Null DatasetOpen accessComputer Science
Yu Liu, Qichun Wei, Guocan Yu et al. · 2014 · Database
Experience in clinical practice and research in systems pharmacology suggested the limitations of the current one-drug-one-target paradigm in new drug discovery. Single-target drugs may not always produce desired physiological effects on the entire biological system, even if they have successfully regulated the activities of their designated targets. On the other hand, multicomponent therapy, in which two or more agents simultaneously interact with multiple targets, has attracted growing attention. Many drug combinations consisting of multiple agents have already entered clinical practice, esp
View details →DOI: 10.1093/database/bau124Cited by 120 Failed Experiment ReportOpen accessComputer Science
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 Negative / Null Result ReportOpen accessMedicine
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
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 ReportMedicine
Khairil, Benny, Jerry et al. · 2026 · Pharmaceuticals (Basel, Switzerland)
The integration of artificial intelligence (AI) into the life sciences has accelerated significantly between 2022 and 2026, accompanied by global investment exceeding USD 100 billion and widespread expectations of a transformative impact…
View details →DOI: 10.3390/ph19060916 Null DatasetMedicine
Tan, Gao, Huang et al. · 2026 · Journal of the American Chemical Society
Sulfur(VI) fluoride exchange (SuFEx) has emerged as a powerful click reaction for constructing diverse S(VI)-based linkages across chemical biology, materials science, and drug discovery. However, the diversity of SuFEx hubs and native…
View details →DOI: 10.1021/jacs.6c04549 Negative / Null Result ReportOpen accessMedicine
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 Report
shakeel h · 2026 · Preprint
Abstract Correct ligand-site assignment is a prerequisite for interpreting molecular dynamics simulations of non-ATP kinase ligands. In computational chemistry and structure-based drug discovery, apparent ligand stability does not by…
View details →DOI: 10.21203/rs.3.rs-9770049/v1 Negative / Null Result ReportMedicine
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
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
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
