Virtual (computational) high-throughput
screening provides a strategy
for prioritizing compounds for experimental screens, but the choice
of virtual screening algorithm depends on the data set and evaluation
strategy. We consider a wide range of ligand-based machine learning
and docking-based approaches for virtual screening on two protein–protein
interactions, PriA-SSB and RMI-FANCM, and present a strategy for choosing
which algorithm is best for prospective compound prioritization. Our
workflow identifies a random forest as the best algorithm for these
targets over more sophisticated neural network-based models. The top
250 predictions from our selected random forest recover 37 of the
54 active compounds from a library of 22,434 new molecules assayed
on PriA-SSB. We show that virtual screening methods that perform well
on public data sets and synthetic benchmarks, like multi-task neural
networks, may not always translate to prospective screening performance
on a specific assay of interest.
In settings with related prediction tasks, integrated multi-task learning models can often improve performance relative to independent single-task models. However, even when the average task performance improves, individual tasks may experience negative transfer in which the multi-task model’s predictions are worse than the single-task model’s. We show the prevalence of negative transfer in a computational chemistry case study with 128 tasks and introduce a framework that provides a foundation for reducing negative transfer in multitask models. Our Loss-Balanced Task Weighting approach dynamically updates task weights during model training to control the influence of individual tasks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.