ArticleACS omega2022
Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Gaps in AI-Driven Pharmacokinetic Property Prediction for Early Drug Development: A Scoping Review.Journal of chemical information and modeling · 2026Article
- DeepSnap: From Three-Dimensional Molecular Images to Quantitative Structure-Activity Predictions.International journal of molecular sciences · 2026Review
- In silico and in vitro investigations of novel Siglec-1 inhibitors in a microglial cell model.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Using Machine Learning to Identify Factors Affecting Antibody Production and Adverse Reactions After COVID-19 Vaccination.Vaccines · 2026Article
- A Machine Learning-Empowered Quantitative Structure-Activity Relationship Model for Predicting the Plasma Half-life of Drugs in Dogs.The AAPS journal · 2025Article
- In Silico ADME Methods Used in the Evaluation of Natural Products.Pharmaceutics · 2025Review
- Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure-Activity Relationships.Molecules (Basel, Switzerland) · 2023Review
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
The toxicity, absorption, distribution, metabolism, and excretion properties of some targets are difficult to predict by quantitative structure-activity relationship analysis. Therefore, there is a need for a new prediction method that performs well for these targets. The aim of this study was to develop a new regression model of rat clearance (CL). We constructed a regression model using 1545 in-house compounds for which we had rat CL data. Molecular descriptors were calculated using molecular operating environment, alvaDesc, and ADMET Predictor software. The classification model of DeepSnap and Deep Learning (DeepSnap-DL) with images of the three-dimensional chemical structures of compounds as features was constructed, and the prediction probabilities for each compound were calculated. For molecular descriptor-based methods that use molecular descriptors and conventional machine learning algorithms selected by DataRobot, the correlation coefficient (
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.