Gachon Institute of Pharmaceutical Science & Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon 21936, Korea.
CimplSoft, Thousand Oaks, CA 91320, USA.
Molecules. 2018 Sep 25;23(10):2452. doi: 10.3390/molecules23102452.
The dopamine D3 receptor is an important CNS target for the treatment of a variety of neurological diseases. Selective dopamine D3 receptor antagonists modulate the improvement of psychostimulant addiction and relapse. In this study, five and six featured pharmacophore models of D3R antagonists were generated and evaluated with the post-hoc score combining two survival scores of active and inactive. Among the Top 10 models, APRRR215 and AHPRRR104 were chosen based on the coefficient of determination (APRRR215: R² = 0.80; AHPRRR104: R² = 0.82) and predictability (APRRR215: Q² = 0.73, R² = 0.82; AHPRRR104: Q² = 0.86, R² = 0.74) of their 3D-quantitative structure⁻activity relationship models. Pharmacophore-based virtual screening of a large compound library from eMolecules (>3 million compounds) using two optimal models expedited the search process by a 100-fold speed increase compared to the docking-based screening (HTVS scoring function in Glide) and identified a series of hit compounds having promising novel scaffolds. After the screening, docking scores, as an adjuvant predictor, were added to two fitness scores (from the pharmacophore models) and predicted Ki (from PLSs of the QSAR models) to improve accuracy. Final selection of the most promising hit compounds were also evaluated for CNS-like properties as well as expected D3R antagonism.
多巴胺 D3 受体是治疗多种神经疾病的重要中枢神经系统靶点。选择性多巴胺 D3 受体拮抗剂可调节改善精神兴奋剂成瘾和复发。在这项研究中，生成了五个和六个 D3R 拮抗剂的特征药效团模型，并使用活性和非活性的两个生存评分的后验分数进行了评估。在前十名模型中，根据决定系数（APRRR215：R² = 0.80；AHPRRR104：R² = 0.82）和可预测性（APRRR215：Q² = 0.73，R² = 0.82；AHPRRR104：Q² = 0.86，R² = 0.74），选择了 APRRR215 和 AHPRRR104 构建 3D-QSAR 模型。使用两个最佳模型对 eMolecules 中的大型化合物库（超过 300 万种化合物）进行基于药效团的虚拟筛选，与基于对接的筛选（Glide 中的 HTVS 评分函数）相比，搜索速度提高了 100 倍，并确定了一系列具有有前途的新骨架的命中化合物。筛选后，将对接评分（作为辅助预测因子）添加到两个适应性评分（来自药效团模型）和预测 Ki（来自 QSAR 模型的 PLS）中，以提高准确性。还对最有前途的命中化合物进行了中枢神经系统样特性和预期 D3R 拮抗作用的最终选择评估。
