Enhancing HDAC Inhibitor Screening: Addressing Zinc Parameterization and Ligand Protonation in Docking Studies.

Precise binding free-energy predictions for ligands targeting metalloproteins, especially zinc-containing histone deacetylase (HDAC) enzymes, require specialized computational approaches due to the unique interactions at metal-binding sites. This study evaluates a docking algorithm optimized for zinc coordination to determine whether it could accurately differentiate between protonated and deprotonated states of hydroxamic acid ligands, a key functional group in HDAC inhibitors (HDACi). By systematically analyzing both protonation states, we sought to identify which state produces docking poses and binding energy estimates most closely aligned with experimental values.

Novel sigma 1-antagonists with -(+)-normetazocine scaffold: synthesis, molecular modeling, and antinociceptive effect.

Inflammatory pain represents one of the unmet clinical needs for patients, as conventional therapies cause several side effects. Recently, new targets involved in inflammatory pain modulation have been identified, including the sigma-1 receptor (σ1R). Selective σ1R antagonists have demonstrated analgesic efficacy in acute and chronic inflammatory pain models.

Design, Synthesis, and Evaluation of Novel (-)-cis-N-Normetazocine Derivatives: In Vitro and Molecular Modeling Insights.

Suitable structural modifications of the functional groups at N-substituent of (-)-cis-N-normetazocine nucleus modulate the affinity and activity profile of related ligands toward opioid receptors. Our research group has developed several compounds and the most interesting ligands, LP1 and LP2, exhibited a dual-target profile for mu-opioid receptor (MOR) and delta-opioid receptor (DOR). Recent structure-affinity relationship studies led to the discovery of novel LP2 analogs (compounds 1 and 2), which demonstrated high MOR affinity in the nanomolar range.

Spectral operator representations.

Machine learning in atomistic materials science has grown to become a powerful tool, with most approaches focusing on atomic geometry, typically decomposed into local atomic environments. This approach, while well-suited for machine-learned interatomic potentials, is conceptually at odds with learning complex intrinsic properties of materials, often driven by spectral properties commonly represented in reciprocal space (e.g.

Development of selective sigma-1 receptor ligands with antiallodynic activity: A focus on piperidine and piperazine scaffolds.

The design and synthesis of a series of piperidine and piperazine-based derivatives as selective sigma receptor (SR) ligands associated with analgesic activity, are the focus of this work. In this study, affinities at S1R and S2R were measured, and molecular modeling studies were performed to investigate the binding pose features. The most promising compounds were subjected to in vitro toxicity testing and subsequently screened for in vivo analgesic properties.