Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug–target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI’s expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI’s growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

Membrane proteins, including small four-transmembrane proteins, play roles in membrane organization and signal transduction. Generally, they share capacities for protein-protein association and subcellular translocation to regulate cellular functions. Despite known commonalities and differences in structure and function, the relationships among the tetraspanin, CD20-like, golgi 4-transmembrane spanning transporter (Mtp), and transmembrane 4 L six (L6) families remain unclear. Here, we mostly compared TM4SF5, a transmembrane 4 L6 family member, to the genuine tetraspanins and the CD20-like family, exploring unique aspects that evolved from the small four-transmembrane proteins. TM4SF5 and other L6 membrane proteins have two conserved cysteines, affecting N-glycosylation in the large extracellular loop (LEL) and favoring a dimeric state, whereas the tetraspanins maintained their cysteine residues in addition to the CCG motif. However, TM4SF5 and other L6 membrane proteins have more sequence similarity to proteins in the CD20-like family, which lack these features in the LEL. Uniquely, TM4SF5 and other L6 membrane proteins have a β-sheet configuration in the LEL, unlike other small four-transmembrane proteins with α-helices. Therefore, the unique features in the LEL and the N-glycosylation of TM4SF5 may contribute to dimer formation and cholesterol binding, potentially influencing regulatory roles in liver malignancy development.