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.

Toxicity, defined as the potential harm a substance can cause to living organisms, requires the implementation of stringent regulatory standards to ensure public safety. These standards involve comprehensive testing frameworks, including hazard identification, dose-response evaluation, exposure assessment, and risk characterization. In drug discovery and development, these processes are often complex, time-consuming, and also resource-intensive. Toxicity-related failures in the later stages of drug development can lead to substantial financial losses, underscoring the need for reliable toxicity prediction during the early discovery phases. The advent of computational approaches has accelerated a shift toward in silico modeling, virtual screening, and, notably, artificial intelligence (AI) to identify potential toxicities earlier in the pipeline. Ongoing advances in databases, algorithms, and computational power have further expanded AI’s role in pharmaceutical research. Today, AI models are capable of predicting wide range of toxicity endpoints, such as hepatotoxicity, cardiotoxicity, nephrotoxicity, neurotoxicity, and genotoxicity, based on diverse molecular representations ranging from traditional descriptors to graph-based methods. This review provides an in-depth examination of AI-driven toxicity prediction, emphasizing its transformative impact on drug discovery and its growing importance in improving safety assessments.

Targeted protein degradation (TPD) provides unique advantages over gene knockdown in that it can induce selective degradation of disease-associated proteins attributed to pathological mutations or aberrant post-translational modifications (PTMs). Herein, we report a protein degrader, PRZ-18002, that selectively binds to an active form of p38 MAPK. PRZ-18002 induces degradation of phosphorylated p38 MAPK (p-p38) and a phosphomimetic mutant of p38 MAPK in a proteasome-dependent manner. Given that the activation of p38 MAPK plays pivotal roles in the pathophysiology of Alzheimer's disease (AD), selective degradation of p-p38 may provide an attractive therapeutic option for the treatment of AD. In the 5xFAD transgenic mice model of AD, intranasal treatment of PRZ-18002 reduces p-p38 levels and alleviates microglia activation and amyloid beta (Aβ) deposition, leading to subsequent improvement of spatial learning and memory. Collectively, our findings suggest that PRZ-18002 ameliorates AD pathophysiology via selective degradation of p-p38, highlighting a novel therapeutic TPD modality that targets a specific PTM to induce selective degradation of neurodegenerative disease-associated protein.

Danbi Yoon, Ju Heun Moon, Anna Cho, Hyejoon Boo, Jeong Seok Cha*, Yoonji Lee*, and Jiho Yoo*. Mol. Cells 2023;46:337-44. https://doi.org/10.14348/molcells.2023.0001

Metformin (MetF) is used worldwide as a first-line therapy for type 2 diabetes. Recently, interest in the pleiotropic effects of MetF, such as its anticancer and antiaging properties, has increased. However, the molecular target of MetF and the detailed mechanism underlying its ability to inhibit cell growth through autophagy induction remain incompletely understood. In this study, using an innovative label-free drug affinity responsive target stability (DARTS)-LC-MS/MS method, we discovered that mitochondrial voltage-dependent anion channel 1 (VDAC1) is a novel binding protein involved in the induction of autophagy-related cell death by high-dose MetF in hepatocellular carcinoma (HCC). Computational alanine scanning mutagenesis revealed that MetF and VDAC1 (D9, E203) interact electrostatically. MetF disrupts the IP₃R-GRP75-VDAC1 complex, which plays a key role in stabilizing mitochondria-associated ER membranes (MAMs), by binding to VDAC1. This disruption leads to increased cytosolic calcium levels, thereby contributing to autophagy induction. MetF also decreased the AMP/ATP ratio and activated the AMPK pathway. Cells with genetic knockdown of VDAC1 mimicked the activity of MetF. In conclusion, this study provides new insights into the involvement of MetF in ionic interactions with VDAC1, contributing to its anticancer effects in HCC. These findings help elucidate the diverse biological and pharmacological effects of MetF, particularly its influence on autophagy, as well as the potential of MetF as a therapeutic agent for diseases characterized by VDAC1 overexpression.

Modulators of the G protein-coupled A_2A adenosine receptor (A_2AAR) have been considered promising agents to treat Parkinson's disease, inflammation, cancer, and central nervous system disorders. Herein, we demonstrate that a thiophene modification at the C8 position in the common adenine scaffold converted an A_2AAR agonist into an antagonist. We synthesized and characterized a novel A_2AAR antagonist, 2 (LJ-4517), with K_i = 18.3 nM. X-ray crystallographic structures of 2 in complex with two thermostabilized A_2AAR constructs were solved at 2.05 and 2.80 Å resolutions. In contrast to A_2AAR agonists, which simultaneously interact with both Ser277^7.42 and His278^7.43, 2 only transiently contacts His278^7.43, which can be direct or water-mediated. The n-hexynyl group of 2 extends into an A_2AAR exosite. Structural analysis revealed that the introduced thiophene modification restricted receptor conformational rearrangements required for subsequent activation. This approach can expand the repertoire of adenosine receptor antagonists that can be designed based on available agonist scaffolds.