Accelerating lead discovery via completion of DMTA cycles: multi-property optimization powered by ultra-large virtual libraries and generative AI

Forschungsstelle

INNOSUISSE

Projektnummer

128.872 INT-ICT

Projekttitel

Accelerating lead discovery via completion of DMTA cycles: multi-property optimization powered by ultra-large virtual libraries and generative AI

Projekttitel Englisch

Accelerating lead discovery via completion of DMTA cycles: multi-property optimization powered by ultra-large virtual libraries and generative AI

Texte zu diesem Projekt

	Deutsch	Französisch	Italienisch	Englisch
Schlüsselwörter	-	-	-
Abstract	-	-	-

Kategorie	Text
Schlüsselwörter (Englisch)	ICT, Machine learning, Chemical biology, Automation
Abstract (Englisch)	This project accelerates hit identification and lead optimization in drug design by integrating physics-inspired generative AI, multi-objective optimization, ultra-large virtual libraries, automated synthesis, and experimental validation in a closed-loop system.