Uncertainty quantification: Can we trust artificial intelligence in drug discovery?

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Upgrading Reliability in Molecular Property Prediction by Robust Quantification of Uncertainty from Machine Learning Models

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Combining Bayesian and Evidential Uncertainty Quantification for Improved Bioactivity Modeling

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Alternate recognition by dengue protease: Proteolytic and binding assays provide functional evidence beyond an induced-fit

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Explainable Graph Neural Networks in Chemistry: Combining Attribution and Uncertainty Quantification

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Targeted deep learning classification and feature extraction for clinical diagnosis

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Temporal distribution shift in real-world pharmaceutical data: Implications for uncertainty quantification in QSAR models

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Uncertainty quantification enables reliable deep learning for protein–ligand binding affinity prediction

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Physics-inspired explainable AI for mechanistic and decision support in drug discovery

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Analysis of machine learning prediction reliability based on sampling distance evaluation with feature decorrelation

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New Era’s of Artificial Intelligence in Pharmaceutical Industries

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Uncertainty-Aware Hydrogen Production Optimization from a Steam Methane Reformer with a Water Gas Shift Reactor and Pressure Swing Adsorption

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Uncertainty Quantification in Pairwise Difference Learning for Classification

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Temporal Evaluation of Uncertainty Quantification Under Distribution Shift

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Computational interrogation of natural compounds identified resveratrol-3-O-D-glucopyranoside as a potential inhibitor of essential monkeypox virus proteins

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Trustworthy Compound–Protein Interaction Prediction with Interpretable and Conformalized Cross-Attention Transformers

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Advances in Antiviral Drug Development Targeting Viral Proteases

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Machine Learning Regions of Reliability based on Sampling Distance Evaluation with Feature Decorrelation for Tabular Time DatasetsPreprint

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TrustworthyCPI: Trustworthy Compound–Protein Interaction Prediction

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Contrastive prior enhances the performance of Bayesian neural network-based molecular property prediction

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Improving Explanation Measures Inside the Decision-Making Process to Increase the Potential of AI Applications in Healthcare

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Uncertainty-Aware Deep Learning for Multi-Metric and Dose-Specific Prediction of Drug SynergyPreprint

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kinCSM-RTK: Machine Learning-Based Screening of Receptor Tyrosine Kinase Inhibitors in Drug Discovery

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Digital transformation of the pharmaceutical industry: the role of artificial intelligence in pharmacy

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HP-MoleQ: An Effective Predictive Model for High-Throughput Screening of Food-Derived Hepatoprotective Compounds

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Trustworthy AI for Computational Drug Discovery: A Realist Review of Explainability, Reproducibility, Validation, and Regulatory Confidence

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From Ethnopharmacological Knowledge to Drug Candidates: A Decision-Intelligence Model for AI-Guided Natural Product Discovery

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Human-in-the-Loop Computational Phytopharmacology: Expert Curation, Model Oversight, Mechanistic Reasoning, and Translational Decision Confidence

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Safety-by-Design Generative Chemistry for Natural Products: Toxicity Boundaries, Interaction Risk, Validation Gates, and Human Oversight

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Every Molecular Prediction Needs an Evidence Passport Linking Confidence, Applicability, Provenance, and Experimental Consequence

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Uncertainty Quantification with Domain Classification Models for Acetylcholinesterase Inhibition

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QSAR without False Confidence: A Model-Evidence Dossier for Applicability Domains, Calibration, Dataset Shift, and Decision-Relevant Uncertainty

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From ranking to decision: TASC-VS for calibration-aware top-k virtual screening

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Artificial intelligence and machine learning in predicting food hunger and severity: A comprehensive review

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Scientific Overconfidence in High-Performing Materials AI Systems

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The Interpretability–Complexity Paradox in Deep Materials Networks

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Advances in the Use of Artificial Intelligence in the Pharmaceutical Industry

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Physically Grounded Descriptors for Machine-Learning-Guided Diluent Design in Lithium Metal Batteries

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Commercial Predictive Toxicology Platforms

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Uncertainty quantification in drug design

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Onzekerheidsquantificatie voor betrouwbare voorspellingen van interacties tussen geneesmiddelen en doelwitten

Friesacher, H. R. (2026) · Lirias

Incentivizing challenging drug discovery: from reducing failure to quantifying uncertainty

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Evaluating Point-Prediction Uncertainties in Neural Networks for Drug DiscoveryPreprint

Fan, Y. J., Allen, J., McLoughlin, K., Shi, D., Bennion, B. J., Zhang, X., & Lightstone, F. C. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.17043

Quantifying sources of uncertainty in drug discovery predictions with probabilistic models

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