COMPUTATIONAL DRUG DESIGN TARGETING THE 5WIV RECEPTOR FOR ADHD THERAPY

Abstract

Background: Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition characterized by persistent inattention, hyperactivity, and impulsive. While traditionally managed with pharmacological and behavioral interventions, the discovery of new therapeutic compounds remains a key challenge, given the long timelines and high costs associated with drug development.

Objective: This study aimed to identify a promising lead compound targeting the ADHD-related receptor 5WIV using a fully computed, in-silico drug discovery approach.

Methods: The 3D structure of the 5WIV receptor was retrieved from the RCSB Protein Data Bank. Ligand candidates were generated using the e- LEA3D de novo design server. The best-scoring ligand (Model g18) was selected and further analyzed. Swiss-Target Prediction was used to identify possible biological targets, while molecular docking was performed via Swiss-Dock (EADock DSS engine) and Auto-Dock Vina. Pharmacokinetic and toxicity properties were assessed using Swiss-ADME. Protein-ligand flexibility was evaluated through normal mode analysis (iMODS). A 3D- QSAR model was constructed using a ligand dataset including g18 and other pharmacologically relevant compounds.

Results: Ligand g18 demonstrated a high docking score (-97.21) and a composite design score of 64.81%. Target prediction suggested a 46% similarity to kinase proteins. ADMET analysis revealed favorable pharmacokinetic properties and low predicted toxicity. Structural dynamics via NMA confirm stable protein-ligand interaction. The 3D-QSAR model showed high predictive accuracy (R² = 0.94), with a low RMSE, validating the potential bioactivity of g18.

Conclusion: This study highlights efficiency and robustness of integrating de novo design, docking, ADMET screening, and QSAR modeling for early- stage drug discovery. The ligand g18 exhibited strong potential as a lead compound, meriting further biological validation. These findings support the role of computational pipelines in accelerating and de-risking ADHD drug development.