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Docking-Guided Discovery of Neuromodulatory Agents for Brain Cancer

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Monochura Saha, Shubham Yadav, Habiba Zeb and Ishaq N. Khan

Submitted: 23 September 2025 Reviewed: 08 October 2025 Published: 06 February 2026

DOI: 10.5772/intechopen.1013556

Molecular Docking in Biomedical Engineering and Computational Chemistry IntechOpen
Molecular Docking in Biomedical Engineering and Computational Che... Edited by Rohit Bhatia

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Molecular Docking in Biomedical Engineering and Computational Chemistry [Working Title]

Rohit Bhatia

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Abstract

High-grade gliomas exploit neural circuits to support growth, creating neuromodulatory vulnerabilities amenable to pharmacological targeting. This chapter presents a practical pipeline that centers on molecular docking and complementary in-silico methods to discover and prioritize neuromodulatory compounds for brain cancer. We summarize neuron-glioma signaling targets (glutamatergic/GABAergic receptors, synaptogenic proteins, ion channels), outline virtual screening and flexible docking approaches, and describe follow-up stability assessments with molecular dynamics (MD), artificial intelligence/machine learning (AI/ML) prioritization, and network-pharmacology mapping. Representative examples, such as perampanel, gabapentin, and valproic acid, illustrate opportunities and translational barriers, particularly blood–brain barrier penetration, protein flexibility, and scoring limitations. Finally, we propose experimental validation paths and delivery strategies, such as focused ultrasound and nanoparticle systems, to bridge computational predictions and clinical translation. Emphasizing rigorous docking methodology and integrated validation will accelerate the repurposing and discovery of neuromodulatory agents for brain cancer.

Keywords

  • molecular docking
  • cancer neuroscience
  • neuron–glioma synapse
  • neuromodulatory therapy
  • drug repurposing
  • blood–brain barrier
  • focused ultrasound
  • nanoparticle delivery
  • molecular dynamics
  • AI/ML-driven drug discovery
  • synaptogenic proteins
  • ion channels

1. Introduction

Brain tumors, particularly high-grade gliomas such as glioblastoma multiforme (GBM), present a formidable challenge in oncology. These neoplasms are characterized by aggressive growth, highly infiltrative behavior, and a dismal prognosis. Despite aggressive multimodal therapy, patients with glioblastoma have a median survival of approximately 15 months (versus roughly 3–4 months without treatment) [1]. The five-year survival rate remains poor, at about 4.7% [2]. The inherent heterogeneity of these tumors, manifesting both across different patients (inter-tumoral) and within a single tumor (intra-tumoral), significantly contributes to therapeutic resistance and frequent recurrence, undermining the efficacy of current treatment paradigms [3]. Conventional therapeutic approaches for brain tumors typically involve maximal safe surgical resection, followed by radiation therapy and chemotherapy, often utilizing agents like temozolomide [1]. However, these established modalities frequently encounter limitations, including significant side effects and the tumor’s capacity to develop resistance, which collectively restrict their long-term effectiveness [4]. The persistent challenge of brain cancer, despite multimodal conventional therapies, underscores a critical need for innovative therapeutic strategies. This situation necessitates a deeper understanding of the unique biological vulnerabilities of brain tumors, especially their intricate interactions with the neural microenvironment, and a corresponding shift toward targeting these newly identified vulnerabilities.

In response to these persistent challenges, neuromodulation is emerging as a promising frontier in oncology, extending its application beyond traditional roles in pain management and neurological disorders [5]. Neuromodulation, broadly defined, involves regulating nervous activity either by controlling the physiological levels of neurotransmitters or through direct stimulation of brain circuits [6]. The recognition that brain tumors actively exploit and integrate into existing neural circuits represents a groundbreaking shift in neuro-oncology. This perspective moves beyond a purely cell-autonomous view of cancer, acknowledging the profound influence of the nervous system on tumor progression [7]. This “cancer neuroscience” paradigm opens entirely new therapeutic avenues, suggesting that modulating these co-opted neural pathways could directly impede tumor growth and survival. Pharmacological neuromodulation, a key component of this evolving field, specifically employs drug molecules to target components of neural pathways that brain tumors co-opt for their proliferation and survival [7]. Unlike the broad cytotoxic effects of conventional chemotherapy, these agents aim for more precise interactions, thereby potentially reducing systemic side effects [8]. This approach signifies a move from merely eliminating cancer cells to a more nuanced strategy of “reprogramming” the tumor’s interaction with its environment. Understanding and disrupting the tumor’s “communication” with neurons is now considered as vital as direct cytotoxicity.

1.1 Molecular docking and its role in neuromodulatory drug discovery

Molecular docking plays a pivotal role in this specialized area of drug discovery. It is a computational technique that predicts the optimal binding orientation and affinity of a small molecule (ligand) to a protein receptor, offering atomistic insights into molecular recognition [9]. This method is a cost- and time-efficient procedure for drug design, particularly valuable for screening vast chemical libraries [10]. For central nervous system (CNS) cancers, molecular docking is indispensable for identifying novel therapeutic targets, evaluating potential drug candidates, and repurposing existing drugs by predicting their interactions with brain-specific targets involved in neuromodulation [11]. The increasing reliance on molecular docking and other in silico methods reflects a broader trend in drug discovery, driven by the complexity and prohibitive cost of traditional experimental methods. For brain cancer, where experimental models are particularly challenging and blood–brain barrier (BBB) penetration is a critical factor, computational tools become an essential first step for initial lead identification and optimization [12–15]. The content of this chapter is summarized in Figure 1.

Figure 1.

Integrated translational pipeline from neuron-glioma biology to clinical translation. This schematic organizes the chapter’s proposed workflow into four interconnected domains: (1) Biology and targets, which enumerates the neuron-glioma processes that define druggable vulnerabilities, that is, glutamatergic and GABAergic signaling, synaptogenic proteins, ion channels, neurotrophic factors, and the tumor microenvironment; (2) computational discovery, a linear discovery engine that progresses from curated databases, virtual screening/docking, molecular dynamics (MD) simulations, AI/ML prioritization, network pharmacology and systems biology to produce ranked ligands, predicted binding, and candidate target networks; (3) delivery and preclinical validation, which lists translational enablers and test platforms, that is, nano-formulations, extracellular-vesicle/exosome systems, and focused-ultrasound delivery, coupled with in vitro and in vivo evaluation; and (4) clinical translation, which denotes safety, efficacy assessment and advanced clinical trials. An iterative “loop-back” arrow explicitly links clinical and preclinical readouts back into the computational discovery pipeline for model refinement and re-prioritization. Together, the figure visualizes a closed, multidisciplinary pathway for converting neuron-glioma mechanistic insight into prioritized, deliverable neuromodulatory therapies.

Molecular docking is a cornerstone technique in structure-based drug discovery that predicts ligand binding modes and estimates relative affinities by sampling poses within a protein binding site and scoring intermolecular interactions. Docking enables rapid, cost-effective virtual screening of large libraries to prioritize compounds for downstream experimental testing and is particularly valuable when seeking repurposed neuromodulatory drugs with known CNS exposure profiles. Several widely used docking platforms (e.g., AutoDock/AutoDock Vina, Glide, MOE) support large-scale screens and have been applied previously to CNS targets and glioma-relevant proteins [9, 16–18]. Accurate docking for brain-relevant targets requires explicit attention to protein flexibility, membrane environment (for ion channels), and BBB constraints. Accordingly, best practice integrates flexible docking with MD to sample receptor conformations and validate pose stability and uses rescoring methods (MM-GBSA or other binding free energy calculations) and AI/ML-based filters to reduce false positives. Combining docking outputs with ADME and BBB-penetration predictions, and with network pharmacology mapping, substantially improves the translational relevance of in silico hits by prioritizing compounds that not only bind strongly in silico but are likely deliverable to tumor sites [12–15, 19–22].

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2. Neuromodulation in brain cancer: Mechanisms and targets

Neuromodulation drugs for brain cancer represent an emerging therapeutic paradigm that targets not only the tumor cells themselves but also the complex neurobiological networks that support their growth, invasion, and resistance to treatment [23–25]. Recent research has revealed that brain tumors, particularly gliomas, do not exist in isolation; they actively engage in bidirectional communication with surrounding neurons, astrocytes, and immune cells, co-opting neurotransmitter systems such as glutamatergic and GABAergic signaling to promote proliferation and evade immune surveillance [26]. Neuromodulatory agents, ranging from ion channel modulators and synaptic transmission regulators to drugs that alter neuroinflammatory signaling, can strategically interfere with these tumor-neuron interactions. By disrupting aberrant neuronal activity that fuels tumor progression, these drugs can attenuate cancer-related neuroplasticity, reduce tumor invasiveness, and potentially enhance the efficacy of conventional treatments such as chemotherapy, radiotherapy, and immunotherapy. Importantly, neuromodulation offers the possibility of selective targeting, as many of these pathways are aberrantly activated in tumor-associated neural circuits but not in healthy brain tissue, reducing off-target toxicity. Beyond direct antitumor effects, neuromodulation drugs may also preserve or restore neurological function by counteracting tumor-induced network disruptions, improving patient quality of life. This dual benefit, which simultaneously weakens the tumor and protects brain function, positions neuromodulatory pharmacology as a compelling and potentially transformative addition to the neuro-oncology arsenal [26, 27]. Given the importance of neuromodulation drugs in cancer therapy, several strategies have been applied, including both non-pharmacological and pharmacological approaches.

2.1 Non-pharmacological neuromodulation approaches

Non-pharmacological neuromodulation techniques offer direct means to influence brain activity and, by extension, the tumor microenvironment (TME). These methods include transcranial magnetic/electrical stimulation (TMS/TES), vagus nerve stimulation (VNS), transcranial direct current stimulation (tDCS), deep brain stimulation (DBS), and focused ultrasound (FUS) [28–30]. (see Figure 2).

Figure 2.

Neuromodulation strategies for brain cancer: pharmacological and non-pharmacological approaches. The schematic illustrates two complementary modalities of neuromodulation currently being explored in the context of brain cancer therapy. Non-pharmacological neuromodulation encompasses device-based interventions including deep brain stimulation (DBS), vagus nerve stimulation (VNS), rhythmic transcranial magnetic stimulation (rTMS), and transcranial direct current stimulation (tDCS), which modulate neuronal excitability and network dynamics through electrical or magnetic fields. Pharmacological neuromodulation targets receptor and ion channel systems that regulate synaptic and extrasynaptic signaling in tumor–neuron interactions. These include ionotropic and metabotropic receptors, gabaergic receptors, voltage-gated ion channels (calcium, sodium, potassium), and neuromodulatory transmitters such as acetylcholine, dopamine, and serotonin. Together, these approaches highlight the dual therapeutic landscape where circuit-level modulation and molecular receptor targeting converge to reprogram aberrant neurophysiological signaling that drives tumor progression.

2.2 Pharmacological neuromodulation: Targeting neural pathways in gliomagenesis

The understanding that brain tumors actively integrate into and exploit neural circuits has led to a focused effort on pharmacological neuromodulation [24, 31, 32]. This involves designing or repurposing drugs that interfere with these tumor-promoting neural interactions. Brain tumor cells, particularly gliomas and metastatic cells, have been found to express functional receptors for various neurotransmitters and to form “tumor synaptogenesis,” integrating into existing neural networks to fuel their growth and survival [7].

2.2.1 Glutamatergic signaling (ionotropic and metabotropic receptors)

Glutamate, the most abundant excitatory neurotransmitter in the CNS, plays a critical role in brain tumor growth and proliferation [7]. This phenomenon, where glioma and metastatic cells exploit glutamatergic pathways, highlights a fundamental mechanism by which tumors hijack the brain’s own communication systems for their benefit [7].

Ionotropic receptors (NMDA, AMPA, Kainate): AMPA receptors (AMPARs) are highly expressed on glioma cells. While their activation can sometimes trigger apoptosis, malignant glioma cells often release glutamate into the extracellular space, which then activates calcium-permeable AMPARs and oncogenic pathways like Akt, ultimately promoting glioma cell growth and migration [7]. NMDAR activation on GBM cells similarly promotes cell survival and migration, with upregulation observed in brain metastatic samples [7]. Antagonizing NMDARs can enhance radiosensitivity by impairing DNA double-strand break repair [7].

Metabotropic receptors (mGluRs): These G-protein-coupled receptors regulate intracellular signaling pathways. Group I mGluRs, such as mGluR3, are predominantly excitatory, and increased mGluR3 expression correlates with higher malignancy and fatality rates in GBM [7]. Inhibition of mGluR3 has been shown to sensitize GBM cells to Temozolomide, leading to reduced tumor growth [33]. Conversely, mGluR4 appears to suppress tumor growth and induce apoptosis [34]. The dual and sometimes contradictory roles of neurotransmitter receptors, such as AMPARs triggering apoptosis in some contexts while promoting growth in others, or the debated role of GABA, underscore the inherent complexity of tumor biology. This suggests that therapeutic targeting must be highly specific, carefully considering receptor subtypes, the cellular context (tumor vs. healthy cells), and the dynamic TME. A simplistic approach, such as broadly blocking all glutamate receptors, might not be effective or even safe.

2.2.2 Gabaergic signaling (GABAA and GABAB receptors)

GABA, the primary inhibitory neurotransmitter in the CNS, also exhibits complex and sometimes paradoxical effects on brain tumors [7].

GABAA receptors: Activated GABAA receptors have been observed to curb glioma cell proliferation and induce apoptosis in neuroblastoma cells [7]. However, endogenous GABAA receptor signaling can also sustain the quiescent state of a subset of tumor-initiating glioma stem-like cells, which are known to contribute to tumor recurrence and therapy resistance [7]. Furthermore, overexpression of glutamate decarboxylase 1 (GAD1), a key enzyme in GABA synthesis, has been linked to facilitated brain metastasis [7]. Paradoxically, tumor-promoting GABAergic neuron-to-glioma synapses, mediated by GABAA receptors, have been identified in diffuse midline gliomas (DMGs). These synaptic inputs depolarize DMG cells due to altered intracellular chloride concentrations, ultimately driving DMG cell proliferation in vivo [7].

GABAB receptors: Increased expression of GABAB receptors has been detected in various cancer cells. Agonists of GABAB receptors, such as baclofen, have shown promise in inhibiting tumor growth in rat models by suppressing the proliferation and migration of cancer cells [35].

GABA metabolism: Human breast cancer cells have been observed to proliferate and metastasize to the brain by catabolizing GABA into succinate, effectively utilizing GABA as an onco-metabolite. Inhibiting GABA synthesis (e.g., by targeting GAD67) or its utilization (e.g., by targeting GABA transaminase, ABAT) can attenuate cancer cell proliferation and migration [36].

Other neurotransmitters: Beyond glutamate and GABA, a wide array of neurotransmitters, including acetylcholine, dopamine, serotonin, norepinephrine, neuropeptides (e.g., substance P, neuropeptide Y), adenosine triphosphate (ATP), and gases like nitric oxide, have been implicated in various aspects of cancer biology, suggesting broader interactions between neurotransmitter systems and cancer progression. Notably, acetylcholinergic neurons have been shown to drive glioblastoma invasion [37].

2.3 Ion channels as therapeutic targets

Ion channels, which regulate the flux of ions such as sodium (Na+), potassium (K+), calcium (Ca2+), and chloride (Cl−) across cell membranes, are crucial for fundamental cellular physiology. They are increasingly recognized for their significant roles in tumor pathophysiology, influencing processes such as gene expression, cell migration, proliferation, glioma-related seizures, chemotherapy sensitivity, and tumor metabolism [38]. Their accessibility on the cell surface makes them particularly attractive “druggable” targets for neuromodulatory therapeutics [39].

Calcium channels (e.g., TRP channels, VGCCs): Higher expression of Ca2+ channels on glioma cells leads to increased Ca2+ influx, which, in turn, promotes Ca2+-dependent proliferative signaling pathways [38]. Transient Receptor Potential Melastatin 7 (TRPM7) activates the JAK2/STAT3 and/or Notch signaling pathways, inducing cell proliferation and migration; its suppression significantly reduces these effects [38]. Midazolam, a benzodiazepine capable of crossing the BBB, has been shown to inhibit TRPM7 currents and calcium influx, leading to G0/G1 cell cycle arrest and decreased glioblastoma cell proliferation [38]. T-type Ca2 + channels (Cav3.2) are highly expressed in glioblastoma, and their pharmacological blockade inhibits tumor growth and enhances the effects of temozolomide [38].

Potassium channels (e.g., hEAG1, hERG, Kv1.5, Kv2.1): These channels are often overexpressed in GBM and are involved in signaling pathways that promote proliferation and inhibit apoptosis [38]. hERG blockers, for instance, have demonstrated therapeutic benefit in xenograft models and in glioblastoma patients [38]. KCND2, which encodes the voltage-gated K+ channel KV4.2, has been implicated in enhancing neuronal excitability in epileptic GBM, suggesting it as a potential therapeutic target [39]. Furthermore, genes encoding ion transport proteins like GJB2 and SCN9A, part of the “ion permeome,” show prominent expression in neoplastic cells and are associated with poor prognosis in glioblastoma; their knockdown impairs cell viability and tumor sphere formation in vitro and extends survival in xenograft models [40].

Chloride and sodium channels: Volume-regulated anion channel (VRAC) and Anoctamin-1 (ANO1), a Ca2+-activated Cl⁻ channel, are involved in glioma cell proliferation [38]. Chloride Intracellular Channel-1 (CLIC1) is overexpressed in GBM, and its expression is associated with a worse prognosis, promoting proliferation [38]. Additionally, ASIC1 and Epithelial Sodium Channel (ENaC) play roles in cell migration and proliferation [38]. The widespread dysregulation of ion channels in brain cancer and their accessibility on the cell surface make them highly attractive “druggable” targets for neuromodulatory therapeutics. Molecular docking is instrumental in identifying and optimizing modulators for these complex membrane proteins.

2.4 Neuron-glioma synapses: A novel therapeutic frontier

Recent groundbreaking discoveries have confirmed the existence of direct synaptic connections between neurons and brain tumor cells, fundamentally altering the understanding of gliomas and brain metastases [24]. These neuron-glioma synapses are not merely incidental but actively drive tumor growth and invasion, particularly within the tumor infiltration zone [41]. Neurons contribute to tumor progression through both paracrine mechanisms, such as the secretion of neuroligin-3, and direct electrochemical mechanisms, notably through glutamatergic AMPA receptors [23]. Tumor cells also form extensive multicellular networks via tumor microtubules (TMs), which facilitate the distribution of small molecules like calcium, thereby promoting tumor cell survival [23]. Disturbingly, radiotherapy has been shown to enhance neuron-tumor connectivity by increasing neuronal activity, suggesting that this crosstalk contributes to therapeutic resistance [37].

Thrombospondin-1 and synaptogenesis: Thrombospondin-1 (TSP-1), a protein involved in neural circuit development and remodeling, has been identified as a key mediator of interactions between neurons and tumor cells that promote glioma growth [42]. This synaptogenic protein facilitates the functional integration of the tumor into the neural circuitry [42]. Gabapentin, a widely used anti-seizure and pain medication, has been shown to inhibit TSP-1, thereby disrupting neuronal synaptogenesis and neuronal activity-dependent glioblastoma proliferation [42]. Retrospective studies have linked gabapentin use to a significant survival benefit (four to six months longer median overall survival) in glioblastoma patients, a benefit potentially mediated through a reduction in serum TSP-1 levels [42]. The identification of specific molecular mediators like TSP-1 in neuron-glioma synaptogenesis provides a concrete, actionable target for pharmacological intervention. The observed survival benefit with gabapentin, linked to TSP-1 reduction, suggests that disrupting this neural integration is a viable anti-cancer strategy, even with existing, repurposed drugs.

Neurotrophic factors and their dual role: Neurotrophins, such as brain-derived neurotrophic factor (BDNF), are vital for the proper development and survival of neurons in the CNS. BDNF binds to its receptor, TrkB, initiating signaling cascades (e.g., via the PI3K/Akt pathway) that promote pro-migratory, anti-apoptotic, and pro-survival proteins in cells [43]. Historically, overexpression of BDNF/TrkB has been considered oncogenic, contributing to the progression of aggressive neuroblastoma and breast cancer stem cells [43]. However, recent evidence suggests a more complex, dual role for these factors. BDNF overexpression in the hypothalamus, for instance, has been shown to possess immune-augmenting properties, leading to an increased anti-tumor immune response and reducing resistance to chemotherapeutic agents in vivo [43]. This highlights that the effect of neurotrophic factors can be tissue-dependent and significantly influenced by the specific microenvironment [43]. The dual role of neurotrophic factors like BDNF presents a critical nuance in targeting neural pathways. What might be oncogenic in one context (e.g., direct tumor cell signaling) could be anti-tumorigenic in another (e.g., modulating immune response). This implies that therapeutic strategies must carefully consider the specific cellular context and the broader systemic effects, not just the direct impact on tumor cells.

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3. Molecular docking and computational drug discovery in brain cancer neuromodulation

Molecular docking is a cornerstone of modern computer-aided drug design (CADD), offering a powerful computational framework to accelerate the discovery and optimization of drug candidates, particularly for complex diseases like brain cancer.

3.1 Principles and applications of molecular docking

Molecular docking is a computational procedure designed to predict the optimal, lowest-energy binding conformations of a small molecule (ligand) within the binding site of a biological macromolecule, typically a protein receptor, and to estimate the binding affinity between them [16]. This process involves quantifying various intermolecular interactions, including shape and electrostatic complementarities, van der Waals forces, and hydrogen bond formation [17]. The sum of these interactions is approximated by a docking score, which serves as an indicator of the potential for binding [17].

The applications of molecular docking are diverse and strategically important in drug discovery:

Predicting ligand–target interactions and binding affinities: Molecular docking provides atomistic insights into precisely how drug molecules interact with their intended biological targets [18]. This detailed understanding is crucial for rational drug design, allowing researchers to predict which compounds are most likely to bind effectively and with high affinity.

Virtual screening for novel compounds: One of the most impactful applications is the rapid in silico screening of vast chemical libraries to identify potential drug candidates [11]. This significantly reduces the time and financial investment required for traditional high-throughput experimental screening, enabling a much more efficient initial filtering of compounds.

Repurposing existing drugs: Molecular docking is instrumental in identifying new therapeutic uses for approved drugs by predicting their interactions with novel targets [44]. This approach is particularly valuable for brain cancer, given the high failure rate of new CNS drug development due to BBB penetration issues. Repurposing existing drugs with known safety profiles and some BBB permeability, such as gabapentin and perampanel, can significantly accelerate clinical translation [42]. The ability to leverage the established safety and pharmacokinetic profiles of existing medications offers a de-risked and faster pathway to new brain cancer treatments.

3.2 Key molecular docking software and algorithms

The field of molecular docking has seen the development of over 60 different tools and programs for both academic and commercial use over the past two decades [17]. These tools employ various strategies for ligand placement, including incremental construction, shape-based algorithms, genetic algorithms, systematic search techniques, and Monte Carlo simulations [17].

Commonly utilized software in the field includes AutoDock (and its optimized version, AutoDock Vina), FlexX, Surflex, GOLD, ICM, Glide, MOE-Dock, and LeDock [18]. For instance, AutoDock 4.2 has been extensively used in studies investigating the interactions of Temozolomide with glioblastoma-related proteins [45, 46]. MOE software has been employed to confirm the high binding affinity of compounds like TIZ to targets such as CDK1 in glioblastoma [47]. PyRx is another common tool used for docking phytochemicals [48].

A critical consideration for CNS targets is accounting for protein dynamics and flexibility. While many docking programs simplify the process by treating the protein receptor as rigid or allowing only limited flexibility to residues near the active site, this can lead to inaccuracies [18]. Proteins are dynamic entities, fluctuating through various conformations, and ligands often interact with specific, transient structural states [19]. This “rigid receptor” assumption is a significant limitation, especially for dynamic CNS targets like ion channels and G-protein coupled receptors (GPCRs), which are known for their substantial conformational changes upon ligand binding [49]. Flexible docking methods, which consider multiple possible conformations of both the ligand and the receptor, are therefore more accurate for predicting binding modes and affinities, though they significantly increase computational complexity. The necessity for flexible docking and MD simulations underscores that simplistic models are often insufficient for accurate predictions in the complex and dynamic brain environment.

3.3 Integration with advanced computational methods

To enhance the understanding of molecular docking’s role in neuromodulatory drug discovery for brain cancer, its integration with complementary computational methods is essential for addressing limitations like protein flexibility and binding accuracy. As illustrated in Figure 3, molecular docking acts as a central node, synergizing with artificial intelligence (AI) and statistical methods for pre-docking screening (e.g., protein conformation selection and scoring function improvement) and post-docking pose rescoring; binding free energy methods for post-docking pose rescoring; MD simulations for pre-docking identification of representative conformations and post-docking assessments of pose refinement and ligand-target complex stability; and ligand-based approaches for pre-docking protein conformation selection and post-docking pose selection/rescoring. This multifaceted integration accelerates virtual screening and optimization of neuromodulatory agents targeting glioma-neuron interactions, such as glutamate or GABA receptors, while improving predictions for BBB-penetrant compounds [20–22].

Figure 3.

Molecular docking’s role in neuromodulatory drug discovery for brain cancer and its integration with complementary computational methods. Molecular docking acts as a central node, synergizing with artificial intelligence (AI) and statistical methods for pre-docking screening (e.g., Protein conformation selection and scoring function improvement) and post-docking pose rescoring; binding free energy methods for post-docking pose rescoring; molecular dynamics (MD) simulations for pre-docking identification of representative conformations and post-docking assessments of pose refinement and ligand–target complex stability; and ligand-based approaches for pre-docking protein conformation selection and post-docking pose selection/rescoring. This multifaceted integration accelerates virtual screening and optimization of neuromodulatory agents targeting glioma–neuron interactions, such as glutamate or GABA receptors, while improving predictions for blood-brain barrier-penetrant compounds, which would ultimately result in accelerated drug discovery and reducing the overall cost.

AI/Machine learning in drug discovery: AI and Machine Learning (ML) are rapidly gaining prominence for their ability to process vast chemical datasets, identify complex patterns, and predict compounds with high therapeutic potential [44]. These technologies can derive predictive characteristics from diverse data sources, including imaging and genomic sequences, and model tumor development dynamics and drug-tumor interactions [50]. AI-powered platforms, such as DrugBank, are already leveraging AI to uncover novel connections and insights in drug–target interactions, streamlining the discovery process [51].

MD simulations: MD simulations are theoretical tools that explore the configurations and dynamic behaviors of molecules over time, providing atomic-level insights into drug mechanisms of action [11]. They are essential for evaluating the flexibility and stability of protein-ligand complexes, thereby overcoming the inherent rigid-receptor limitation of basic docking approaches [17]. MD simulations can reveal how a drug interacts with a dynamic target, providing a more realistic picture of binding.

Network pharmacology and systems biology approaches: These methodologies analyze molecular and genetic mechanisms at a systems level, identifying core driving genes and abnormal regulation pathways within complex biological networks [52]. They are crucial for understanding intricate protein–protein interactions and broader signaling pathways, providing a holistic view of disease mechanisms and drug effects [52]. The convergence of molecular docking with MD simulations, AI/ML, and network pharmacology is critical for tackling the immense complexity of brain tumor biology, particularly the dynamic neuron–glioma interactions and the heterogeneous TME. This integrated approach allows researchers to move beyond simple ligand–receptor binding to understand complex biological systems and the broader impact of drugs.

3.4 Relevant drug–target interaction databases

High-quality, integrated databases form the backbone of modern computational drug discovery. Their ability to link drugs to targets, diseases, and even genetic features of cancer cells is fundamental for identifying neuromodulatory compounds and validating in silico predictions.

DrugBank: DrugBank is a comprehensive online database that provides reliable and up-to-date information on drugs and their targets. It offers real-time insights, increasingly powered by AI, and integrates essential external datasets. This resource is widely utilized by scientists, researchers, and biotech teams to discover hidden drug–target interactions and explore extensive datasets. DrugBank lists “Brain (Nervous System) Cancers” as a relevant indication, allowing for targeted searches [53].

Cancer therapeutics response portal: The cancer therapeutics response portal (CTRP) is a valuable resource that links genetic, lineage, and other cellular features of cancer cell lines to small-molecule sensitivity [54]. It contains an “Informer Set” of 481 compounds rigorously tested across 860 cancer cell lines, providing annotations for small molecules by protein target and for cell lines by mutation and lineage [54]. CTRP serves as a dynamic resource for generating insights into small-molecule action mechanisms and formulating novel therapeutic hypotheses. The existence and quality of these integrated databases are crucial enablers for the entire computational drug discovery pipeline, allowing for data-driven hypothesis generation and validation, and addressing the critical need for robust datasets in drug discovery [19].

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4. Promising neuromodulatory drug molecules and molecular docking insights

The application of molecular docking and related computational methods has facilitated the identification and characterization of several promising neuromodulatory drug molecules for brain cancer therapeutics. These agents often target neurotransmitter receptors, ion channels, or other neural-related proteins that are aberrantly involved in tumor progression.

4.1 Neurotransmitter-based modulators

Brain tumors can hijack neurotransmitter systems to promote their growth and survival, making neurotransmitter receptors attractive therapeutic targets. Computational approaches, including molecular docking, have helped identify several neurotransmitter-targeted modulators with potential anti-tumor effects. Below, we examine key examples targeting glutamate and GABA pathways:

4.1.1 Glutamate receptor modulators

Perampanel (AMPA antagonist): Perampanel (PER) is a highly selective, non-competitive antagonist of glutamate α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors [55]. While primarily approved for treating seizures, including brain tumor-related epilepsy (BTRE), PER has demonstrated promising antitumor effects in in vitro studies. It inhibits glioblastoma cell proliferation, attenuates glucose uptake, and reduces extracellular glutamate levels [55]. PER also exhibits good BBB penetration, a crucial property for CNS drugs [55]. However, despite encouraging in vitro results, its anti-tumor effects have not been consistently confirmed in preclinical studies, and clinical data, specifically on its impact on tumor progression, remain scarce. The contrast between in vitro efficacy and limited in vivo or clinical success for many glutamate antagonists strongly underscores the persistent challenges of BBB penetration. This highlights that successful neuromodulatory drugs for brain cancer must either possess inherent BBB permeability (like perampanel or gabapentin) or be delivered via BBB-disrupting technologies (like FUS) [55].

Gabapentin (α2δ-1/TSP-1 inhibition): Gabapentin, a widely used anti-seizure and neuropathic pain medication, exerts its effects by decreasing the activity of the α2δ-1 protein [56]. Crucially, α2δ-1 has been identified as the neuronal receptor for TSP-1 [56]. TSP-1 is a synaptogenic protein that actively promotes the functional integration of tumors into neural circuitry, thereby fueling glioma growth [42]. By antagonizing TSP-1 binding to α2δ-1, gabapentin effectively inhibits excitatory synapse formation [56]. Retrospective studies have linked gabapentin use to a significant survival benefit (an average of four to six months longer median overall survival) in glioblastoma patients, a benefit potentially mediated through a reduction in serum TSP-1 levels [46]. This promising finding warrants further prospective clinical trials to validate its therapeutic potential. The identification of specific molecular mediators like TSP-1 in neuron-glioma synaptogenesis provides a concrete, actionable target for pharmacological intervention. The observed survival benefit with gabapentin, linked to TSP-1 reduction, suggests that disrupting this neural integration is a viable anti-cancer strategy, even with existing, repurposed drugs.

Other glutamate antagonists: Other glutamate antagonists, such as dizocilpine (an NMDA antagonist) and GYKI52466 (an AMPA antagonist), have demonstrated antiproliferative effects in various human tumor cell lines, including astrocytoma. These effects are attributed to decreased cell division and increased cell death [57]. However, similar to other CNS-targeted agents, their poor efficacy in penetrating the BBB has significantly limited their clinical application for brain tumors [57]. Fluoxetine, a selective serotonin reuptake inhibitor (SSRI), also exhibited calcium-dependent cytotoxicity in glioma cells, requiring transmembrane calcium influx [58]. Talampanel, another AMPA receptor antagonist, showed mixed results in clinical trials, further emphasizing the challenges of translating in vitro efficacy to consistent clinical benefit [55].

4.1.2 GABA receptor modulators

GABAA and GABAB receptor agonists/antagonists: As discussed previously, activation of GABAA receptors has been shown to curb glioma cell proliferation and induce apoptosis in neuroblastoma cells [7]. Conversely, GABAB receptor agonists like baclofen have demonstrated promise in inhibiting tumor growth by suppressing proliferation and migration [59]. Molecular docking studies have been performed for GABAA receptor modulators, such as pyrazoloquinolinones (PQs), providing insights into their binding modes and interactions with specific receptor subunits [60].

Valproic acid (VPA): Valproic acid (VPA) is an antiepileptic drug frequently used in glioblastoma patients for seizure control [82]. Beyond its anticonvulsive properties, VPA is also a histone deacetylase inhibitor (HDACi), primarily targeting Class I and IIa HDACs [61]. Molecular docking and dynamics studies have elucidated VPA’s binding to the catalytic site of various HDACs, providing structural and energetic insights into its inhibitory mechanism [62]. VPA has been shown to sensitize glioma cells to temozolomide by upregulating solute carrier (SLC) transporters and promoting the reexpression of tumor suppressor genes [61]. Furthermore, VPA reduces cell proliferation, induces cell cycle arrest, differentiation, and apoptosis, partly by increasing the production of reactive oxygen species (ROS) [61]. Valproic acid exemplifies a repurposed drug with dual mechanisms relevant to brain cancer: direct neuromodulation (anti-epileptic effects) and epigenetic modulation (HDAC inhibition). This highlights that drugs may exert anti-cancer effects through multiple, interconnected pathways, which can be elucidated and optimized through molecular docking and systems-level analysis.

4.2 Ion channel modulators

Ion channels are a significant class of drug targets, with approximately 10%–20% of small-molecule drugs targeting them [63]. Their widespread dysregulation in brain cancer and their accessibility on the cell surface make them highly attractive targets for neuromodulatory therapeutics.

4.2.1 Specific examples and mechanisms

TRPM7 inhibitors: TRPM7 is a calcium-permeable cation channel linked to glioma cell survival and proliferation. Inhibiting TRPM7 can stunt tumor growth. For example, the anesthetic midazolam was found to acutely block TRPM7 channel activity in glioblastoma cells. Even a few seconds of midazolam exposure suppressed TRPM7 currents and downstream Ca2⁺ influx, while longer exposures reduced cell proliferation. This suggests TRPM7 channel blockade (by midazolam or other agents) can induce calcium starvation and growth arrest in glioma cells. Carvacrol and other TRPM7 modulators have similarly shown anti-glioma effects in preclinical models. Targeting TRPM7, which also interacts with signaling pathways (Notch, PI3K/mTOR), represents a potentially novel anti-glioma strategy that is currently under investigation.

T-type Ca2+ channel blockers: T-type calcium channels (Cav3.x) help regulate cell cycle transitions. Mibefradil, a T-type Ca2⁺ channel blocker formerly used for hypertension, was repurposed for glioma based on preclinical efficacy. Mibefradil inhibited glioblastoma growth and notably sensitized tumors to TMZ in both cell culture as well as rodent models. In glioma stem cell-derived xenografts, oral mibefradil slowed tumor progression and extended survival when combined with TMZ. Furthermore, a Phase I/II trial (NOA-08/Persurge) tested sequential mibefradil + TMZ in recurrent GBM, demonstrating safety and some pharmacodynamic effects, though further trials are still needed to establish efficacy. T-channel blockers prevent the Ca2⁺ influx needed for G1/S cell-cycle progression, thereby arresting tumor cells. This mechanism, coupled with synergy with chemotherapy, makes mibefradil and newer Cav3 inhibitors potential candidates.

hERG potassium channel blockers: The hERG (Kv11.1) potassium channel, better known for its role in cardiac repolarization, is also expressed in gliomas. High hERG expression in GBM correlates with poorer prognosis. Some clinically approved drugs incidentally block hERG channels (e.g., certain anti-arrhythmics, antipsychotics). Retrospective analyses suggest that GBM patients on hERG-blocking drugs had improved survival. Specifically, patient populations whose tumors highly expressed hERG and those receiving one or more hERG blockers lived significantly longer. Further, hERG channel blockers showed anti-glioma effects in xenograft studies, potentially by inducing apoptosis or by sensitizing cells to therapy. Despite this, caution is needed owing to the risk of cardiac side effects (QT prolongation) with hERG blockade, and as such, efforts have now focused on non-torsadogenic hERG inhibitors or localized delivery to reduce the systemic risks. Nonetheless, the data identify hERG as a functional player in glioma cell viability and a possible therapeutic target when carefully managed.

Gap junctions and sodium channels: Beyond classical ion channels, glioma cells also exploit channels involved in cell–cell communication and excitability. A recent large-scale pan-cancer analysis identified GJB2 (connexin 26, a gap junction protein) and SCN9A (Nav1.7, a voltage-gated sodium channel) as key pro-tumor ion transport genes in GBM. It was found that knocking down GJB2 or SCN9A in patient-derived GBM cells had a drastic effect on cell viability, and it impaired the formation of tumor spheres (a measure of self-renewal). These results suggest that electrical coupling (via gap junctions) and sodium current activity support glioma cell survival and stemness. While no specific drugs targeting connexin 26 or Nav1 [5] in GBM are in use yet, these hold promise as emerging targets. Strategies like gap junction inhibitors or selective sodium channel blockers (already used in epilepsy and pain) could be explored as adjuncts to disconnect tumor cells from supportive neural/glial networks.

4.3 Other targeted neuromodulatory agents

Beyond direct neurotransmitter and ion channel modulators, other agents and delivery strategies hold neuromodulatory implications.

FUS-gated nanoparticle delivery: Advances in biophysics now allow us to temporarily breach the BBB or deliver drugs in situ using noninvasive FUS. FUS, when combined with microbubbles or smart drug carriers, can localize therapy to the tumor region with minimal systemic side effects. For example, FUS has been used to open the BBB transiently, allowing intravenous drugs to perfuse into brain tumors at high concentrations. In addition, researchers have developed nanoparticles that respond to ultrasound triggers. A striking case is the use of a nanoemulsion carrying the anesthetic propofol. In this, when ultrasound is applied to the target region, the already-present nanoemulsion releases propofol on demand. This technique enabled a noninvasive, focal neuromodulation paradigm where propofol (a GABA_A agonist) was delivered into specific brain areas, causing local neural inhibition without systemic anesthesia. Such FUS-gated therapies have immediate applications, for instance, selectively calming hyperexcitable tumor-infiltrated networks or improving drug delivery to invasive tumor margins. The approach has already entered clinical translation for gliomas, where FUS paired with microbubbles has been used to enhance chemotherapy delivery in GBM patients (in clinical trials like NCT03744026). Additionally, efforts are underway to integrate FUS with drug-loaded liposomes/nanoparticles for precision therapy. While not a “drug molecule” per se, FUS is a powerful enabling technology that can amplify the impact of neuromodulatory drugs by overcoming the BBB, which has proven to be a recurring hurdle in neuro-oncology.

Traditional targeted therapies with neuromodulatory implications: While not strictly “neuromodulatory” in their primary mechanism of action, some traditional targeted therapies for brain cancer interact with pathways that can indirectly influence the tumor’s microenvironment or its interaction with neural elements. For instance, Bevacizumab targets vascular endothelial growth factors (VEGF), inhibiting the formation of new blood vessels that supply tumors. Everolimus blocks mTOR, a protein crucial for cell growth and division. Temozolomide, a DNA-alkylating agent, has been shown via molecular docking studies to interact with several secretory proteins (GDF1, SLIT1, NPTX1, CREG2, SERPINI) implicated in gliomagenesis and radio resistance [45]. The epidermal growth factor receptor (EGFR) is another significant target in GBM, with mutations like EGFRvIII leading to constitutive activation of mitogenic signaling pathways [64]. The overlap between traditional targeted therapies and neuromodulation, particularly in the context of the TME and cell signaling, suggests that a holistic, interdisciplinary approach is needed. Drugs initially designed for one purpose might have unexpected neuromodulatory effects that can be leveraged for therapeutic benefit.

Table 1 summarizes key neuromodulatory drug molecules, their molecular targets, and relevant insights from docking and mechanistic studies in the context of brain cancer.

Drug name (type) Primary target(s) Mechanism of action (neuromodulatory and anti-cancer) Key findings/molecular docking insights Ref (s)
Perampanel (AMPA antagonist) AMPA-type glutamate receptors Non-competitive AMPA receptor blocker; reduces excitatory transmission and glutamate-driven proliferation. BBB penetrant. Inhibits glioma proliferation in vitro but has limited in vivo efficacy. Used clinically for BTRE seizure control. [55]
Gabapentin (α2δ-1 ligand) α2δ-1 calcium channel subunit (Thrombospondin-1 receptor) Blocks TSP-1-induced excitatory synaptogenesis by binding the α2δ-1 subunit. Inhibits neuron-glioma synapses. Retrospective study: + 4–6 months survival in GBM patients with lower serum TSP-1. [56]
Dizocilpine (MK-801) GYKI-5,2466 (+ Talampanel) NMDA receptor (MK-801); AMPA receptors (GYKI & talampanel) Ionotropic glutamate receptor antagonists block Ca2⁺ currents and excitotoxic signaling. Broad antiproliferative effects in vitro, but poor BBB penetration. Talampanel showed a modest survival benefit in Phase II. [65]
Fluoxetine (SSRI antidepressant) AMPA receptors (on tumor cells); Serotonin transporters (in the CNS) Allosteric AMPAR modulator causing excessive Ca2⁺ influx and apoptosis in glioma cells. Selective glioma cell killing via Ca2⁺-mediated apoptosis. Oral treatment suppressed GBM growth, comparable to TMZ. [58]
Baclofen (GABA_B agonist) GABA_B receptors (GPCR, metabotropic GABA) Activates inhibitory GABA_B signaling; reduces cAMP and pro-growth pathways. Suppressed tumor growth in rodent models via GABA_B-mediated inhibition of CREB/ERK signaling. [66]
Pyrazoloquinolinones (PQs) (GABA_A receptor modulators) GABA_A receptor (extracellular benzodiazepine site at α1 +/β3– interface) Allosteric GABA_A potentiators enhancing GABAergic inhibition. Molecular docking validated α1–β3 interface binding. Tool compounds for modulating neuronal–tumor interactions. [60]
Valproic Acid (VPA) (antiepileptic and HDAC inhibitor) Neuronal target: Increases GABA levels. Tumor target: Class I and IIa HDAC enzymes. Enhances GABA neurotransmission and inhibits HDACs for epigenetic tumor suppression. Sensitizes GBM to TMZ via SLC upregulation and DNA-repair downregulation. Induces cell-cycle arrest and differentiation. [67]
Midazolam(Benzodiazepine anesthetic) TRPM7 channels (in glioma cells); GABA_A receptors (in neurons – sedation effect). GABA_A modulator (sedation) and TRPM7 channel blocker (anti-proliferative). Suppresses TRPM7 currents and glioma proliferation. Lipophilic, BBB-crossing, but limited by CNS depression. [68]
Mibefradil (T-type Ca2⁺ channel blocker) Cav3 (T-type) calcium channels (+ some effect on L-type Ca channels) Blocks T-type Ca2⁺ channels; prevents Ca2⁺ influx needed for cell cycle progression. Induced tumor cell cycle arrest and enhanced TMZ efficacy in preclinical models. A Phase I/II trial showed tolerability. [69]
hERG channel blockers (e.g., astemizole, etc.) hERG (Kv11.1) potassium channels (often misexpressed in tumors) Block K⁺ currents are important for tumor electrophysiology and cell cycle regulation. High hERG expression correlates with worse GBM outcomes. Patients on hERG blockers showed improved survival. [70]
Thioridazine(Antipsychotic, EAG2 blocker) EAG2 (Kv10.2) potassium channels; D2 dopamine receptors (original CNS target). EAG2 K⁺ channel antagonist impairing cell volume regulation during proliferation. Significantly reduced medulloblastoma growth and eliminated spinal metastases in preclinical models. [71]
Propofol (FUS-nanoparticle)(Focused Ultrasound delivery) GABA_A receptors (β-subunit binding site on GABA_A – propofol’s anesthetic target) GABA_A-positive modulator delivered via FUS-triggered nanoparticles for targeted neuromodulation. FUS-triggered propofol release achieved localized neuromodulation with negligible systemic exposure. [72]
Bevacizumab (Anti-VEGF antibody) VEGF-A (vascular endothelial growth factor) in tumor vasculature Neutralizes VEGF to inhibit angiogenesis; reduces tumor edema and intracranial pressure. Rapidly improves GBM symptoms through reduced edema. Prolongs progression-free survival but not overall survival. [73]
Everolimus (mTOR inhibitor) mTOR kinase (component of the mTORC1 complex) Allosterically inhibits mTORC1; downregulates the PI3K–AKT–mTOR growth pathway. Cytostatic effects in high-grade gliomas. Tumor shrinkage and seizure reduction in TSC-associated low-grade gliomas. [74]
Temozolomide (TMZ) (DNA alkylator) Genomic DNA in tumor cells (plus potential secreted protein targets). DNA methylation at O6-guanine, triggering mismatch repair and apoptosis. Standard GBM chemotherapy. Potential off-target binding to neuronal proteins (GDF1, SLIT1), affecting the tumor microenvironment. [75]

Table 1.

Key neuromodulatory drug molecules, their molecular targets, and relevant docking/mechanism insights in brain cancer.

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5. Challenges and limitations in molecular docking for brain cancer neuromodulation

Despite the advances in molecular docking for developing neuromodulatory drugs for brain cancer, several significant barriers hinder their translation into clinical practice.

BBB penetration: The BBB restricts most drugs from entering the CNS, allowing only small, lipophilic molecules (typically under 400–500 Da) to pass. Efflux pumps, like P-glycoprotein, further limit access to the CNS, and while chemical modifications can enhance permeability, they often compromise the drug’s efficacy. Possible solutions include using FUS to disrupt the BBB and employing integrated models to evaluate permeability at early stages.

Tumor heterogeneity and microenvironment: Glioblastomas exhibit extreme genetic and phenotypic diversity, along with complex TMEs that include immune, vascular, and stromal components. Phenomena such as immune evasion – exemplified by the upregulation of PD-1/PD–L1 – and changes driven by tumor recurrence render single-target docking methods inadequate for capturing the dynamics of multiple cell types.

Docking limitations: Assumptions regarding protein rigidity can overlook induced-fit effects, and scoring functions often lack universal accuracy, leading to false positives. The absence of standardized datasets makes reproducibility challenging, and large-scale screening requires substantial computational resources.

Translational gaps: Beyond technical limitations, issues related to the BBB, tumor heterogeneity, and the complexities of trial design slow down clinical progress. Docking approaches must integrate biological insights, rigorous in vitro and in vivo validation, and well-designed clinical trials to ensure therapeutic success.

The computational strategy positions molecular docking at the core of neuromodulatory drug discovery for brain cancer. Docking provides the primary triage for large chemical libraries, generating ranked ligand lists and predicted binding poses that are subsequently validated by MD, rescoring, and ADME/BBB filters. Emphasizing rigorous docking workflows, such as careful receptor preparation, explicit treatment of flexibility, pose rescoring, and cross-platform comparison, will reduce false positives and streamline experimental prioritization.

Molecular docking is not merely an exploratory screen but the central decision step of the pipeline: it rapidly narrows chemical space to a manageable set of candidates for downstream experimental validation. To maximize translational yield, docking must be executed as part of an integrated workflow, such as ensemble/flexible docking to account for receptor dynamics, rescoring with physics-based methods (MM-GBSA or FEP where feasible), MD validation of binding pose stability, and ADME/BBB filtering to prioritize deliverable compounds. We therefore recommend reporting both docking and rescoring metrics, presenting representative pose visualizations, and benchmarking the pipeline against reference ligands (e.g., perampanel for AMPAR, gabapentin for α2δ-1) to demonstrate predictive value prior to experimental testing.

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6. Future directions and recommendations

Developing neuromodulatory drugs for brain cancer requires a multi-pronged strategy that integrates advanced computational tools, innovative delivery systems, personalized medicine, and robust translational research. AI-driven drug discovery, deep learning, and integrated multi-scale modeling are essential for navigating the complexity of tumor biology, improving docking accuracy, and efficiently repurposing compounds. Overcoming the BBB requires innovative delivery methods, including FUS, targeted nanoparticles, radioconjugates, and exosome-based systems to achieve accurate drug localization. Personalized approaches, guided by multi-omics data and predictive biomarkers, will enable therapies tailored to individual tumor profiles. Connecting findings from computer simulations with thorough laboratory, animal, and clinical studies is essential for confirming their effectiveness and safety. Ultimately, progress will depend on collaboration across various fields and the sharing of open-access data to accelerate innovation and its application from the lab to patient care.

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7. Conclusion

The landscape of brain cancer therapeutics is undergoing a transformative shift, driven by the burgeoning field of cancer neuroscience and the recognition that brain tumors actively exploit neural pathways for their growth and survival. Neuromodulation, both pharmacological and non-pharmacological, offers a compelling new paradigm for intervention, moving beyond traditional cytotoxic approaches to disrupt the intricate interplay between tumor cells and the neural microenvironment. Molecular docking stands as an indispensable computational tool in this endeavor, enabling the prediction of drug–target interactions, the virtual screening of vast chemical libraries, and the repurposing of existing drugs with known safety profiles. Promising candidates, such as perampanel and gabapentin, demonstrate the potential of targeting neurotransmitter receptors and synaptogenic proteins like TSP-1, leading to observed anti-proliferative effects and, in the case of gabapentin, a survival benefit in glioblastoma patients. Similarly, the multi-mechanistic action of valproic Acid, combining neuromodulatory and epigenetic effects, underscores the value of identifying compounds with pleiotropic anti-cancer activities. Ion channels, widely dysregulated in brain tumors and accessible on the cell surface, also represent highly attractive and “druggable” targets for novel neuromodulatory therapeutics. Despite these advancements, significant hurdles persist. The BBB remains a formidable physiological barrier, limiting the brain penetration of many promising drug candidates. The profound intra- and inter-tumoral heterogeneity of brain cancers, coupled with the complex and dynamic TME, poses challenges for single-target therapies. Furthermore, inherent limitations in molecular docking methodologies, particularly regarding accurate protein dynamics, scoring functions, and the need for standardized validation, necessitate continuous refinement of computational models. To overcome these challenges and translate in silico predictions into meaningful clinical outcomes, a concerted, multidisciplinary effort is required. This includes advancing computational models through AI and multi-scale simulations, developing innovative drug delivery strategies like FUS-enhanced nanoparticle delivery and exosome-mediated transport, and embracing personalized medicine approaches guided by comprehensive biomarker identification. Ultimately, rigorous experimental validation in vitro and in vivo, followed by well-designed prospective clinical trials, remains paramount to confirm the safety and efficacy of neuromodulatory drug molecules. Collaborative research and open data-sharing initiatives will be critical to accelerating discovery and ensuring that these innovative therapeutic strategies reach patients in need, offering renewed hope in the fight against brain cancer.

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Conflict of Interest

The authors declare no conflict of interest.

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Written By

Monochura Saha, Shubham Yadav, Habiba Zeb and Ishaq N. Khan

Submitted: 23 September 2025 Reviewed: 08 October 2025 Published: 06 February 2026

© 2026 The Author(s). Licensee IntechOpen. This content is distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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