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Failure-mode index

Search what already failed

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.

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.

8 results in Negative / Null Result Report for "drug discovery"

Negative / Null Result ReportOpen accessMedicine

Integrating Artificial Intelligence and PET Imaging for Drug Discovery: A Paradigm Shift in Immunotherapy

Jeremy McGale, Harrison J. Howell, Arnaud Beddok et al. · 2024 · Pharmaceuticals

The integration of artificial intelligence (AI) and positron emission tomography (PET) imaging has the potential to become a powerful tool in drug discovery. This review aims to provide an overview of the current state of research and highlight the potential for this alliance to advance pharmaceutical innovation by accelerating the development and deployment of novel therapeutics. We previously performed a scoping review of three databases (Embase, MEDLINE, and CENTRAL), identifying 87 studies published between 2018 and 2022 relevant to medical imaging (e.g., CT, PET, MRI), immunotherapy, arti

View details →DOI: 10.3390/ph17020210Cited by 14
Negative / Null Result ReportOpen accessMedicine

Thought-provoking Molecules for Drug Discovery: antioxidants

İpek Komşuoğlu Çelikyurt · 2011 · Pharmaceutica Analytica Acta

Affirmative influence of antioxidant vitamins may be observed in atherosclerosis and endothelial dysfunction, male infertility, Parkinson's disease (especially via lipoic acid), diabetes, brain tumors and skin maturation There is not yet…

View details →DOI: 10.4172/2153-2435.s3-001Cited by 10
Negative / Null Result ReportOpen accessMedicine

Bioavailability as Proof to Authorize the Clinical Testing of Neurodegenerative Drugs-Protocols and Advice for the FDA to Meet the ALS Act Vision.

Niazi SK · 2024 · International journal of molecular sciences

Although decades of intensive drug discovery efforts to treat neurodegenerative disorders (NDs) have failed, around half a million patients in more than 2000 studies continue being tested, costing over USD 100 billion, despite the…

View details →DOI: 10.3390/ijms251810211
Negative / Null Result ReportMedicine

End-to-end multimodal attention fusion for drug-target interaction prediction with cold-scaffold validation on the Davis kinase benchmark.

Agboola OE, Agboola SS, Shaleye AB et al. · 2026 · Journal of molecular graphics & modelling

Predicting drug-target interactions computationally is a practical strategy for prioritizing candidate compounds in early drug discovery, but the reliability of published models is often limited by small resampled datasets, warm-only…

View details →DOI: 10.1016/j.jmgm.2026.109461
Negative / Null Result ReportOpen accessComputer Science

Dead Science Walking: Publication Bias and the AI Scientist Pipeline

Kargi Chauhan · 2026 · arXiv

AI scientist systems are beginning to automate the production, evaluation, and iteration of scientific hypotheses. Their promise is speed; their risk is that speed also scales errors embedded in the scientific record. We argue that a near-term risk is corpus failure: AI scientist systems are trained on and grounded in a literature that over-represents positive results and under-represents null findings. We formalise this distortion as the null result gap, estimate it across three domains (drug discovery ~0.60, psychology ~0.56, cancer biology ~0.35), and introduce an amplification index for re

Negative / Null Result ReportOpen accessComputer Science

Rethinking Drug-Drug Interaction Modeling as Generalizable Relation Learning

Dong Xu, Jiantao Wu, Qihua Pan et al. · 2026 · arXiv

Drug-drug interaction (DDI) prediction is central to drug discovery and clinical development, particularly in the context of increasingly prevalent polypharmacy. Although existing computational methods achieve strong performance on standard benchmarks, they often fail to generalize to realistic deployment scenarios, where most candidate drug pairs involve previously unseen drugs and validated interactions are scarce. We demonstrate that proximity in the embedding spaces of prevailing molecule-centric DDI models does not reliably correspond to interaction labels, and that simply scaling up mode