First look: Guide your compound design strategy with new visual, industry-leading affinity predictions
Accurate predictions of binding affinity are the holy grail of early-phase discovery, enabling teams to significantly reduce the synthesis and…
In this webinar, we explore how the limitations of pharmaceutical data can impact conventional predictive model building. Our speakers, Julian Levell (Constellation pharmaceuticals), Ben Irwin and matt Segall (Optibrium) demonstrate how the deep learning imputation algorithm underlying our Cerella platform, overcomes these challenges.
Walking through case studies from a collaboration between Constellation Pharmaceuticals and Optibrium on applying deep learning imputation to project data, we see the impact our methods can bring at all stages from early screening of datasets over temporal validation to later stage models, larger applications and the potential of this cutting-edge technology for future projects.
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