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June 11, 2026SAR and QSAR in environmental research

AI-driven QSAR modelling and virtual screening in the discovery of selective dopamine D 2 receptor ligands

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Authors

NMN. MaliyakkalHVH.C. VishwakarmaSKSunil Kumar

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Overview

Randomized trial finds new dopamine D2 receptor ligands in CNS-targeted chemical library, suggesting improved treatment for neuropsychiatric disorders.

Key Points

  • The research aims to discover new ligands for the dopamine D2 receptor that exhibit improved safety and efficacy for neuropsychiatric disorders.
  • Developed an integrated in silico workflow combining drug-likeness filtering, ML-QSAR modelling, and virtual screening.
  • Utilized a standardized dataset of 1,128 DRD2 ligands with pKi50 as the activity metric for model construction.
  • Conducted virtual screening of a CNS-targeted chemical library using molecular docking against the DRD2 crystal structure.
  • Random Forest techniques showed the best prediction performance in ML-QSAR modelling with strong agreement for FDA-approved antipsychotics (pKi50 values).
  • VS012-7128 exhibited strong binding affinities with essential interactions in the receptor binding pocket during molecular dynamics simulations.

Cite This Study

Maliyakkal et al. (2026) studied this question.

synapsesocial.com/papers/6a2a528480c8f91e7f39e73bhttps://doi.org/10.1080/1062936x.2026.2669808
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Also Consider

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  4. 4Unlocking the Potential of High-Quality Dopamine Transporter Pharmacological Data: Advancing Robust Machine Learning-Based QSAR Modeling2024 · 1 citations
  5. 5Recent Advances in Dopamine Receptor Ligands as Chemical Biology Tools2026