No abstract

No abstract

Preliminary results of a machine learning application concerning computer-aided molecular design applied to drug discovery are presented. The artificial intelligence techniques of machine learning use a sample of active and inactive compounds, which is viewed as a set of positive and negative examples, to allow the induction of a molecular model characterizing the interaction between the compounds and a target molecule. The algorithm is based on a twofold phase. In the first one--the specialization step--the program identifies a number of active/inactive pairs of compounds which appear to be the most useful in order to make the learning process as effective as possible and generates a dictionary of molecular fragments, deemed to be responsible for the activity of the compounds. In the second phase--the generalization step--the fragments thus generated are combined and generalized in order to select the most plausible hypothesis with respect to the sample of compounds. A knowledge base concerning physical and chemical properties is utilized during the inductive process.

No abstract

For inference in complex models, composite likelihood combines genuine likelihoods based on low‐dimensional portions of the data, with weights to be chosen. Optimal weights in composite likelihood may be searched following different routes, leading to a solution only in scalar parameter models. Here, after briefly reviewing the main approaches, we show how to obtain the first‐order optimal weights when using composite likelihood for inference on a scalar parameter in the presence of nuisance parameters. These weights depend on the true parameter value and need to be estimated. Under regularity conditions, the resulting likelihood ratio statistic has the standard asymptotic null distribution and improved local power. Simulation results in multivariate normal models show that estimation of optimal weights maintains the standard approximate null distribution and produces a visible gain in power with respect to constant weights.