APPLICATION OF GILLESPIE ALGORITHM FOR SIMULATING EVOLUTION OF FITNESS OF MICROBIAL POPULATION

In this study we present simulation system based on Gillespie algorithm for generating evolutionary events in the evolution scenario of microbial population. We present Gillespie simulation system adjusted to reproducing experimental data obtained in barcoding studies – experimental techniques in microbiology allowing tracing microbial populations with very high resolution. Gillespie simulation engine is constructed by defining its state vector and rules for its modifications. In order to efficiently simulate barcoded experiment by using Gillespie algorithm we provide modification - binning cells by lineages. Different bins define components of state in the Gillespie algorithm. The elaborated simulation model captures events in microbial population growth including death, division and mutations of cells. The obtained simulation results reflect population behavior, mutation wave and mutation distribution along generations. The elaborated methodology is confronted against literature data of experimental evolution of yeast tracking clones sub-generations. Simulation model was fitted to measurements in experimental data leading to good agreement

Self-organizing neural network for modeling 3D QSAR of colchicinoids

A novel scheme for modeling 3D QSAR has been developed. A method involving mul- tiple self-organizing neural network adjusted to be analyzed by the PLS (partial least squares) analysis was used to model 3D QSAR of the selected colchicinoids. The model obtained allows the identification of some structural determinants of the bio- logical activity of compounds

Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry

The availability of computers has brought novel prospects in drug design. Neural networks (NN) were an early tool that cheminformatics tested for converting data into drugs. However, the initial interest faded for almost two decades. The recent success of Deep Learning (DL) has inspired a renaissance of neural networks for their potential application in deep chemistry. DL targets direct data analysis without any human intervention. Although back-propagation NN is the main algorithm in the DL that is currently being used, unsupervised learning can be even more efficient. We review self-organizing maps (SOM) in mapping molecular representations from the 1990s to the current deep chemistry. We discovered the enormous efficiency of SOM not only for features that could be expected by humans, but also for those that are not trivial to human chemists. We reviewed the DL projects in the current literature, especially unsupervised architectures. DL appears to be efficient in pattern recognition (Deep Face) or chess (Deep Blue). However, an efficient deep chemistry is still a matter for the future. This is because the availability of measured property data in chemistry is still limited

Beware of ligand efficiency (LE) : understanding LE data in modeling structure-activity and structure-economy relationships

Background: On the one hand, ligand efficiency (LE) and the binding efficiency index (BEI), which are binding properties (B) averaged versus the heavy atom count (HAC: LE) or molecular weight (MW: BEI), have recently been declared a novel universal tool for drug design. On the other hand, questions have been raised about the mathematical validity of the LE approach. Results: In fact, neither the critics nor the advocates are precise enough to provide a generally understandable and accepted chemistry of the LE metrics. In particular, this refers to the puzzle of the LE trends for small and large molecules. In this paper, we explain the chemistry and mathematics of the LE type of data. Because LE is a weight metrics related to binding per gram, its hyperbolic decrease with an increasing number of heavy atoms can be easily understood by its 1/MW dependency. Accordingly, we analyzed how this influences the LE trends for ligand-target binding, economic big data or molecular descriptor data. In particular, we compared the trends for the thermodynamic δG data of a series of ligands that interact with 14 different target classes, which were extracted from the BindingDB database with the market prices of a commercial compound library of ca. 2.5 mln synthetic building blocks. Conclusions: An interpretation of LE and BEI that clearly explains the observed trends for these parameters are presented here for the first time. Accordingly, we show that the main misunderstanding of the chemical meaning of the BEI and LE parameters is their interpretation as molecular descriptors that are connected with a single molecule, while binding is a statistical effect in which a population of ligands limits the formation of ligand-receptor complexes. Therefore, LE (BEI) should not be interpreted as a molecular (physicochemical) descriptor that is connected with a single molecule but as a property (binding per gram). Accordingly, the puzzle of the surprising behavior of LE is explained by the 1/MW dependency. This effect clearly explains the hyperbolic LE trend not as a real increase in binding potency but as a physical limitation due to the different population of ligands with different MWs in a 1 g sample available for the formation of ligand-receptor complexes

The use of MoStBioDat for rapid screening of molecular diversity

MoStBioDat is a uniform data storage and extraction system with an extensive array of tools for structural similarity measures and pattern matching which is essential to facilitate the drug discovery process. Structure-based database screening has recently become a common and efficient technique in early stages of the drug development, shifting the emphasis from rational drug design into the probability domain of more or less random discovery. The virtual ligand screening (VLS), an approach based on high-throughput flexible docking, samples a virtually infinite molecular diversity of chemical libraries increasing the concentration of molecules with high binding affinity. The rapid process of subsequent examination of a large number of molecules in order to optimize the molecular diversity is an attractive alternative to the traditional methods of lead discovery. This paper presents the application of the MoStBioDat package not only as a data management platform but mainly in substructure searching. In particular, examples of the applications of MoStBioDat are discussed and analyze

Szarawara–Kozik’s temperature criterion in the context of three-parameter equation for modeling ammonia or methanol decomposition during heterogenous catalysis

Szarawara–Kozik’s temperature criterion, suggested many years ago, has been reinterpreted as three-parameter fitting equation. We demonstrated interpretation of the chemical reactions of ammonia and methanol catalytic decomposition (to produce syngas and hydrogen) by associating two parameters with the activation energy and the average enthalpy of reaction for the equilibrium conversion degrees. It was proved that the three-parameter equation can be applicable to studying a wide variety of catalytic/enzymatic processes in isothermal conditions

Self-organizing neural networks for modeling robust 3D and 4D QSAR: application to dihydrofolate reductase inhibitors

We have used SOM and grid 3D and 4D QSAR schemes for modeling the activity of a series of dihydrofolate reductase inhibitors. Careful analysis of the performance and external predictivities proves that this method can provide an efficient inhibition model

Molecular descriptor data explain market prices of a large commercial chemical compound library

The relationship between the structure and a property of a chemical compound is an essential concept in chemistry guiding, for example, drug design. Actually, however, we need economic considerations to fully understand the fate of drugs on the market. We are performing here for the first time the exploration of quantitative structure-economy relationships (QSER) for a large dataset of a commercial building block library of over 2.2 million chemicals. This investigation provided molecular statistics that shows that on average what we are paying for is the quantity of matter. On the other side, the influence of synthetic availability scores is also revealed. Finally, we are buying substances by looking at the molecular graphs or molecular formulas. Thus, those molecules that have a higher number of atoms look more attractive and are, on average, also more expensive. Our study shows how data binning could be used as an informative method when analyzing big data in chemistry

The role of oxidative stress in activity of anticancer thiosemicarbazones

Thiosemicarbazones are chelators of transition metals such as iron or copper whose anticancer potency is intensively investigated. Although two compounds from this class have entered clinical trials, their precise mechanism of action is still unknown. Recent studies have suggested the mobilization of the iron ions from a cell, as well as the inhibition of ribonucleotide reductase, and the formation of reactive oxygen species. The complexity and vague nature of this mechanism not only impedes a more rational design of novel compounds, but also the further development of those that are highly active that are already in the preclinical phase. In the current work, a series of highly active thiosemicarbazones was studied for their antiproliferative activity in vitro. Our experiments indicate that these complexes have ionophoric properties and redox activity. They appeared to be very effective generating reactive oxygen species and deregulating the antioxidative potential of a cell. Moreover, the genes that are responsible for antioxidant capacity were considerably deregulated, which led to the induction of apoptosis and cell cycle arrest. On the other hand, good intercalating properties of the studied compounds may explain their ability to cleave DNA strands and to also poison related enzymes through the formation of reactive oxygen species. These findings may help to explain the particularly high selectivity that they have over normal cells, which generally have a stronger redox equilibrium

Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem

(1) Background: Properties and descriptors are two forms of molecular in silico representations. Properties can be further divided into functional, e.g., catalyst or drug activity, and material, e.g., X-ray crystal data. Millions of real measured functional property records are available for drugs or drug candidates in online databases. In contrast, there is not a single database that registers a real conversion, TON or TOF data for catalysts. All of the data are molecular descriptors or material properties, which are mainly of a calculation origin. (2) Results: Here, we explain the reason for this. We reviewed the data handling and sharing problems in the design and discovery of catalyst candidates particularly, material informatics and catalyst design, structural coding, data collection and validation, infrastructure for catalyst design and the online databases for catalyst design. (3) Conclusions: Material design requires a property prediction step. This can only be achieved based on the registered real property measurement. In reality, in catalyst design and discovery, we can observe either a severe functional property deficit or even property famine