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Physical chemistry chemical physics : PCCP

ISSN 1463-9084

3 papers in the library · publishing 2021-2026

Papers

MFCC-DFT mapping of ligand recognition at the 5-HT2A receptor: energetic analysis of the interactions between serotonin, psychedelics, and antipsychotics.

Physical chemistry chemical physics : PCCP July 1, 2026 W S Clemente Junior, K S Bezerra, E G C Matias et al.

Depression is a major global health problem, and the serotonin 5-HT2A receptor is a key target for psychoactive drugs. This computational study examined how four ligands—serotonin, psilocybin/psilocin, LSD, and lumateperone—bind to the receptor. Using molecular fragmentation and density functional theory calculations, the total interaction energies ranged from -35.38 to -71.98 kcal/mol. Key amino acids Asp155, Phe339, Leu229, and Val366 acted as structural anchors in the binding pocket. Different parts of the ligands showed distinct interaction patterns. The findings provide a detailed energetic map of ligand recognition that may aid the rational design of new serotonergic drugs.

Computational design of an improved photoswitchable psychedelic based on light absorption, membrane permeation and protein binding.

Physical chemistry chemical physics : PCCP September 18, 2025 Vito F Palmisano, Claudio Agnorelli, Shirin Faraji et al.

A new photoswitchable compound, PQ-azo-N,N-DMT, was computationally designed to improve upon an earlier version. It binds tightly to the 5-HT2A receptor, maintains key interactions similar to LSD, shows good membrane permeability, and absorbs red-shifted light for visible-spectrum photocontrol. This offers precise spatio-temporal control over receptor activation, which could help clarify the role of hallucinatory effects in antidepressant drug development.

Application and assessment of deep learning for the generation of potential NMDA receptor antagonists.

Physical chemistry chemical physics : PCCP January 21, 2021 Katherine J Schultz, Sean M Colby, Yasemin Yesiltepe et al.

Generative deep learning models can design new drug-like compounds that block the N-methyl d-aspartate receptor (NMDAR), a target for neurological diseases like Parkinson's and Alzheimer's, though some existing NMDAR antagonists cause dissociative effects and have been used to create illicit drugs. The study created a library of experimentally validated NMDAR PCP-site antagonists and applied ligand- and structure-based assessment techniques to deep learning-generated compounds. Twelve candidate antagonists not found in existing chemical databases were identified, but synthesis and experimental validation are still needed.