Year: 2026
Type: preprint
Abstract: Molecular structures are abundant, but measured properties are scarce and scattered across assays that each cover different compounds. Most molecular pretraining pipelines use only the structures, moving directly from self-supervision to endpoint-specific fine-tuning and leaving large, sparsely labelled bioactivity databases unused. We present GRASP, a 93.5M-parameter graph Transformer trained in two stages: replaced-token detection over atom tokens with shortest-path relative attention using 1.54B molecule presentations from ZINC20, followed by adaptation on sparse labels from 642 ChEMBL assays. On 23 cluster-held-out OpenADMET endpoints, fine-tuned GRASP reaches a mean MAE of 0.374, compared with 0.383 for CheMeleon and 0.408 for ECFP4-LightGBM, and has lower error than Chemprop on 22 of 23 endpoints. Introducing the intermediate ChEMBL stage reduces mean MAE from 0.407 to 0.374 and improves 21 of 23 endpoints. In a controlled smaller-scale recipe comparison, replaced-token detection yields lower MAE than a masked-token recipe on 16 of 23 OpenADMET endpoints and favorable point estimates on 17 of 22 TDC ADMET tasks. Sparse multi-assay data, used as an intermediate training stage, is an effective complement to structural pretraining.
Institutions: Shiv Nadar University, University of Edinburgh
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7216352652