ReviewiScience2022
Uncertainty quantification: Can we trust artificial intelligence in drug discovery?
Review in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- Uncertainty Quantification with Domain Classification Models for Acetylcholinesterase Inhibition.Journal of chemical information and modeling · 2026Article
- Toward an AI Era: Application of Artificial Intelligence in Inclusion Complex Screening.Pharmaceutics · 2026Review
- HP-MoleQ: An Effective Predictive Model for High-Throughput Screening of Food-Derived Hepatoprotective Compounds.Interdisciplinary sciences, computational life sciences · 2026Article
- Uncertainty Quantification in Molecular Machine Learning for Property Predictions under Data Shifts.Journal of chemical information and modeling · 2026Article
- Article
- Combining Bayesian and Evidential Uncertainty Quantification for Improved Bioactivity Modeling.Journal of chemical information and modeling · 2025Article
- Uncertainty quantification enables reliable deep learning for protein-ligand binding affinity prediction.Scientific reports · 2025Article
- IECata: interpretable bilinear attention network and evidential deep learning improve the catalytic efficiency prediction of enzymes.Briefings in bioinformatics · 2025Article
- Scalable crystal structure relaxation using an iteration-free deep generative model with uncertainty quantification.Nature communications · 2024Article
- Machine Learning ADME Models in Practice: Four Guidelines from a Successful Lead Optimization Case Study.ACS medicinal chemistry letters · 2024Article
- Development and Evaluation of Conformal Prediction Methods for Quantitative Structure-Activity Relationship.ACS omega · 2024Article
- Reducing overconfident errors in molecular property classification using Posterior Network.Patterns (New York, N.Y.) · 2024Article
- The whack-a-mole governance challenge for AI-enabled synthetic biology: literature review and emerging frameworks.Frontiers in bioengineering and biotechnology · 2024Review
- Article
- Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence.Journal of the American Chemical Society · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The problem of human trust is one of the most fundamental problems in applied artificial intelligence in drug discovery. In silico models have been widely used to accelerate the process of drug discovery in recent years. However, most of these models can only give reliable predictions within a limited chemical space that the training set covers (applicability domain). Predictions of samples falling outside the applicability domain are unreliable and sometimes dangerous for the drug-design decision-making process. Uncertainty quantification accordingly has drawn great attention to enable autonomous drug designing. By quantifying the confidence level of model predictions, the reliability of the predictions can be quantitatively represented to assist researchers in their molecular reasoning and experimental design. Here we summarize the state-of-the-art approaches to uncertainty quantification and underline how they can be used for drug design and discovery projects. Furthermore, we also outline four representative application scenarios of uncertainty quantification in drug discovery.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.