Biological psychiatry global open science · 2026
Refining Drug Classes: A Multidimensional, Data-Driven Map of Antidepressant Pharmacology.
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Martins Daniel, Fernandes Lia, Williams Steven C R, Turkheimer Federico, Mehta Mitul A
Аннотация
BACKGROUND: Traditional classifications of antidepressant medications are organized around primary molecular targets or historical development pathways. However, these categorical systems obscure substantial heterogeneity in receptor- and transporter-level pharmacology both within and across classes. Many antidepressants exhibit clinically relevant polypharmacology, engaging multiple neuromodulatory systems at biologically meaningful affinities. Therefore, we undertook a data-driven reappraisal of antidepressant pharmacology based on multidimensional ligand-target binding profiles.
METHODS: Experimentally measured inhibition constants (Ki) were curated from public pharmacological databases to construct receptor and transporter affinity "fingerprints" for 25 commonly prescribed antidepressants across serotonergic, dopaminergic, histaminergic, cholinergic, and adrenergic targets. Median pKi values were aggregated to generate a drug × target affinity matrix. This matrix was analyzed using unsupervised hierarchical clustering and principal component analysis, without reference to conventional therapeutic class labels.
RESULTS: Antidepressants formed a structured yet continuous pharmacological landscape organized along graded dimensions reflecting transporter selectivity versus receptor-level polypharmacology. Drugs traditionally grouped within the same class frequently diverged in their multidimensional affinity profiles, whereas compounds from different classes often clustered together. Histaminergic, muscarinic, and adrenergic targets were major contributors to the separation between broadly acting and more selective agents.
CONCLUSIONS: Antidepressants are more accurately characterized by multidimensional affinity architectures than by categorical class labels. Affinity-based representations provide a mechanistically transparent framework for linking molecular pharmacology to systems-level brain function and for understanding how antidepressants engage distributed neuromodulatory systems beyond their traditionally defined primary targets.
METHODS: Experimentally measured inhibition constants (Ki) were curated from public pharmacological databases to construct receptor and transporter affinity "fingerprints" for 25 commonly prescribed antidepressants across serotonergic, dopaminergic, histaminergic, cholinergic, and adrenergic targets. Median pKi values were aggregated to generate a drug × target affinity matrix. This matrix was analyzed using unsupervised hierarchical clustering and principal component analysis, without reference to conventional therapeutic class labels.
RESULTS: Antidepressants formed a structured yet continuous pharmacological landscape organized along graded dimensions reflecting transporter selectivity versus receptor-level polypharmacology. Drugs traditionally grouped within the same class frequently diverged in their multidimensional affinity profiles, whereas compounds from different classes often clustered together. Histaminergic, muscarinic, and adrenergic targets were major contributors to the separation between broadly acting and more selective agents.
CONCLUSIONS: Antidepressants are more accurately characterized by multidimensional affinity architectures than by categorical class labels. Affinity-based representations provide a mechanistically transparent framework for linking molecular pharmacology to systems-level brain function and for understanding how antidepressants engage distributed neuromodulatory systems beyond their traditionally defined primary targets.