(Q62651119)

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A Survey of Multi‐Task Learning Methods in Chemoinformatics

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A Survey of Multi-task Learning Methods in Chemoinformatics

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scholarly article

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A Survey of Multi‐Task Learning Methods in Chemoinformatics (English)

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A Survey of Multi-task Learning Methods in Chemoinformatics (English)

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multi-task learning

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cheminformatics

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Michael Withnall

object named as

Michael Withnall

3

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Sergey Sosnin

1

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Igor V. Tetko

6

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6

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Igor V Tetko

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5

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Maxim Fedorov

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author name string

Mariia Vashurina

2

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Pavel Karpov

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Sergey Sosnin

1

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Michael Withnall

3

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publication date

28 November 2018

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April 2019

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Molecular Informatics

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38

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4

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e1800108

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describes a project that uses

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Europe PubMed Central

8 June 2022

https://europepmc.org/search?query=(METHODS:'scikit-image')

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inferred from PubMed Central ID database lookup

Application of ALOGPS 2.1 to predict log D distribution coefficient for Pfizer proprietary compounds

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https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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Associative neural network

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https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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Inductive transfer of knowledge: application of multi-task learning and feature net approaches to model tissue-air partition coefficients.

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https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

1 reference

https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins

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https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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Time-split cross-validation as a method for estimating the goodness of prospective prediction

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https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules

1 reference

https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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Inferring multi-target QSAR models with taxonomy-based multi-task learning.

1 reference

https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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Deep neural nets as a method for quantitative structure-activity relationships

1 reference

https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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Stargate GTM: Bridging Descriptor and Activity Spaces

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https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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A renaissance of neural networks in drug discovery.

1 reference

https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry

1 reference

https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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GTM-Based QSAR Models and Their Applicability Domains.

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https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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Molecular graph convolutions: moving beyond fingerprints.

1 reference

https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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Low Data Drug Discovery with One-Shot Learning

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https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships

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https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction.

1 reference

https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges.

1 reference

https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.

1 reference

https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

based on heuristic

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Effect of missing data on multitask prediction methods.

1 reference

https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

based on heuristic

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Application of Bioactivity Profile Based Fingerprints For Building Machine Learning Models

1 reference

https://pubmed.ncbi.nlm.nih.gov/30499195

12 December 2020

based on heuristic

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Identifiers

10.1002/MINF.201800108

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