ReviewJournal of chemical theory and computation2023
A Perspective on Explanations of Molecular Prediction Models.
Review in Journal of chemical theory and computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 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
37 citing papers in PubMed.
- Binary interaction parameters of a million mixtures for PCP-SAFT using deep learning.Digital discovery · 2026Article
- Resolving Chemically InequivalentJACS Au · 2026Article
- Semantic knowledge improves molecular machine learning for chemical toxicity prediction.iScience · 2026Article
- Deciphering Molecular and Solvent Effects on Aqueous and Organic Solubility through Interpretable Machine Learning Approaches.ACS omega · 2026Article
- Integrated Computer-Aided Drug Design: Advances in GPCR Natural Ligand Discovery.Cell biochemistry and biophysics · 2026Review
- Explainable artificial intelligence for molecular design in pharmaceutical research.Chemical science · 2026Review
- Explainable artificial intelligence for materials discovery: application to catalysts for the HER and ORR.Chemical science · 2026Article
- Uncertainty-aware machine learning to predict non-cancer human toxicity for the global chemicals market.Nature communications · 2026Article
- Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices.ACS catalysis · 2026Article
- Design of active sites for amine-functionalized direct air capture materials using integrated high-throughput calculations and machine learning.Communications chemistry · 2025Article
- Peering inside the black box by learning the relevance of many-body functions in neural network potentials.Nature communications · 2025Article
- Multi-level QTAIM-enriched graph neural networks for resolving properties of transition metal complexes.Digital discovery · 2025Article
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- Property-driven localization and characterization in deep molecular representations.Scientific reports · 2025Article
- ACES-GNN: can graph neural network learn to explain activity cliffs?Digital discovery · 2025Article
- A multimodal contrastive learning framework for predicting P-glycoprotein substrates and inhibitors.Journal of pharmaceutical analysis · 2025Article
- CIAA: Integrated Proteomics and Structural Modeling for Understanding Cysteine Reactivity with Iodoacetamide Alkyne.ACS chemical biology · 2025Article
- A robust and interpretable graph neural network-based protocol for predicting p-glycoprotein substrates.Briefings in bioinformatics · 2025Article
- Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges.ACS applied materials & interfaces · 2025Review
- Comparing Explanations of Molecular Machine Learning Models Generated with Different Methods for the Calculation of Shapley Values.Molecular informatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Chemists can be skeptical in using deep learning (DL) in decision making, due to the lack of interpretability in "black-box" models. Explainable artificial intelligence (XAI) is a branch of artificial intelligence (AI) which addresses this drawback by providing tools to interpret DL models and their predictions. We review the principles of XAI in the domain of chemistry and emerging methods for creating and evaluating explanations. Then, we focus on methods developed by our group and their applications in predicting solubility, blood-brain barrier permeability, and the scent of molecules. We show that XAI methods like chemical counterfactuals and descriptor explanations can explain DL predictions while giving insight into structure-property relationships. Finally, we discuss how a two-step process of developing a black-box model and explaining predictions can uncover structure-property relationships.
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.