Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting.

ABSTRACT: We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property of interest. This problem arises in discovery processes that rely on screening funnels for trading off the overall costs against throughput and accuracy. Typically, individual stages in these processes are loosely connected and each one generates data at different scale and fidelity. We consider this setup holistically and demonstrate empirically that existing transfer learning techniques for graph neural networks are generally unable to harness the information from multi-fidelity cascades. Here, we propose several effective transfe

SUBMITTER: Buterez D 

PROVIDER: S-EPMC11258334 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature