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With enormous success in both science and engineering, the recent advances in evolutionary computation particularly memetic computing is gaining increasing attention in the molecular optimization community.
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Abdolhosseini Qomi, M. J. et al. Combinatorial molecular optimization of cement hydrates.
Zhao, W. et al. Molecular optimization enables over 13% efficiency in organic solar cells.
Both methods have been widely used to solve the nonlinear constrained optimization problems in the numerical optimization community.
Additionally, a full-time molecular optimization module called ChemMOP was also developed to perform the molecular optimization operation conveniently.
While Bayesian optimization has been developed in the machine learning community, CMA-ES has been developed in the optimization community.
MMP analysis has a practical goal: molecular transformations can help medicinal chemists to drive the molecular optimization process.
The molecular optimization was carried out using the density function theory (DFT /B3LYP with basis set 6-31G.
This study investigates how prediction-driven MMP rules can guide the molecular optimization process.
Several important points should be noted regarding the limitations of transformation-driven molecular optimization.
This can facilitate drug design as a part of molecular optimization process.
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