Structure-Guided Identification of 2-Arylbenzothiazole as disruptors for the Oncogenic MTDH-SND1 Protein-Protein Interaction.

Structure-based discovery of diphenylpyrazines as disruptors of the Skp2–Cks1 protein–protein interaction: computational insights and biochemical validation

Findings validate the diphenylpyrazine scaffold as a promising chemotype for Skp2–Cks1 inhibition and identify C3 as a strong lead for further optimization.

Fragment-Based Discovery of BRD4-BD2 Selective Binders: Biophysical Validation and Structural Insights for Domain-Selective Inhibitor Design

BRD4, a member of the bromodomain and extra-terminal (BET) family of epigenetic readers, plays a central role in transcriptional regulation and is implicated in a wide spectrum of diseases. Although BRD4 inhibitors targeting both bromodomains have advanced into clinical studies, their development and therapeutic use are limited by dose-limiting toxicities associated with nonselective BET inhibition. These limitations underscore the need for domain-selective targeting strategies. In this study, we performed fragment-based screening of a 200-compound diversity library against BRD4-BD2, identifying three chemically distinct hits: CCBT-T-226 (methyl 2-(2-(3,5-dimethylisoxazol-4-yl)acetamido)-5-propylthiazole-4-carboxylate), CCBT-F-70 (2-(benzylamino)pyrimidin-4(3H)-one), and CCBT-F-182 (3-(8-methyl-3,8a-dihydroimidazo[1,2-a]pyridin-2-yl)aniline). These fragments were validated as binders using TSA, ITC, STD-NMR, and 1H-15N HSQC NMR. Among them, CCBT-T-226 and CCBT-F-182 exhibited measurable BD2-over-BD1 selectivity, with CCBT-F-182 showing a pronounced domain preference, and CCBT-T-226 demonstrating approximately 7-fold selectivity for BD2 over BD1. X-ray crystallography of the BRD4-BD2–CCBT-F-70 complex, together with cofolding models for CCBT-T-226 and CCBT-F-182, provides a structural framework for the rational, structure-guided development of potent and selective BRD4-BD2 inhibitors.

Deciphering the interaction landscape of NDM-1 with a novel β-lactam-based inhibitor: implications for rational drug design

In silico evaluation of novel metabolites from Tricholoma pardinum: Insights into drug-likeness, toxicity and target interactions via molecular docking and dynamics.

Six previously uncharacterized metabolites isolated from the poisonous mushroom Tricholoma pardinum are investigated using an integrated in silico approach to evaluate their therapeutic potential, highlighting the potential of metabolites from T. pardinum as novel scaffolds for developing anticancer agents targeting PARP1 and PIP4K2γ.

In Silico Optimization of Dihydropteridine Derivatives Targeting PLK1 for Glioblastoma: An Integrated QSAR, Docking, and Molecular Dynamics Study

Computational findings support the prioritization of X14 for further experimental validation in glioblastoma therapy, and generally favorable ADMET profiles were observed, hepatotoxicity alerts were predicted for all compounds, which represents an important limitation supporting the prioritization of X14.

Structure-Based Virtual Screening and De Novo Design Guided by 3D-QSAR Triage for the Identification of Nanomolar STK3 Inhibitors

Serine/threonine kinase 3 (STK3) is a core kinase in the Hippo signaling pathway that regulates cell proliferation, differentiation, and apoptosis. Dysregulation of this pathway is linked to various cancers and immune disorders, emphasizing STK3 as a promising therapeutic target for diseases that involve abnormal cell growth. Despite their therapeutic relevance, only a limited number of potent STK3 inhibitors have been reported, likely due to the difficulty in designing molecules that can bind tightly to the ATP-binding site of STK3. Using the 2-(2-amino-5-phenylpyrimidin-4-yl)-5-methoxyphenol (APPP) scaffold identified through virtual screening, we discovered a series of novel, potent STK3 inhibitors with half-maximal inhibitory concentration (IC50) values as low as 3.52 nM. This was made possible through an integrated molecular design strategy that combined structure-based de novo design with a predictive 3D-QSAR model to estimate IC50 values and prioritize compounds for synthesis. This approach improved the prioritization of compounds for synthesis by enhancing binding energy discrimination beyond the limitations of scoring functions in de novo design. Strategic derivatization at four defined positions on the APPP core enhanced interactions in the ATP-binding site of STK3, resulting in 18 of the 25 synthesized derivatives exhibiting IC50 values below 100 nM. These findings are particularly significant given the scarcity of known STK3 inhibitors with comparable potency, while also indicating that 3D-QSAR modeling contributed as a complementary triage tool rather than a standalone driver of lead discovery. This advancement stems from the use of quantum-mechanically derived 3D structural alignments and the incorporation of electrostatic potential distributions as quantitative molecular descriptors. Taken together, this study establishes a robust design paradigm for the rapid development of highly potent kinase inhibitors through structure-based computational strategies.