Computational evaluation of some compounds as potential anti-breast cancer agents

Abstract

Abstract Background The emergence of high resistance and toxicity of the existing anti-breast cancer drugs have demanded the need to design new drugs with improved activities against breast cancer. A computational technique incorporating quantitative structure–activity relationship and virtual template-based design was carried out to evaluate thirty-four compounds from derivatives of thiophene, pyrimidine, coumarin, pyrazole and pyridine with anti-breast cancer activities. The chemical structures of the compounds were drawn with chem draw v.12.0.2 and they were optimized using Spartan 14 software. The molecular descriptors were calculated with the aid of PaDel descriptor software. The dataset was curated and then divided into training and test set that was used to generate and validate the model. Results The first out of the four models generated was chosen as the paramount model with statistical validations of R 2 = 0.9847, $$R_{{{\text{adj}}}}^{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>R</mml:mi> <mml:mrow> <mml:mtext>adj</mml:mtext> </mml:mrow> <mml:mn>2</mml:mn> </mml:msubsup> </mml:math> = 0.9814, $$Q_{{{\text{cv}}}}^{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>Q</mml:mi> <mml:mrow> <mml:mtext>cv</mml:mtext> </mml:mrow> <mml:mn>2</mml:mn> </mml:msubsup> </mml:math> = 0.9763, min expt. error for non-significant LOF (95%) = 0.0679, an external validation $$R_{{{\text{test}}}}^{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>R</mml:mi> <mml:mrow> <mml:mtext>test</mml:mtext> </mml:mrow> <mml:mn>2</mml:mn> </mml:msubsup> </mml:math> of 0.8240 and coefficient of Y-randomization ( $${\text{cR}}_{{\text{p}}}^{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mtext>cR</mml:mtext> <mml:mrow> <mml:mtext>p</mml:mtext> </mml:mrow> <mml:mn>2</mml:mn> </mml:msubsup> </mml:math> ) = 0.8200, which confirm the robustness of the model. Conclusions The high predictive power of the generated model describes the models’ reliability and the designed compounds pointed out compound 2 with pGI 50 = 4.2504 as the best designed compound to inhibit breast cancer, compared to its co-designed compounds and the template. The results of this research provide vital information to the pharmaceutical chemists and the pharmacologist in the course of developing new breast cancer drugs.