Is 57% enough?

Interesting paper in J. Chem. Inf. Model. on Predicting Key Example Compounds in Competitors’ Patent Applications Using Structural Information Alone. That’s actually a pretty cool concept and something many pharma companies are either already doing or would be quite interested in.

The authors method is based on the assumption that medicinal chemists usually carry out extensive structure-activity relationship (SAR) studies around key compounds. Using that assumption, the method identifies compounds located at the centers of densely populated regions in the chemical space of patent examples (represented by Extended Connectivity Fingerprints (ECFPs)). The authors had a success rate of 57% for their test sets. Call me old and jaded, but percentages like that just don’t impress me anymore. I think the entire informatics/computational modeling field needs to understand that for methods to be applicable and accepted, they need to radically reduce time or error rates or both. During the days that I was evangelizing physics based approaches for protein-ligand evaluation, I remember someone from a big pharma company telling me. You have to achieve throughput better than X compounds a week, because otherwise, we’re just going to make them. You had to be significantly better than their med chemists WITH sufficient quality.

Technorati Tags: Cheminformatics, Drug Discovery