805-819.
59. Xu M., et al. “In silico prediction of chemical aquatic toxicity by multiple machine learning and deep learning approaches”. Journal of Applied Toxicology11 (2022): 1766-1776.
60. Cui S., et al. “Advances and applications of machine learning and deep learning in environmental ecology and health”. Environmental Pollution 335 (2023): 122358.
61. Guo W., et al. “Review of machine learning and deep learning models for toxicity prediction”. Experimental Biology and Medicine21 (2023): 1952-1973.
62. Banerjee A and Roy K. “ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data”. Environmental Science: Processes and Impacts6 (2024): 991-1007.
63. Fan J., et al. “Prediction of chemical reproductive toxicity to aquatic species using a machine learning model: An application in an ecological risk assessment of the Yangtze River, China”. Science of The Total Environment 796 (2021): 148901.
64. Corradi MP., et al. “Natural language processing in toxicology: Delineating adverse outcome pathways and guiding the application of new approach methodologies”. Biomaterials and Biosystems 7 (2022): 100061.
65. Corradi M., et al. “The application of natural language processing for the extraction of mechanistic information in toxicology”. Frontiers in Toxicology 6 (2024): 1393662.
66. Shi H and Zhao Y. “Integration of advanced large language models into the construction of adverse outcome pathways: opportunities and challenges”. Environmental Science and Technology35 (2024): 15355-15358.
67. Corradi M., et al. “The application of natural language processing for the extraction of mechanistic information in toxicology”. Frontiers in Toxicology 6 (2024): 1393662.
68. Mostafa F and Chen M. “Computational models for predicting liver toxicity in the deep learning era”. Frontiers in Toxicology 5 (2024): 1340860.
69. Wahed SA and Wahed MA. “AI-deep learning framework for predicting neuropsychiatric outcomes following toxic effects of drugs on the brain”. Multidisciplinar (Montevideo) 3 (2025).
70. Kleinstreuer N and Hartung T. “Artificial intelligence (AI)—it's the end of the tox as we know it (and I feel fine)”. Archives of Toxicology3 (2024): 735-754.
71. Olker JH., et al. “The ECOTOXicology knowledgebase: A curated database of ecologically relevant toxicity tests to support environmental research and risk assessment”. Environmental Toxicology and Chemistry6 (2022): 1520-1539.
72. Ravichandran J., et al. “NeurotoxKb 1.0: Compilation, curation and exploration of a knowledgebase of environmental neurotoxicants specific to mammals”. Chemosphere 278 (2021): 130387.
73. Novo JP., et al. “Cellular and molecular mechanisms mediating methylmercury neurotoxicity and neuroinflammation”. International Journal of Molecular Sciences6 (2021): 3101.
74. Roberts SM., et al. “Principles of toxicology: environmental and industrial applications” (2022).
75. Shi XX., et al. “Unveiling toxicity profile for food risk components: a manually curated toxicological databank of food-relevant chemicals”. Critical Reviews in Food Science and Nutrition15 (2024): 5176-5191.
76. Shi XX., et al. “Toxicological data bank bridges the gap between environmental risk assessment and green organic chemical design in One Health world”. Green Chemistry6 (2023): 2170-2219.
77. Lee H., et al. “Integrated multi-omics analysis reveals the underlying molecular mechanism for developmental neurotoxicity of perfluorooctanesulfonic acid in zebrafish”. Environment International 157 (2021): 106802.
78. Pang X., et al. “NeuTox 2.0: A hybrid deep learning architecture for screening potential neurotoxicity of chemicals based on multimodal feature fusion”. Environment International 195 (2025): 109244.
79. Mortimer M., et al. “Omics approaches in toxicological studies”. In advances in toxicology and risk assessment of nanomaterials and emerging contaminants. Singapore: Springer Singapore (2022): 61-94.
80. Stoccoro A and Coppedè F. “Exposure to metals, pesticides, and air pollutants: focus on resulting DNA methylation changes in neurodegenerative diseases”. Biomolecules 11 (2024): 1366.
81. Rajvanshi S and Venkatesh R. “Neurotoxic effects of environmental pollutants and pharmacological neuroprotective strategies”. International Journal of Toxicology and Pharmacological Sciences1 (2025).
82. Chaudhry MA and Kazim E. “Artificial intelligence in education (AIEd): A high-level academic and industry note 2021”. AI and Ethics1 (2022): 157-165.
83. Yadav U and Shrawankar U. “Artificial intelligence across industries: A comprehensive review with a focus on education”. AI applications and strategies in teacher education (2025): 275-320.
84. Javaid M., et al. “Artificial intelligence applications for industry 4.0: A literature-based study”. Journal of Industrial Integration and Management1 (2022): 83-111.
85. Kraus N., et al. “Artificial intelligence in established of industry 4.0”. WSEAS Transactions on Business and Economics 19 (2022): 1884-1900.
86. Stadnicka D., et al. “Industrial needs in the fields of artificial intelligence, internet of things and edge computing”. Sensors12 (2022): 4501.
87. Perry LM., et al. “Patient-reported outcome dashboards within the electronic health record to support shared decision-making: protocol for co-design and clinical evaluation with patients with advanced cancer and chronic kidney disease”. JMIR Research Protocols9 (2022): e38461.
88. Wang W., et al. “Associations of semaglutide with first‐time diagnosis of Alzheimer's disease in patients with type 2 diabetes: target trial emulation using nationwide real‐world data in the US”. Alzheimer's and Dementia12 (2024): 8661-8672.
89. Leahy AB., et al. “CD19-targeted chimeric antigen receptor T-cell therapy for CNS relapsed or refractory acute lymphocytic leukaemia: a post-hoc analysis of pooled data from five clinical trials”. The Lancet Haematology10 (2021): e711-e722.
90. Qian ET., et al. “Cefepime vs piperacillin-tazobactam in adults hospitalized with acute infection: the ACORN randomized clinical trial”. Journal of the American Medical Association16 (2023): 1557-1567.
91. Gadaleta D., et al. “Prediction of the neurotoxic potential of chemicals based on modelling of molecular initiating events upstream of the adverse outcome pathways of (developmental) neurotoxicity”. International Journal of Molecular Sciences6 (2022): 3053.
92. Spînu N., et al. “Probabilistic modelling of developmental neurotoxicity based on a simplified adverse outcome pathway network”. Computational Toxicology 21 (2022): 100206.
93. Qin SJ., et al. “Neurotoxicity of fine and ultrafine particulate matter: A comprehensive review using a toxicity pathway-oriented adverse outcome pathway framework”. Science of The Total Environment 947 (2024): 174450.
94. Ahmadi M., et al. “Toxicity prediction of nanoparticles using machine learning approaches”. Toxicology 501 (2024): 153697.
95. Bilgi E and Karakus CO. “Machine learning-assisted prediction of the toxicity of silver nanoparticles: a meta-analysis”. Journal of Nanoparticle Research 25 (2023): 157.
96. Subramanian NA and Palaniappan A. “NanoTox: Development of a parsimonious in silico model for toxicity assessment of metal-oxide nanoparticles using physicochemical features”. ACS Omega17 (2021): 11729-11739.
97. Romano R., et al. “From modeling dose-response relationships to improved performance of decision-tree classifiers for predictive toxicology of nanomaterials”. Computational Toxicology 27 (2023): 100277.
98. Tang AS., et al. “Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations”. Nature Communications1 (2022): 675.
99. Gu M., et al. “Sepsis and cerebral dysfunction: BBB damage, neuroinflammation, oxidative stress, apoptosis and autophagy as key mediators and the potential therapeutic”. Neurotoxicity Research2 (2021): 489-503.