MolToxPred: small molecule toxicity prediction using machine learning approach

Setiya, Anjali; Jani, Vinod; Sonavane, Uddhavesh; Joshi, Rajendra

doi:10.1039/d3ra07322j

Cited by 6 publications

(1 citation statement)

References 104 publications

(143 reference statements)

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“…For example, Setiya et al . [ 4 ] proposed MolToxPred, a machine learning tool, which uses a stacked model approach to predict the potential toxicity of small molecules. However, these representations heavily relied on manually crafted features, limiting their utility.…”

Section: Introductionmentioning

confidence: 99%

MvMRL: a multi-view molecular representation learning method for molecular property prediction

Zhang,

Lin,

et al. 2024

Briefings in Bioinformatics

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Effective molecular representation learning is very important for Artificial Intelligence-driven Drug Design because it affects the accuracy and efficiency of molecular property prediction and other molecular modeling relevant tasks. However, previous molecular representation learning studies often suffer from limitations, such as over-reliance on a single molecular representation, failure to fully capture both local and global information in molecular structure, and ineffective integration of multiscale features from different molecular representations. These limitations restrict the complete and accurate representation of molecular structure and properties, ultimately impacting the accuracy of predicting molecular properties. To this end, we propose a novel multi-view molecular representation learning method called MvMRL, which can incorporate feature information from multiple molecular representations and capture both local and global information from different views well, thus improving molecular property prediction. Specifically, MvMRL consists of four parts: a multiscale CNN-SE Simplified Molecular Input Line Entry System (SMILES) learning component and a multiscale Graph Neural Network encoder to extract local feature information and global feature information from the SMILES view and the molecular graph view, respectively; a Multi-Layer Perceptron network to capture complex non-linear relationship features from the molecular fingerprint view; and a dual cross-attention component to fuse feature information on the multi-views deeply for predicting molecular properties. We evaluate the performance of MvMRL on 11 benchmark datasets, and experimental results show that MvMRL outperforms state-of-the-art methods, indicating its rationality and effectiveness in molecular property prediction. The source code of MvMRL was released in https://github.com/jedison-github/MvMRL.

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Section: Introductionmentioning

confidence: 99%