ReviewMolecular informatics2019
A Survey of Multi-task Learning Methods in Chemoinformatics.
Review in Molecular informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Cardiosim-Tox: an interpretable multitask deep learning QSAR platform with multimodal feature fusion for predicting hERG, Cav1.2, and Nav1.5 blockade risk and potency.Archives of toxicology · 2026Article
- Analysis of the Toxicological Profile ofPlants (Basel, Switzerland) · 2025Article
- Python tools for structural tasks in chemistry.Molecular diversity · 2025Review
- An Attention-Based Multidimensional Fault Information Sharing Framework for Bearing Fault Diagnosis.Sensors (Basel, Switzerland) · 2025Article
- QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool.Journal of cheminformatics · 2024Article
- Optimal Configuration of Multi-Task Learning for Autonomous Driving.Sensors (Basel, Switzerland) · 2023Article
- Enabling data-limited chemical bioactivity predictions through deep neural network transfer learning.Journal of computer-aided molecular design · 2022Article
- Improving VAE based molecular representations for compound property prediction.Journal of cheminformatics · 2022Article
- Multi-Task Neural Networks and Molecular Fingerprints to Enhance Compound Identification from LC-MS/MS Data.Molecules (Basel, Switzerland) · 2022Article
- Prediction of Combined Sorbent and Catalyst Materials for SE-SMR, Using QSPR and Multitask Learning.Industrial & engineering chemistry research · 2022Article
- Improving Compound Activity Classification via Deep Transfer and Representation Learning.ACS omega · 2022Article
- Representation of molecules for drug response prediction.Briefings in bioinformatics · 2022Review
- Pushing the Boundaries of Molecular Property Prediction for Drug Discovery with Multitask Learning BERT Enhanced by SMILES Enumeration.Research (Washington, D.C.) · 2022Article
- Multitask CapsNet: An Imbalanced Data Deep Learning Method for Predicting Toxicants.ACS omega · 2021Article
- Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace.Briefings in bioinformatics · 2021Review
- Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods.Journal of chemical information and modeling · 2021Article
- Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets.Chemical research in toxicology · 2021Article
- Recommender Systems in Antiviral Drug Discovery.ACS omega · 2020Article
- graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein-Ligand Complexes.ACS omega · 2020Article
- Building attention and edge message passing neural networks for bioactivity and physical-chemical property prediction.Journal of cheminformatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the increasing volume of available data, the proportion of experimentally measured data remains small compared to the virtual chemical space of possible chemical structures. Therefore, there is a strong interest in simultaneously predicting different ADMET and biological properties of molecules, which are frequently strongly correlated with one another. Such joint data analyses can increase the accuracy of models by exploiting their common representation and identifying common features between individual properties. In this work we review the recent developments in multi-learning approaches as well as cover the freely available tools and packages that can be used to perform such studies.
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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.