Absci recently detailed two of its drug discovery machine learning (ML) breakthroughs, and presented validation of its in-silico lead optimization models. Absci’s first breakthrough is an ML model for quantitative prediction of antibody target affinity, enabling computational predictions of binding strength. The second is an ML model designed to score the ‘naturalness’ of antibody variants . . .

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