Abstract. The rational development of new drugs is a complex and expensive process, comprising several steps. Typically, it starts by screening databases of small organic molecules for chemical structures with potential of binding to a target receptor and prioritizing the most promising ones. Only a few of these will be selected for biological evaluation and further refinement through chemical synthesis. Despite the accumulated knowledge by pharmaceutical companies that continually improve the process of finding new drugs, a myriad of factors affect the activity of putative candidate molecules in vivo and the propensity for causing adverse and toxic effects is recognized as the major hurdle behind the current "target-rich, lead-poor" scenario. In this study we evaluate the use of several Machine Learning algorithms to find useful rules to the elucidation and prediction of toxicity using 1D and 2D molecular descriptors. The results indicate that: i) Machine Learning algorithms can effectively use 1D molecular descriptors to construct accurate and simple models; ii) extending the set of descriptors to include 2D descriptors improve the accuracy of the models.
SummaryIt has been recognized that the development of new therapeutic drugs is a complex and expensive process. A large number of factors affect the activity in vivo of putative candidate molecules and the propensity for causing adverse and toxic effects is recognized as one of the major hurdles behind the current "target-rich, lead-poor" scenario. Structure-Activity Relationship (SAR) studies, using relational Machine Learning (ML) algorithms, have already been shown to be very useful in the complex process of rational drug design. Despite the ML successes, human expertise is still of the utmost importance in the drug development process. An iterative process and tight integration between the models developed by ML algorithms and the know-how of medicinal chemistry experts would be a very useful symbiotic approach. In this paper we describe a software tool that achieves that goal -iLogCHEM. The tool allows the use of Relational Learners in the task of identifying molecules or molecular fragments with potential to produce toxic effects, and thus help in stream-lining drug design in silico. It also allows the expert to guide the search for useful molecules without the need to know the details of the algorithms used. The models produced by the algorithms may be visualized using a graphical interface, that is of common use amongst researchers in structural biology and medicinal chemistry. The graphical interface enables the expert to provide feedback to the learning system. The developed tool has also facilities to handle the similarity bias typical of large chemical databases. For that purpose the user can filter out similar compounds when assembling a data set. Additionally, we propose ways of providing background knowledge for Relational Learners using the results of Graph Mining algorithms.
Given the problem of school evasion, a problem that impacts educational institutions around the world, this paper presents the application of Bayesian networks to predict the percentage chance of student evasion, in order to assist educational managers in preventing these types of situations. The prediction is performed based on the characteristics of the students, collected from the data base system used by SENAI (Tubarão/SC). It is possible to manipulate these characteristics, by the manager, in order to simulate scenarios to minimize the chances of the student evasion. Through the validation of the results, we have obtained 85.6% of accuracy, which indicates a good performance of the Bayesian network modeled for the system developed.Resumo. Tendo em vista o problema de evasão escolar, problema este que impacta instituições de ensino do mundo inteiro, este artigo apresenta a aplicação de Redes Bayesianas, com o intuito de predizer os percentuais de chance de evasão dos alunos, com o objetivo de auxiliar os gestores educacionais na prevenção destes tipos de situações. A predição é realizada com base nas características dos alunos, coletadas do sistema utilizado pelo SENAI de Tubarão/SC. É possível ainda manipular tais características, por parte do gestor, a fim de simular cenários com o objetivo de minimizar as chances de o aluno evadir. Através da validação dos resultados foi obtido 85,6% de taxa de acerto, o que indica um bom desempenho da rede bayesiana modelada para o sistema desenvolvido.
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