Structural input
++ X-ray, NMR, cryo-EM, and public fragment data provide the starting + points for target-aware design. +
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+ Target-based drug design
+AI for molecular design
++ FragmentScreen connects fragment-based screening with + AI-supported fragment-to-lead optimisation. This repository is the + open computational side: train target-specific screeners, generate + and decorate molecular scaffolds, evaluate properties, and plan + synthesis routes. +
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+ FragmentScreen
++ FragmentScreen develops instrumentation, workflows, and experimental + and computational methods for fragment-based drug discovery. Work + package 6 focuses on AI-supported fragment-to-lead optimisation, + bridging structural biology, medicinal chemistry, and generative AI. +
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+ AI design
++ Chemical language models combine motifs, grow scaffolds, and use + Regression Transformer models to decorate promising analogues. +
+DMTA loop
++ Designed molecules move through synthesis planning, experimental + validation, and model refinement in a design-make-test-analyse + cycle. +
+Project links
++ The FragmentScreen WP6 blog describes the FFUL and IBM Research work + on AI-supported design for SARS-CoV-2 targets, including motif-guided + generation, Regression Transformer optimisation, RXN-assisted synthesis + planning, and wet-lab validation. +
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