QSPR Models for Prediction of Aqueous Solubility: Exploring the Potency of Randić-type Indices

The development of QSPR models to predict aqueous solubility (logS) is presented. A structurally diverse set of over 1600 compounds with experimentally determined solubility values (AqSolDB database) is used for building the data-driven models based on multiple linear regression (MLR) and artificial neural network (ANN) methods to predict aqueous solubility. Molecular structures are encoded by numerous structural descriptors, including the connectivity index developed by Randić in 1975, and many later derived variations. To evaluate the potency of Randić-like descriptors in the structure-property relationship, we developed models based on two sets of descriptors, first using only Randić-like descriptors calculated with Dragon, and second using 17 commonly applied descriptors available in the AqSolDB database. All models were validated with external prediction sets, with the RMSE ranging from 0.8 to 1.1. Interestingly, the RMSE of predicted LogS values of models based only on the Randić-like descriptors were in average just 0.1 larger than the models with 17 descriptors preselected as suitable for modelling logS. This work is licensed under a Creative Commons Attribution 4.0 International License

Introducing game mechanics into models of e-business

To delo se osredotoča na igrifikacijo: uporabo mehanizmov igre v kontekstih in sistemih, ki sicer niso povezani z igrami. Namen je ugotoviti, kako lahko mehanizme igre uspešno vpeljemo v modele poslovanja na spletu, saj je e-poslovanje postalo ključen del poslovnih procesov današnjih podjetij. Za namen raziskave in ugotavljanje smiselnosti uporabe igrifikacije, je v delu predstavljena SWOT-analiza za to področje, ki ponuja vpogled v prednosti, slabosti, pomanjkljivosti in nevarnosti uporabe igrifikacije v spletnem poslovanju. Cilj implementacije igrifikacije v poslovanje je pogosto povečanje motivacije, tako zaposlenih kot tudi uporabnikov določenih izdelkov in storitev. Ker z igrifikacijo spodbujamo želeno vedenje sodelujočih, je treba pri snovanju igrificiranih rešitev slediti določenim etičnim načelom, zato se delo osredotoča tudi na etične in moralne vidike tega fenomena. Pri snovanju igrificiranih rešitev je ključnega pomena dobro poznavanje uporabnikov in sodelujočih v procesu, saj je treba vedeti, kaj te uporabnike motivira in kateri elementi iger so zanje zabavni, saj lahko le tako ustvarimo zares uspešen igrificiran sistem in zanimivo izkušnjo.This work is focused on gamification: the use of game mechanics in contexts and systems that are otherwise not related to games. The purpose of this paper is to find out how game mechanisms can be successfully introduced into models of online business, as e-business has become an essential part of today’s companies. For the purpose of this research and determining the meaningful use of gamification, this paper presents the SWOT analysis, which provides an insight into the advantages, weaknesses, opportunities and risks of using gamification in e-business. The goal of implementing gamification in business is often to increase motivation of employees and users of certain products and services. Since gamification promotes the manipulation of behavior of participants, it is necessary to follow certain ethical principals when we are designing gamified solutions, therefore the work focuses also on the ethical and moral aspects of this phenomenon. When designing gamified solutions, it is crucial to know the users and participants in the process very well, because it is necessary to know what motivates these users and which elements of games are fun for them. Only then, we can create a truly successful gamified system and interesting experience

Application of Supervised SOM Algorithms in Predicting the Hepatotoxic Potential of Drugs

The hepatotoxic potential of drugs is one of the main reasons why a number of drugs never reach the market or have to be withdrawn from the market. Therefore, the evaluation of the hepatotoxic potential of drugs is an important part of the drug development process. The aim of this work was to evaluate the relative abilities of different supervised self-organizing algorithms in classifying the hepatotoxic potential of drugs. Two modifications of standard counter-propagation training algorithms were proposed to achieve good separation of clusters on the self-organizing map. A series of optimizations were performed using genetic algorithm to select models developed with counter-propagation neural networks, X-Y fused networks, and the two newly proposed algorithms. The cluster separations achieved by the different algorithms were evaluated using a simple measure presented in this paper. Both proposed algorithms showed a better formation of clusters compared to the standard counter-propagation algorithm. The X-Y fused neural network confirmed its high ability to form well-separated clusters. Nevertheless, one of the proposed algorithms came close to its clustering results, which also resulted in a similar number of selected models