Scoring functions are key elements in docking protocols as they approximate the binding affinity of a ligand (usually a small bioactive molecule) by calculating its interaction energy with a biomacromolecule (usually a protein). In this study, we present a pairwise comparison of scoring functions applying a multi-criterion decision-making approach based on InterCriteria analysis (ICrA). As criteria, the five scoring functions implemented in MOE (Molecular Operating Environment) software were selected, and their performance on a set of protein–ligand complexes from the PDBbind database was compared. The following docking outputs were used: the best docking score, the lowest root mean square deviation (RMSD) between the predicted poses and the co-crystallized ligand, the RMSD between the best docking score pose and the co-crystallized ligand, and the docking score of the pose with the lowest RMSD to the co-crystallized ligand. The impact of ICrA thresholds on the relations between the scoring functions was investigated. A correlation analysis was also performed and juxtaposed with the ICrA. Our results reveal the lowest RMSD as the best-performing docking output and two scoring functions (Alpha HB and London dG) as having the highest comparability. The proposed approach can be applied to any other scoring functions and protein–ligand complexes of interest.

The conventional treatment of neurodegenerative diseases (NDDs) is based on the “one molecule—one target” paradigm. To combat the multifactorial nature of NDDs, the focus is now shifted toward the development of small-molecule-based compounds that can modulate more than one protein target, known as “multi-target-directed ligands” (MTDLs), while having low affinity for proteins that are irrelevant for the therapy. The in silico approaches have demonstrated a potential to be a suitable tool for the identification of MTDLs as promising drug candidates with reduction in cost and time for research and development. In this study more than 650,000 compounds were screened by a series of in silico approaches to identify drug-like compounds with predicted activity simultaneously towards three important proteins in the NDDs symptomatic treatment: acetylcholinesterase (AChE), histone deacetylase 2 (HDAC2), and monoamine oxidase B (MAO-B). The compounds with affinities below 5.0 µM for all studied targets were additionally filtered to remove known non-specifically binding or unstable compounds. The selected four hits underwent subsequent refinement through in silico blood-brain barrier penetration estimation, safety evaluation, and molecular dynamics simulations resulting in two hit compounds that constitute a rational basis for further development of multi-target active compounds against NDDs.

The aim of this study was to investigate the chemical space and interactions of natural compounds with sulfotransferases (SULTs) using ligand- and structure-based in silico methods. An in-house library of natural ligands (hormones, neurotransmitters, plant-derived compounds and their metabolites) reported to interact with SULTs was created. Their chemical structures and properties were compared to those of compounds of non-natural (synthetic) origin, known to interact with SULTs. The natural ligands interacting with SULTs were further compared to other natural products for which interactions with SULTs were not known. Various descriptors of the molecular structures were calculated and analyzed. Statistical methods (ANOVA, PCA, and clustering) were used to explore the chemical space of the studied compounds. Similarity search between the compounds in the different groups was performed with the ROCS software. The interactions with SULTs were additionally analyzed by docking into different experimental and modeled conformations of SULT1A1. Natural products with potentially strong interactions with SULTs were outlined. Our results contribute to a better understanding of chemical space and interactions of natural compounds with SULT enzymes and help to outline new potential ligands of these enzymes.

Scoring functions are key elements in docking protocols as they approximate the binding affinity of a ligand (usually a small bioactive molecule) through calculating its interaction energy with a biomacromolecule (usually protein). In this study we present a pairwise comparison of scoring functions applying a multi-criterion decision-making approach based on InterCriteria analysis (ICrA). As criteria, the five scoring functions implemented in MOE (Molecular Operating Environment) software were selected and their performance on a set of protein-ligand complexes from the PDBbind database were compared. The following docking outputs were used: the best docking score; the lowest root-mean-square deviation (RMSD) between the predicted pose and the co-crystalized ligand; the RMSD between the best docking score pose and the co-crystalized ligand; the docking score of the pose with the lowest RMSD to the co-crystalized ligand. The impact of ICrA thresholds on the relations between the scoring functions was investigated. A correlation analysis was also performed and juxtaposed to ICrA. Our results reveal the lowest RMSD as the best performing docking output and two scoring functions (Alpha HB and London dG) as having the highest comparability. The proposed approach can be applied to any other scoring functions and protein-ligand complexes of interest.

This study addresses the growing need for efficient and reliable application of New Approach Methodologies (NAMs) to assess aquatic toxicity of chemicals in response to increasing environmental contamination and regulatory demands. Particular emphasis is placed on in silico methods, especially quantitative structure–activity relationship (QSAR) modeling. Curated and structurally diverse datasets were compiled for representative aquatic organisms from different trophic levels, including the microalga Raphidocelis subcapitata, the crustacean Daphnia magna, and fish species (zebrafish embryo and fathead minnow). The models demonstrated consistently strong predictive performance across the evaluated assays. They were based on interpretable molecular descriptors associated with lipophilicity, polarity, and molecular reactivity. Furthermore, interspecies quantitative structure–activity–activity relationship (QSAAR) models were developed, demonstrating that toxicity data from lower trophic levels, combined with structural descriptors, can effectively predict fish toxicity. These models support cross-species extrapolation and contribute to environmental hazard assessment and regulatory decision-making.