ReviewChemical science2026
Explainable artificial intelligence for molecular design in pharmaceutical research.
Review in Chemical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Chemically Interpretable Explanations for Molecular Property Prediction via Fragment-Level Shapley Values.Journal of chemical information and modeling · 2026Article
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Explainability Methods from Machine Learning Detect Important Drugs' Atoms in Drug-Target Interactions.Journal of chemical information and modeling · 2026Article
- In Silico Drug Design and Discovery: Big Data for Small Molecule Design-2nd Edition.Biomolecules · 2026Article
- Explainable neuro-symbolic artificial intelligence for automated interpretation of corneal topography and early keratoconus detection.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.Frontiers in neuroscience · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rise of artificial intelligence (AI) has taken machine learning (ML) in molecular design to a new level. As ML increasingly relies on complex deep learning frameworks, the inability to understand predictions of black-box models has become a topical issue. Consequently, there is strong interest in the field of explainable AI (XAI) to bridge the gap between black-box models and the acceptance of their predictions, especially at interfaces with experimental disciplines. Therefore, XAI methods must go beyond extracting learning patterns from ML models and present explanations of predictions in a human-centered, transparent, and interpretable manner. In this Perspective, we examine current challenges and opportunities for XAI in molecular design and evaluate the benefits of incorporating domain-specific knowledge into XAI approaches for model refinement, experimental design, and hypothesis testing. In this context, we also discuss the current limitations in evaluating results from chemical language models that are increasingly used in molecular design and drug discovery.
Identifiers
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.