Validation and comparison with other technologies of the SAFAN-ISP technology for in silico profiling of small molecules and peptides.



S.A.F.AN. BIOINFORMATICS is a small bioinformatic company based in Turin (Italy) with the mission of reducing costs and increasing efficiency of bringing new drugs to market. It was born in 2004 after a business plan competition organized by the Politecnico of Turin. Our proprietary technology  SAFAN-ISP is  a new fragment based in-silico screening profiling  technology.

SAFAN-ISP works now on small molecules SAFAN-ISPSM and on peptides SAFAN-ISPPEPTIDES.

It has in silico times and costs but its reliability is similar to an experiment.

Our customers  can  use SAFAN-ISP for:

  1. Drug repositioning using the target: disease database
  2. Side Effect prediction using target: side effect database
  3. Target identification in phenotypic screening outputs.

4- Potential therapeutic indications for natural compounds.

S.A.F.AN. BIOINFORMATICS participated in the H2020 CaSR Biomedicine European Training Network and  in the Newprot FP7 European project.



SAFAN-ISP can be classified as a ligand based technology (LBT) predicting data can be directly compared with the experimental data and scaled among themselves. The principle of LBT is that structurally similar molecules are likely to have similar properties. It needs structural information and bioactivity data for small molecules and a way to compute molecular similarity.

Molecular similarity is computed by comparing molecular descriptors for different compounds.SAFAN-ISP uses innovative molecular descriptors and an innovative way to compute similarity.

New computational methods need a very accurate validation process in order to be accepted by the pharmaceutical companies. Compounds and bioactivities described in public databases not overlapping with SAFAN-ISP database can be used to validate it.

Work Plan

  1. Selection of BindingDB data not overlapping with SAFAN-ISP database
  2. Running SAFAN-ISP on the selected compounds
  3. Statistical analysis of affinities obtained from point 2

Business Partner

EOSC Service provider

Supporting project