Fermentation processes by nature are complex, time‐varying, and highly nonlinear. As dynamic systems their modeling and further high‐quality control are a serious challenge. The conventional optimization methods cannot overcome the fermentation processes peculiarities and do not lead to a satisfying solution. As an alternative, genetic algorithms as a stochastic global optimization method can be applied. For the purpose of parameter identification of a fed‐batch cultivation ofS. cerevisiaealtogether four kinds of simple and four kinds of multipopulation genetic algorithms have been considered. Each of them is characterized with a different sequence of implementation of main genetic operators, namely, selection, crossover, and mutation. The influence of the most important genetic algorithm parameters—generation gap, crossover, and mutation rates has—been investigated too. Among the considered genetic algorithm parameters, generation gap influences most significantly the algorithm convergence time, saving up to 40% of time without affecting the model accuracy.

Alzheimer’s disease (AD) is considered a complex neurodegenerative condition which warrants the development of multitargeted drugs to tackle the key pathogenetic mechanisms of the disease. In this study, two novel series of melatonin- and donepezil-based hybrid molecules with hydrazone (3a–r) or sulfonyl hydrazone (5a–l) fragments were designed, synthesized, and evaluated as multifunctional ligands against AD-related neurodegenerative mechanisms. Two lead compounds (3c and 3d) exhibited a well-balanced multifunctional profile, demonstrating intriguing acetylcholinesterase (AChE) inhibition, promising antioxidant activity assessed by DPPH, ABTS, and FRAP methods, as well as the inhibition of lipid peroxidation in the linoleic acid system. Compound 3n, possessing two indole scaffolds, showed the highest activity against butyrylcholinesterase (BChE) and a high selectivity index (SI = 47.34), as well as a pronounced protective effect in H₂O₂-induced oxidative stress in SH-SY5Y cells. Moreover, compounds 3c, 3d, and 3n showed low neurotoxicity against malignant neuroblastoma cell lines of human (SH-SY5Y) and murine (Neuro-2a) origin, as well as normal murine fibroblast cells (CCL-1) that indicate the in vitro biocompatibility of the experimental compounds. Furthermore, compounds 3c, 3d, and 3n were capable of penetrating the blood–brain barrier (BBB) in the experimental PAMPA-BBB study. The molecular docking showed that compound 3c could act as a ligand to both MT1 and MT2 receptors, as well as to AchE and BchE enzymes. Taken together, those results outline compounds 3c, 3d, and 3n as promising prototypes in the search of innovative compounds for the treatment of AD-associated neurodegeneration with oxidative stress. This study demonstrates that hydrazone derivatives with melatonin and donepezil are appropriate for further development of new AChE/BChE inhibitory agents.

Background: Discovery of new bioactive molecules that could enter drug discovery programs or that could serve as chemical probes is a very complex and costly endeavor. Structure-based and ligand-based in silico screening approaches are nowadays extensively used to complement experimental screening approaches in order to increase the effectiveness of the process and facilitating the screening of thousands or millions of small molecules against a biomolecular target. Both in silico screening methods require as input a suitable chemical compound collection and most often the 3D structure of the small molecules has to be generated since compounds are usually delivered in 1D SMILES, CANSMILES or in 2D SDF formats. Results: Here, we describe the new open source program DG-AMMOS which allows the generation of the 3D conformation of small molecules using Distance Geometry and their energy minimization via Automated Molecular Mechanics Optimization. The program is validated on the Astex dataset, the ChemBridge Diversity database and on a number of small molecules with known crystal structures extracted from the Cambridge Structural Database. A comparison with the free program Balloon and the well-known commercial program Omega generating the 3D of small molecules is carried out. The results show that the new free program DG-AMMOS is a very efficient 3D structure generator engine. Conclusion: DG-AMMOS provides fast, automated and reliable access to the generation of 3D conformation of small molecules and facilitates the preparation of a compound collection prior to high-throughput virtual screening computations. The validation of DG-AMMOS on several different datasets proves that generated structures are generally of equal quality or sometimes better than structures obtained by other tested methods.

The cytotoxicity and microbicidal capacity of seven organic solvents commonly applied for studying plant extracts and bioactive compounds were systematically investigated based on international standards. Four cell lines of normal (CCL-1, HaCaT) or tumor (A-375, A-431) tissue origin, seven bacterial and one fungal strain were used. The impact of the least toxic solvents in the determination of in vitro cytotoxicity was evaluated using a standardized extract from Vaccinium macrocarpon containing 54.2% v/v proanthocyanidins (CystiCran^®). The solvents ethanol, methoxyethanol and polyethylene glycol were the least cytotoxic to all cell lines, with a maximum tolerated concentration (MTC) between 1 and 2% v/v. Ethanol, methanol and polyethylene glycol were mostly suitable for antimicrobial susceptibility testing, with minimum inhibitory concentrations (MICs) ≥ 25% v/v. The MTC values of the solvents dimethyl sulfoxide, dimethoxyethane and dimethylformamide varied from 0.03% to 1.09% v/v. The MICs of dimethyl sulfoxide, methoxyethanol and dimethoxyethane were in the range of 3.125–25% v/v. The cytotoxic effects of CystiCran^® on eukaryotic cell lines were directly proportional to the superimposed effect of the solvents used. The results of this study can be useful for selecting the appropriate solvents for in vitro estimation of the cytotoxic and growth inhibitory effects of bioactive molecules in eukaryotic and prokaryotic cells.

Background: Virtual or in silico ligand screening combined with other computational methods is one of the most promising methods to search for new lead compounds, thereby greatly assisting the drug discovery process. Despite considerable progresses made in virtual screening methodologies, available computer programs do not easily address problems such as: structural optimization of compounds in a screening library, receptor flexibility/induced-fit, and accurate prediction of protein-ligand interactions. It has been shown that structural optimization of chemical compounds and that post-docking optimization in multi-step structure-based virtual screening approaches help to further improve the overall efficiency of the methods. To address some of these points, we developed the program AMMOS for refining both, the 3D structures of the small molecules present in chemical libraries and the predicted receptor-ligand complexes through allowing partial to full atom flexibility through molecular mechanics optimization. Results: The program AMMOS carries out an automatic procedure that allows for the structural refinement of compound collections and energy minimization of protein-ligand complexes using the open source program AMMP. The performance of our package was evaluated by comparing the structures of small chemical entities minimized by AMMOS with those minimized with the Tripos and MMFF94s force fields. Next, AMMOS was used for full flexible minimization of protein-ligands complexes obtained from a mutli-step virtual screening. Enrichment studies of the selected pre-docked complexes containing 60% of the initially added inhibitors were carried out with or without final AMMOS minimization on two protein targets having different binding pocket properties. AMMOS was able to improve the enrichment after the pre-docking stage with 40 to 60% of the initially added active compounds found in the top 3% to 5% of the entire compound collection. Conclusion: The open source AMMOS program can be helpful in a broad range of in silico drug design studies such as optimization of small molecules or energy minimization of pre-docked protein-ligand complexes. Our enrichment study suggests that AMMOS, designed to minimize a large number of ligands pre-docked in a protein target, can successfully be applied in a final post-processing step and that it can take into account some receptor flexibility within the binding site area.