RESUMO
Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water-octanol distribution coefficient (logD) for chemical compounds, aiding drug discovery projects. Using ACD/logD data for 1.6 million compounds from the ChEMBL database, models are created and evaluated by a support-vector machine with a linear kernel using conformal prediction methodology, outputting prediction intervals at a specified confidence level. The resulting model shows a predictive ability of [Formula: see text] and with the best performing nonconformity measure having median prediction interval of [Formula: see text] log units at 80% confidence and [Formula: see text] log units at 90% confidence. The model is available as an online service via an OpenAPI interface, a web page with a molecular editor, and we also publish predictive values at 90% confidence level for 91 M PubChem structures in RDF format for download and as an URI resolver service.
RESUMO
The exoproteases of Staphylococcus aureus have been proposed as virulence factors during S. aureus infections. To investigate this, we used the wild-type S. aureus strain 8325-4 and its mutants devoid of aureolysin, serine protease, and cysteine protease, respectively, in a well-established model of septic arthritis in mice. The inactivation of the exoprotease genes did not affect the frequency or the severity of joint disease. We conclude that in the model of haematogenously spread staphylococcal arthritis, the bacterial proteases studied do not act as virulence factors.
