To address this problem, several innovative approaches have been developed and used in CNS drug discovery, including molecular drug targets identification and drug design aided by either physics-based modeling or deep-learning based artificial intelligence (AI) . For example, monoamine transporters (MATs), including dopamine transporter (DAT), norepinephrine transporter (NET), and serotonin transporter (SERT), play a crucial role in regulating neurotransmitters through reuptake and have been identified as important molecular targets of CNS disorders' medications (e.g. anti-neurodegenerative drugs and antidepressants) . Nonetheless, currently approved MATs-targeted drugs still exhibit certain side effects, including addiction and drug resistance . Therefore, rational design of new drugs acting on MATs are urgently needed . For the traditional computer-aided drug design (CADD) approach, it is often necessary to conduct virtual screening on molecular libraries with large size to identify molecules with specific characteristics . Despite significant progress has achieved in the computational power for processing these databases, the enormous size of the chemical space 10 23 -10 60 drug-like molecules , continues to pose challenges in efficiently identifying molecules that specifically bind to molecular drug target . While the recent advent of deep learning-based molecular generation techniques may offer viable solutions to this predicament .