  In therapeutic design, balancing various physiochemical properties is crucial
for molecule development, similar to how Multiparameter Optimization (MPO)
evaluates multiple variables to meet a primary goal. While many molecular
features can now be predicted using \textit{in silico} methods, aiding early
drug development, the vast data generated from high throughput virtual
screening challenges the practicality of traditional MPO approaches. Addressing
this, we introduce a scaffold focused graph-based Markov chain Monte Carlo
framework (ScaMARS) built to generate molecules with optimal properties. This
innovative framework is capable of self-training and handling a wider array of
properties, sampling different chemical spaces according to the starting
scaffold. The benchmark analysis on several properties shows that ScaMARS has a
diversity score of 84.6\% and has a much higher success rate of 99.5\% compared
to conditional models. The integration of new features into MPO significantly
enhances its adaptability and effectiveness in therapeutic design, facilitating
the discovery of candidates that efficiently optimize multiple properties.
