ArticleACS omega2021
Solubility Prediction from Molecular Properties and Analytical Data Using an In-phase Deep Neural Network (Ip-DNN).
Article in ACS omega, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Role of High Fidelity Vs. Low Fidelity Experimental Data in Machine Learning Model Performance for Predicting Polymer Solubility.Macromolecular rapid communications · 2026Article
- Exploration of Colonic Mucus as A Dissolution Compartment for Orally Administered Drugs.The AAPS journal · 2026Article
- Prediction of Mycobacterium tuberculosis cell wall permeability using machine learning methods.Molecular diversity · 2024Article
- Predicting small molecules solubility on endpoint devices using deep ensemble neural networks.Digital discovery · 2024Article
- Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state.Journal of computer-aided molecular design · 2023Article
- Prediction of organic compound aqueous solubility using machine learning: a comparison study of descriptor-based and fingerprints-based models.Journal of cheminformatics · 2023Article
- Application of Machine Learning in Material Synthesis and Property Prediction.Materials (Basel, Switzerland) · 2023Review
- A machine learning approach for thermodynamic modeling of the statically measured solubility of nilotinib hydrochloride monohydrate (anti-cancer drug) in supercritical COScientific reports · 2023Article
- Materials informatics approach using domain modelling for exploring structure-property relationships of polymers.Scientific reports · 2022Article
- Prediction and Planning of Sports Competition Based on Deep Neural Network.Computational intelligence and neuroscience · 2022Article
- The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science.RSC advances · 2021Review
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
Materials informatics is an emerging field that allows us to predict the properties of materials and has been applied in various research and development fields, such as materials science. In particular, solubility factors such as the Hansen and Hildebrand solubility parameters (HSPs and SP, respectively) and Log
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.