Equinox Pharma

for structural biology and drug discovery

INDDEx Advantages

INDDEx^TM gives a 50% prediction rate

We have confirmed our results in vitro on three targets and have several compound hits that will be the basis for hit-to-lead and lead optimisation during the next phase of development. Our proof of concept shows that for antagonists we can achieve a 50% rate of prediction of in vitro hits using our computational techniques. This is similar to that achieved in high throughput screening (which is much more expensive and can only involve much smaller numbers of compounds) and compares very favourably with competing in silico approaches which provide between a 5-15% hit rate.

75% hits are new chemotypes

The strength of Equinox’s INDDEx^TM technology is in the structural rules it learns. The number of rules depends on the number of molecules in the training set as well as the diversity of molecules in this set (from a few hundreds to a few thousands). The rules are generated on fragments of the overall molecular structure rather than the structure itself. This allows INDDEx^TM to identify new chemotypes in which these fragments lie. This contrasts with the rules generated by many other systems that are based upon the complete structure and therefore tend to identify similar structures. We have shown that up to 75% represent new chemotypes. Conventionally medicinal chemists consider a compound to represent a new chemotype is it has a Tanimoto coefficient (a measure of structural similarity) of greater than 70%.

Cost effective hit finding

The clear advantage of using an in silico approach is that large numbers of compounds can be screened rapidly and cheaply – for example we are able to screen the 4 million compounds that make up the ‘Zinc’ database in just a few days. And the whole process of building a model, screening in silico and getting in vitro confirmation can be achieved within less than six months.

Application in hit-to-lead and lead optimisation

The QSAR rules can be used to guide medicinal chemists in the hit-to-lead and lead optimisation programme by allowing ‘what if?’ questions to be posed regarding potential chemical substitutions and other structural modifications. Whilst this would not replace the need for synthetic medicinal chemistry, it will make the process both more efficient and more productive. Other rules based upon key ‘drugability’ properties such as solubility, metabolism and distribution can be derived. These will also be used to guide lead selection and optimisation.

INDDEx^TM does not require target structure data

INDDEx^TM can be applied to all drug targets where SAR exists and does not require knowledge of the 3D structure of the target. This is particularly relevant to GPCR’s where target-structure data is very limited.

INDDEx^TM has other important advantages

• Learns from both active and inactive compounds
• Can manage large number of active compounds
• Can manage diverse set of active compounds
• Flexibility in defining features to generate the predictive QSAR
• Does not require a global superposition
• Can automatically identify more than one binding mode
• The strategy can readily be applied to a broad range of chemoinformatics problems including incorporating knowledge of the receptor