We applied multi-template docking to a virtual library of 26,375 potential compounds originating from MAGLinhibiting hit molecules, narrowing the pool to 1675 candidates based on predicted potency with pIC 50 ≥ 6. The reaction forward prediction models were then used to assess the synthetic feasibility of these candidates, identifying 668 compounds with predicted reaction yields of ≥ 5%. In the final step, a physicochemical and ADMET property prediction funnel was employed to automatically select 34 compounds with favorable solubility and permeability profiles. These compounds were subsequently prioritized for synthesis and biological testing. This workflow utilized an MDO strategy, enabling the simultaneous consideration of potency, synthetic feasibility, and physicochemical AD-MET properties. The MDO framework streamlined the prioritization of compounds that achieved a balance across these diverse criteria. This combined approach considerably accelerated the hit-to-lead optimization process by reducing the need for manual intervention and experimental resources. The integration of reaction yield prediction into this workflow was crucial, as demonstrated by recent studies highlighting the significant impact of predictive modeling on synthetic efficiency and material waste reduction in medicinal chemistry . This rapid optimization cycle, completed within one month, demonstrates the efficiency of this approach. It encompassed computational work (1 day), efficient synthesis (2 weeks), and biological measurement (2 weeks).