AI-Assisted Drug Discovery, Target Identification, and Precision Therapy for Neurological Disorders and Neuro-Oncology

Neurological disorders and neuro oncology impose a growing clinical and societal burden, while therapeutic development remains slow, costly, and uncertain. Major challenges include disease heterogeneity, limited human relevant models, blood brain barrier constraints, insufficient biomarkers, treatment resistance, and poor translation from computational or preclinical findings into clinical benefit. Recent advances in artificial intelligence, molecular representation learning, multimodal omics, single cell sequencing, spatial transcriptomics, medical imaging, and clinical data integration provide new opportunities to improve drug discovery and precision therapeutics. However, many current approaches still lack interpretability, robustness, biological validation, and clinical applicability. There is a strong need for multidisciplinary research that connects computational innovation with mechanistic understanding, target identification, candidate drug discovery, biomarker development, and individualized treatment strategies for neurological diseases and neuro oncology.This Research Topic aims to bring together studies that use artificial intelligence to advance drug discovery, target identification, and precision therapeutics for neurological disorders and neuro oncology. It will focus on how computational methods can help uncover disease mechanisms, identify therapeutic targets, screen or generate candidate compounds, predict drug properties, evaluate blood brain barrier permeability, support drug repurposing, and optimize patient specific treatment strategies. Particular attention will be given to research integrating molecular data, single cell and spatial omics, imaging, pathology, and clinical information to improve biological insight and translational relevance. The topic also seeks to highlight explainable, reproducible, and clinically meaningful artificial intelligence models that can support decision making in therapeutic discovery. By connecting artificial intelligence, neuroscience, neuro oncology, pharmacology, bioinformatics, and clinical medicine, this collection will promote innovative approaches for developing effective and precise treatments for neurological diseases and central nervous system tumors.We welcome original research articles, reviews, and perspectives from multidisciplinary fields, with emphasis on (but not limited to) the following topics:1. Applications of drug discovery in neurological diseases and neuro-oncology: virtual screening, ADMET modeling, blood-brain barrier permeability prediction, molecular generation, drug-drug interaction prediction, drug-target binding prediction, and candidate compound optimization for neurological tumors.2. Emerging Technologies: Research on Large Language Model (LLM) technology in neurology.3. Interpretability, Robustness, and Clinical Translation: Methods and insights to enhance the transparency, reliability, reproducibility, and clinical applicability of AI models in drug discovery for neurological diseases and neuro-oncology, facilitating the translation of computational discovery outcomes into therapeutic innovations and clinical practice.4. Multimodal Omics Data-Driven Precision Therapy Strategies: AI methods integrating single-cell RNA sequencing, spatial transcriptomics, radiomics, pathology, and clinical data to elucidate disease mechanisms, identify targets, detect biomarkers, stratify patients, and predict treatment responses in neurological diseases. Topic editors: Leyi Wei, Xiucai Ye Source: https://www.frontiersin.org/research-topics/82415/ai-assisted-drug-discovery-target-identification-and-precision-therapy-for-neurological-disorders-and-neuro-oncology