QSAR Studies and Structure Property/Activity Relationships Applied in Pyrazine Derivatives as Antiproliferative Agents Against the BGC823

Electronic structures, the effect of the substitution, structure physicochemical property/activity relationships and drug-likeness applied in pyrazine derivatives, have been studied at ab initio (HF, MP2) and B3LYP/DFT (density functional theory) levels. In the paper, the calculated values, i.e., NBO (natural bond orbitals) charges, bond lengths, dipole moments, electron affinities, heats of formation and quantitative structure-activity relationships (QSAR) properties are presented. For the QSAR studies, we used multiple linear regression (MLR) and artificial neural network (ANN) statistical modeling. The results show a high correlation between experimental and predicted activity values, indicating the validation and the good quality of the derived QSAR models. In addition, statistical analysis reveals that the ANN technique with (9-4-1) architecture is more significant than the MLR model. The virtual screening based on the molecular similarity method and applicability domain of QSAR allowed the discovery of novel anti-proliferative activity candidates with improved activity.

• pyrazine derivatives
• antiproliferative agents
• structure-activity relationship
• QSAR
• electronic structure
• natural bond orbital charges
• artificial neural networks
• multiple linear regression
• virtual screening
• molecular similarity
• applicability domain
• drug-likeness

• 3 — Bonne santé et bien-être

• Computational Drug Discovery Methods
• Advanced Chemical Physics Studies
• Synthesis and biological activity
• pyrazine derivatives
• antiproliferative agents
• structure-activity relationship
• QSAR
• electronic structure
• natural bond orbital charges
• artificial neural networks
• multiple linear regression
• Computational Theory and Mathematics
• Atomic and Molecular Physics, and Optics
• Organic Chemistry
• Computer Science
• Physics and Astronomy
• Chemistry