Abstract

Objective: Antipsychotic therapy is frequently burdened by severe cognitive, motor, and cardiac adverse ef fects. This study aims to map the biophysical basis of these iatrogenic events by analyzing the interaction of major neuroleptics with a set of critical targets: nAChR, AChE, AMPA, 5-HT2A, and hERG.

Methods: A dual computational approach was employed, integrating blind docking screening (CB-Dock2) with high-resolution structural prediction via the Boltz-2 deep learning algorithm (AlphaFold 3 implementa tion). Ligand Efficiency (LE) and complex stability metrics (pLDDT, ipTM, PAE) were calculated for typical (Haloperidol, Chlorpromazine) and atypical (Risperidone, Clozapine, Olanzapine) molecules. These were compared against natural ligands (Nicotine, Acetylcholine) and specific inhibitors (Vecuronium, Donepezil).

Results: Simulations reveal that neuroleptics act as nonspecific steric hindrances.

• nAChR: Clozapine demonstrates superior geometric complementarity (pLDDT = 0.937) compared to Haloperidol, acting as a specific orthosteric site interferent. Nicotine exhibits the highest LE (0.53), supporting the hypothesis of self-medication through thermodynamic displacement.
• AChE/Akathisia: Risperidone shows a binding affinity of -12.0 kcal/mol, comparable to Donepezil, indi cating enzymatic saturation as the underlying cause of iatrogenic cholinergic dysregulation
• hERG/Cardiotoxicity: Chlorpromazine manifests the highest electrophysiological risk (LE = 0.48), com promising cardiac repolarization.
• Glutamate: The Clozapine LE_GLUr/LE_D2 ratio (1.08) highlights a predominance of synaptic plastici ty inhibition over the dopaminergic effect.

Conclusions: The study demonstrates that neuroleptics induce a global biophysical rigidity, converting dy namic neural signaling into mechanical constraints.

DOI: doi.org/10.63721/26JPIR0130

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