A generalizable definition of chemical similarity for read-across.
BACKGROUND
Methods that provide a measure of chemical similarity are strongly relevant in several fields of chemoinformatics as they allow to predict the molecular behavior and fate of structurally close compounds. One common application of chemical similarity measurements, based on the principle that similar molecules have similar properties, is the read-across approach, where an estimation of a specific endpoint for a chemical is provided using experimental data available from highly similar compounds.


RESULTS
This paper reports the comparison of multiple combinations of binary fingerprints and similarity metrics for computing the chemical similarity in the context of two different applications of the read-across technique.


CONCLUSIONS
Our analysis demonstrates that the classical similarity measurements can be improved with a generalizable model of similarity. The proposed approach has already been used to build similarity indices in two open-source software tools (CAESAR and VEGA) that make several QSAR models available. In these tools, the similarity index plays a key role for the assessment of the applicability domain.
CRS4 Biomedicine, Parco Polaris, Loc. Pixinamanna, Pula, Italy ; Istituto di Ricerca Genetica e Biomedica (IRGB), CNR, Monserrato, Italy.
Matteo
M
Floris
IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy.
Alberto
A
Manganaro
Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Bari, Italy.
Orazio
O
Nicolotti
Istituto di Ricerca Genetica e Biomedica (IRGB), CNR, Monserrato, Italy.
Ricardo
R
Medda
Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Bari, Italy.
Giuseppe Felice
GF
Mangiatordi
IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy.
Emilio
E
Benfenati
Applicability domain; Chemical similarity; QSAR; Read-across