Combating small molecule aggregation with machine learning study led by Tiago Rodrigues, researcher from iMed.ULisboa at Medicinal Chemistry group, in collaboration with researchers from Instituto de Medicina Molecular, Duke University and Insilico Medicine Taiwan, was published, on 13^th September 2021, in the Cell Reports Physical Science journal.

In this paper, a deep neural network was built to detect false positive hits and potential assay nuisances among small molecules in screening campaigns. The results show that a high percentage of small molecules is likely to aggregate at typical biological screen concentrations (small colloidally aggregating molecules – SCAMs) and that automating their identification is competitive with expert intuition, as assessed in a Turing test. The app§roach suggests that smart computational tools are a viable means to tackle some of the bottlenecks in the development of drug leads.

The manuscript can be read here.

Article:

Lee K, Yang A, Lin Y-C, Reker D, Bernardes G.J.L., Rodrigues T. Combating small molecule aggragation with machine learning. Cell Reports Physical Science 2021; 2 (9): 100573. DOI: 10.1016/j.xcrp.2021.100573