ArticleJACS Au2024
Bridging Machine Learning and Thermodynamics for Accurate p
Article in JACS Au, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- EasyDock 1.3: An Automated Pipeline for Molecular Docking.Journal of chemical information and modeling · 2026Article
- Methyl Cation Affinity and Methyl Anion Affinity Prediction Using Uni-Mol-Based Models.The journal of physical chemistry. A · 2026Article
- Unifying pJournal of chemical information and modeling · 2026Article
- Benchmarking pJournal of chemical information and modeling · 2026Article
- Digitized dataset of aqueous acid dissociation constants.RSC advances · 2026Article
- Development of a pKa predictor (pKaLearn) by leveraging teaching experience to improve machine learning.Communications chemistry · 2026Article
- Predictive free energy simulations through hierarchical distillation of quantum Hamiltonians.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Developing a Machine Learning Model for Hydrogen Bond Acceptance Based on Natural Bond Orbital Descriptors.The Journal of organic chemistry · 2025Article
- Machine and deep learning models for predicting high pressure density of heterocyclic thiophenic compounds based on critical properties.Scientific reports · 2025Article
- A workflow to create a high-quality protein-ligand binding dataset for training, validation, and prediction tasks.Digital discovery · 2025Article
- Computational tools for the prediction of site- and regioselectivity of organic reactions.Chemical science · 2025Review
- Article
- Interpretable Deep-Learning pJournal of chemical information and modeling · 2025Article
- pKJournal of computational chemistry · 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Integrating scientific principles into machine learning models to enhance their predictive performance and generalizability is a central challenge in the development of AI for Science. Herein, we introduce Uni-p
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.