This study presents a targeted virtual drug screening approach for autism spectrum disorder (ASD), focusing on Cav1.2 calcium ion channels as potential therapeutic targets. ASD is a complex neurodevelopmental disorder characterized by impairments in social communication and behavior, with genetic factors playing a significant role. Cav1.2 channels have been implicated in the pathophysiology of ASD due to their role in regulating neuronal excitability and synaptic transmission. We employed computational methods to virtually screen a large database of compounds for their potential to modulate Cav1.2 channel function. Molecular docking simulations were used to identify potential Cav1.2 inhibitors, followed by pharmacokinetic modeling to assess drug-like properties. Molecular dynamics (MD) simulations were performed to evaluate the interactions of the top candidates with Cav1.2, and Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) analysis was employed to predict binding free energies. This approach identified several promising drug candidates, including ZINC000828320609, which exhibited strong binding affinity to Cav1.2, favorable pharmacokinetic properties, and no predicted toxicity. The virtual screening results provide a solid foundation for further experimental validation and potential drug development for ASD, offering a novel and efficient strategy to target Cav1.2 channels in the treatment of this complex disorder.

Cytotoxin VacA, a major virulence factor of Helicobacter pylori—the principal etiological agent of gastric cancer—represents a compelling therapeutic target. Pharmacological inhibition of VacA through small-molecule agents offers a putative strategy to substantially mitigate gastric carcinogenesis risk. Notably, contemporary medical practice lacks a single “specific drug” capable of independently eradicating H. pylori, with current management limited to anti-inflammatory regimens. In this context, drug repurposing has gained prominence as a cost-efficient paradigm in pharmaceutical innovation, optimizing resource allocation during the discovery phase. The present investigation thus focuses on identifying novel small-molecule inhibitors specifically targeting the VacA protein. Our multi-tiered screening methodology integrates computational and biophysical approaches: molecular docking, ADMET, Molecular Dynamics (MD) simulations and MM/PBSA calculations with the ZINC15 database constituting our primary compound source.Following rigorous virtual screening, seven lead candidates emerged based on AutoDock Vina scoring metrics. These were subjected to extended molecular dynamics simulations to validate binding affinity and conformational resilience under physiological conditions. Subsequent pharmacokinetic profiling benchmarked key parameters against established drug-likeness criteria, while energy decomposition analysis precisely mapped critical protein-ligand interaction hotspots within the VacA binding pocket. This systematic investigation not only identifies putative small-molecule inhibitors of cytotoxin VacA but also delineates a rational framework for virulence factor-targeted therapy against H. pylori infection. Our approach establishes a blueprint for next-generation antimicrobials that could transform the therapeutic landscape of gastric cancer prevention through targeted virulence attenuation.

ClC-3 chloride channels play essential roles in cervical cancer progression by regulating lysosomal acidification, cell volume homeostasis, and chemoresistance. However, no highly selective small-molecule modulators of ClC-3 have been reported to date. Motivated by the urgent clinical need to reverse ClC-3-mediated chemoresistance and the challenge of processing massive chemical libraries under limited computational hardware resources, we propose a novel AI-driven Drug Discovery (AIDD) pipeline. Here, we present an integrated virtual drug discovery framework that transitions from traditional Computer-Aided Drug Design (CADD) by combining large-scale molecular docking, deep-learning-based rescoring, pharmacokinetic filtering, and atomistic molecular dynamics (MD) simulations. The primary advantage of this proposed scheme lies in the integration of GNINA 3D-convolutional neural network (CNN) rescoring, which significantly reduces the false-positive rates inherent in empirical scoring functions for membrane proteins. A library of ∼180,000 ZINC15 compounds was initially screened using AutoDock Vina, followed by GNINA convolutional neural network rescoring to refine predicted binding affinity and pose confidence. ADMET profiling further narrowed the candidates, providing computational proof of drug-likeness and toxicity criteria rather than experimental validation. Ultimately, only ZINC000001556308 (Lig8) satisfied all in silico criteria. To validate binding stability, we performed 100-ns all-atom MD simulations of the ClC-3–Lig8 complex embedded in a lipid bilayer. Lig8 induced reduced RMSD fluctuations, lower RMSF values across key transmembrane helices, and a more compact radius of gyration, indicating enhanced structural stabilization of ClC-3. MM/PBSA calculations confirmed favorable binding energetics dominated by van der Waals interactions, while per-residue decomposition identified PHE527, GLY283, and GLY584 as major contributors to ligand recognition. These results reveal a previously uncharacterized binding pocket within the ClC-3 transmembrane domain and highlight Lig8 as a promising lead compound for targeting ClC-3-mediated oncogenic signaling. Overall, this study establishes the first comprehensive computational framework for ClC-3 modulator discovery and provides a validated chemical scaffold for future therapeutic development against cervical cancer.