Failure-mode index

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A searchable index of real negative results, null findings, and replication failures from the published literature — so you can learn what didn't work before repeating it.

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WASTE indexes published research — it does not host or republish full papers. Each entry is a metadata record (title, authors, DOI) compiled from open scholarly databases, with the abstract shown in full only where the paper is openly licensed (e.g. Creative Commons); otherwise a short excerpt is shown for reference under fair use. WASTE classifies each work by failure type; classifications are automated and approximate.

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1 result in Failed Experiment Report for "drug discovery"

Failed Experiment ReportOpen accessComputer Science

DrugEx v3: scaffold-constrained drug design with graph transformer-based reinforcement learning

Xuhan Liu, Kai Ye, Herman van Vlijmen et al. · 2023 · Journal of Cheminformatics

Abstract Rational drug design often starts from specific scaffolds to which side chains/substituents are added or modified due to the large drug-like chemical space available to search for novel drug-like molecules. With the rapid growth of deep learning in drug discovery, a variety of effective approaches have been developed for de novo drug design. In previous work we proposed a method named DrugEx , which can be applied in polypharmacology based on multi-objective deep reinforcement learning. However, the previous version is trained under fixed objectives and does not allow users to input a

View details →DOI: 10.1186/s13321-023-00694-zCited by 80