(1) Background: In silico approaches to rational drug design are among the fastest evolving ones. Depending on the available structural information for the biomacromolecule and the small molecule, the in silico methods are classified as ligand- and structure-based. The latter predict ligand–receptor binding using 3D structures of both molecules, whose computational simulation is referred to as molecular docking. It aims at estimating the binding affinity (approximated by scoring function) and the ligand binding pose in the receptor’s active site, which postulates a key role of the scoring functions in molecular docking algorithms. This study focuses on the performance of different types of scoring functions implemented in molecular modelling software packages. (2) Methods: An InterCriteria analysis (ICrA) was applied to assess the performance of the scoring functions available in MOE, GOLD, SeeSAR, and AutoDock Vina software platforms. The InterCriteria analysis was developed to distinguish possible relations between pairs of criteria when multiple objects are considered. All 12 investigated scoring functions were tested by docking a set of protease inhibitors in the binding sites of two protein targets. The dataset consisted of 88 benzamidine-type compounds with experimentally measured inhibitory constants for thrombin and trypsin, which allows for the objective assessment of the scoring functions performance. The results generated by the molecular docking were subjected to ICrA in order to analyze both docking energies as approximations of the binding affinities and RMSDs (root-mean-square deviation) as measures of the experimental binding pose proximity between the compounds and the co-crystalized ligand, based on the atoms in the common scaffold. (3) Results: The results obtained for the best poses, the average of the best 5 or 30 poses retained after docking, were analyzed. A comparison with the experimentally observed inhibitory effects was also performed. The InterCriteria analysis application confirms that the performance of the scoring functions for the same dataset of ligands depends on the studied protein. The analysis reveals that none of the studied scoring functions is a good predictor of the compounds’ binding affinities for the considered protein targets. (4) Conclusion: In terms of this analysis, the investigated scoring functions do not produce equivalent results, which suggests the necessity for their combined use in consensus docking studies.

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 water cycle algorithm (WCA), which is a metaheuristic method inspired by the movements of rivers and streams towards the sea in nature, has been adapted and applied here for the first time for solving such a challenging problem as the parameter identification of fermentation process (FP) models. Bacteria and yeast are chosen as representatives of FP models that are subjected to parameter identification due to their impact in different industrial fields. In addition, WCA is considered in comparison with the genetic algorithm (GA), which is another population-based technique that has been proved to be a promising alternative of conventional optimisation methods. The obtained results have been thoroughly analysed in order to outline the advantages and disadvantages of each algorithm when solving such a complicated real-world task. A discussion and a comparative analysis of both metaheuristic algorithms reveal the impact of WCA on model identification problems and show that the newly applied WCA outperforms GA with regard to the model accuracy.

Electromyography (EMG) is a widely used method for estimating muscle activity and could help in understanding how muscles interact with each other and affect human movement control. To detect muscle interactions during elbow flexion and extension, a recently developed InterCriteria Analysis (ICrA) based on the mathematical formalisms of index matrices and intuitionistic fuzzy sets is applied. ICrA has had numerous implementations in different fields, including biomedicine and quality of life; however, this is the first time the approach has been used for establishing muscle interactions. Six human upper arm large surface muscles or parts of muscles responsible for flexion and extension in shoulder and elbow joints were selected. Surface EMG signals were recorded from four one-joint (pars clavicularis and pars spinata of m. deltoideus [DELcla and DELspi, respectively], m. brachialis [BRA], and m. anconeus [ANC]) and two two-joint (m. biceps brachii [BIC] and m. triceps brachii-caput longum [TRI]) muscles. The outcomes from ten healthy subjects performing flexion and extension movements in the sagittal plane at four speeds with and without additional load are implemented in this study. When ICrA was applied to examine the two different movements, the BIC–BRA muscle interaction was distinguished during flexion. On the other hand, when the ten subjects were observed, four interacting muscle pairs, namely DELcla-DELspi, BIC-TRI, BIC-BRA, and TRI-BRA, were detected. The results obtained after the ICrA application confirmed the expectations that the investigated muscles contribute differently to the human upper arm movements when the flexion and extension velocities are changed, or a load is added.

In this paper, a new hybrid MpGA-CS is elaborated between multi-population genetic algorithm (MpGA) and cuckoo search (CS) metaheuristic. Developed MpGA-CS has been adapted and tested consequently for modelling of bacteria and yeast fermentation processes (FP), due to their great impact on different industrial areas. In parallel, classic MpGA, classic CS, and a new hybrid MpGA-CS have been separately applied for parameter identification of E. coli and S. cerevisiae FP models. For completeness, the newly elaborated MpGA-CS has been compared with two additional nature-inspired algorithms; namely, artificial bee colony algorithm (ABC) and water cycle algorithm (WCA). The comparison has been carried out based on numerical and statistical tests, such as ANOVA, Friedman, and Wilcoxon tests. The obtained results show that the hybrid metaheuristic MpGA-CS, presented herein for the first time, has been distinguished as the most reliable among the investigated algorithms to further save computational resources.