The patent, titled ‘Molecular Property Modeling Using Ranking’, describes methods for using advanced machine-learning technology to design small molecule compounds in silico that meet specific candidate drug profiles.
Numerate has said that the ranking technology enables the use of new kinds of experimental data beyond those accessible by other statistical techniques.
To make discoveries, Numerate scientists run their platform on Amazon’s Elastic Compute Cloud (EC2), allowing them to search through spaces of hundreds of millions of compounds to identify those with the highest probability of activity against a specific target. The compounds are then synthesized and their properties verified in the laboratory.
Nigel Duffy, chief technology officer of Numerate, said: “To make accurate predictions, we have developed ways to address key issues, including the bias inherent in biological data, and the experimental noise typical in assay results. As a result, we are able to deliver novel compounds that meet pre-specified objectives and perform in the laboratory as we predict they will.”
Guido Lanza, CEO of Numerate, said: “The only way to improve the efficiency of drug discovery significantly is with a new approach that lets the data drive the design and addresses these multiple objectives simultaneously. Numerate’s platform provides the basis for just such a paradigm shift.
We are using it today to substantially reduce the time and cost of designing and delivering lead-stage, small molecule drug candidates to our partners. This patent significantly extends our leadership position in technology for predicting the properties of small molecule drug candidates."