Prediction of Multi-Target Networks of Neuroprotective Compounds with Entropy Indices and Synthesis, Assay, and Theoretical Study of New Asymmetric 1,2-Rasagiline Carbamates

In a multi-target complex network, the links (L-ij) represent the interactions between the drug (d(i)) and the target (t(j)), characterized by different experimental measures (K-i, K-m, IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (c(j)). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%-90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally.

Personal identity. A theoretical and experimental analysis

Published as article in: Journal of Economic Methodology, 2010, vol. 17, issue 3, pages 261-275.

Theoretical Characterization of Pentacovalent Oxyphosphorane Intermediate Structures of the Hydrolysis of RNA catalyzed by RNase A

266 p. : il. graf.

Competitive Pressure and Job Interview Lying: A Game Theoretical Analysis

We consider a job contest in which candidates go through interviews (cheap talk) and are subject to reference checks. We show how competitive pressure - increasing the ratio of "good" to "bad" type candi- dates - can lead to a vast increase in lying and in some cases make bad hires more likely. As the number of candidates increases, it becomes harder to in- duce truth-telling. The interview stage becomes redundant if the candidates, a priori, know each others' type or the result of their own reference check. Finally, we show that the employer can bene t from committing not to reject all the applicants.

Use of generalised additive models to categorise continuous variables in clinical prediction

13 P.