The present algorithm, on the other hand, is inspired by the Configurational Bias Monte Carlo (CBMC) for conformer generation in-silico , making every step of the process more efficient. Moreover, while constructing the strong binders based on physical interaction strength, we use a reinforcement learning (RL) model to bias the generation toward making molecules with certain desired properties such as logP (water-octanol partition coefficient) values, synthesizability, ADMET (adsorptiondistribution-metabolism-excretion-toxicity) properties , etc. For each of the properties, a separate RL model needs to be trained. In this study, we focus on the synthesizability criteria and demonstrate how binding affinity and synthesizability can be parallelly optimized using our algorithm. To our knowledge, this is the first algorithm capable of optimizing molecular properties using machine-learning methods while generating the molecules atomistically and directly at the active site. We believe that this method provides a new perspective on de novo SBDD. It is a method that can help find new molecular scaffolds to treat diseases where the old drugs face resistance (such as antibiotics) or where treatments are scarce.