Literature DB >> 28949520

Interpretation of Quantitative Structure-Activity Relationship Models: Past, Present, and Future.

Pavel Polishchuk1.   

Abstract

This paper is an overview of the most significant and impactful interpretation approaches of quantitative structure-activity relationship (QSAR) models, their development, and application. The evolution of the interpretation paradigm from "model → descriptors → (structure)" to "model → structure" is indicated. The latter makes all models interpretable regardless of machine learning methods or descriptors used for modeling. This opens wide prospects for application of corresponding interpretation approaches to retrieve structure-property relationships captured by any models. Issues of separate approaches are discussed as well as general issues and prospects of QSAR model interpretation.

Mesh:

Year:  2017        PMID: 28949520     DOI: 10.1021/acs.jcim.7b00274

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  23 in total

1.  Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions.

Authors:  Raquel Rodríguez-Pérez; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2020-05-02       Impact factor: 3.686

2.  A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility.

Authors:  Bowen Tang; Skyler T Kramer; Meijuan Fang; Yingkun Qiu; Zhen Wu; Dong Xu
Journal:  J Cheminform       Date:  2020-02-21       Impact factor: 5.514

3.  Classification models and SAR analysis on HDAC1 inhibitors using machine learning methods.

Authors:  Rourou Li; Yujia Tian; Zhenwu Yang; Yueshan Ji; Jiaqi Ding; Aixia Yan
Journal:  Mol Divers       Date:  2022-06-23       Impact factor: 2.943

4.  QSAR Methods.

Authors:  Giuseppina Gini
Journal:  Methods Mol Biol       Date:  2022

5.  Direct Prediction of Physicochemical Properties and Toxicities of Chemicals from Analytical Descriptors by GC-MS.

Authors:  Yasuyuki Zushi
Journal:  Anal Chem       Date:  2022-06-14       Impact factor: 8.008

6.  QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction.

Authors:  Chiakang Hung; Giuseppina Gini
Journal:  Mol Divers       Date:  2021-06-19       Impact factor: 2.943

7.  Benchmarks for interpretation of QSAR models.

Authors:  Mariia Matveieva; Pavel Polishchuk
Journal:  J Cheminform       Date:  2021-05-26       Impact factor: 5.514

8.  Exploring the Prominent and Concealed Inhibitory Features for Cytoplasmic Isoforms of Hsp90 Using QSAR Analysis.

Authors:  Magdi E A Zaki; Sami A Al-Hussain; Syed Nasir Abbas Bukhari; Vijay H Masand; Mithilesh M Rathore; Sumer D Thakur; Vaishali M Patil
Journal:  Pharmaceuticals (Basel)       Date:  2022-03-01

9.  Quantum chemical predictions of water-octanol partition coefficients applied to the SAMPL6 logP blind challenge.

Authors:  Michael R Jones; Bernard R Brooks
Journal:  J Comput Aided Mol Des       Date:  2020-01-30       Impact factor: 3.686

10.  Exploring naphthyl derivatives as SARS-CoV papain-like protease (PLpro) inhibitors and its implications in COVID-19 drug discovery.

Authors:  Sk Abdul Amin; Insaf Ahmed Qureshi; Kalyan Ghosh; Samayaditya Singh; Tarun Jha; Shovanlal Gayen
Journal:  Mol Divers       Date:  2021-03-06       Impact factor: 3.364

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