Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Abstract

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning-and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

CNSMolGen: A Bidirectional Recurrent Neural Network-Based Generative Model for De Novo Central Nervous System Drug Design

“…To address this problem, several innovative approaches have been developed and used in CNS drug discovery, including the identification of molecular drug targets , 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., antineurodegenerative drugs and antidepressants) . Nonetheless, currently approved MATs-targeted drugs still exhibit certain side effects, including addiction and drug resistance, and the discovery of novel and effective drug molecules remains slow . Therefore, rational design of new drugs acting on MATs is urgently needed .…”