Sequence-Conditioned Ligand Generation with Compact Protein Prefixes

Designing ligands directly from protein sequences could enable the targeting of therapeutically relevant proteins for which well-characterized three-dimensional binding-pocket structures are not available. We develop a flexible autoregressive Transformer that conditions molecular generation on a compact protein prefix as well as separate affinity inputs. We use pretrained ESM3 residue embeddings to provide protein context without training a protein encoder from scratch. After a learned projection, we pool these embeddings over fixed sequence segments to provide regional protein summaries while keeping the conditioning sequence short. To test the contribution of protein context, we replace the protein prefix while keeping the intended target and oracle-based candidate-selection rule fixed. On a selected 42-target panel, correct protein conditioning yields a mean RF3 interface predicted TM-score (ipTM) of 0.920, compared with 0.829 for mismatched conditioning. A separate comparison also shows higher mean interface confidence for generated ligands compared to molecular-weight-matched ligands. Comparisons with PCMol and ProtoBind-Diff show numerically close mean RF3 ipTM scores for selected candidates on their respective panels. Our smaller ligand generator also uses less time and peak allocated GPU memory in cached-feature benchmarks of the tested implementations.