Evaluation of machine-learning methods for ligand-based virtual screening

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed

THERMAL MODEL FOR THE DESORPTION OF (MOLECULAR) IONS INDUCED BY MeV HEAVY IONS

Un modèle thermique a été développé pour décrire la désorption d'ions secondaires par des ions lourds de plusieurs MeV. Le modèle est capable de reproduire la dépendance du rendement d'émission avec l'énergie et la charge initiale des ions de plusieurs MeV.A thermal model is developed to describe the desorption of ions by MeV heavy ions. The model is able to reproduce the dependence of measured secondary ion yields on the energy and the initial charge state of the MeV ions