An implicit scoring model is capable of capturing ‘chemical intuition’, researchers from Novartis and Microsoft write in Nature Communications. They see their tool as a valuable addition to allow for a more efficient selection of drug leads.  

In chemistry, many problems can be tackled using a rational approach. Theoretical underpinning, well-established physico-chemical relationships and a wealth of literature and experimental data are all available. Even so, these do no fully explain all the choices chemists make on a daily basis. Accumulated experience, often expressed as ‘intuition’, also plays a major role.   

Formalising  

It is hard to make that intuition explicit, but that is exactly what researchers from Novartis and Microsoft had in mind. Because if you can reveal underlying patterns, you can formalise that ‘hidden’ knowledge and use it for new, predictive machine learning techniques that can make the selection and optimisation process of new drug leads more efficient.