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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Biophysics".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 16515

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Special Issue Editors

Dr. Xiangxiang Zeng

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Guest Editor

School of Information Science and Engineering, Hunan University, Changsha 410082, China
Interests: biostatistics; system biology

Dr. Tao Song

E-Mail Website
Guest Editor

Department of Artificial Intelligence, Faculty of Computer Science, Polytechnical University of Madrid, Campus de Montegancedo, 28660 Madrid, Spain
Interests: intelligent drug design; biomedical information processing; bio-inspired computing

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) has demonstrated its capability in almost all science and engineering fields. Many researchers endeavour to explore AI methods and approaches in drug discovery, which is particularly fascinating. Drug discovery is essentially a process of identifying new medicines. Artificial neural network methods have exhibited their superior potential in drug repurposing, drug molecule re/structuring and drug molecular mechanics, all of which are topics of drug discovery.

This thematic issue aims to cover the recent development in drug discovery with a special focus on using artificial intelligence in service of the analysis and interpretation of the molecular structure of drugs, protein-ligand interaction, drug target affinity and relevant open issues. The objective is to provide a comprehensive and up-to-date compilation of research and experimental works in the field. Studies of any stages, in-silicon, in-vitro and in-viva, are welcomed. To close the loop, we would like to invite contributions of medical imaging which possibly relate to drug discovery, treatment results and so on. Please kindly note that since IJMS is a journal of molecular science, thus pure clinical studies will not suitable for our journal.

New methods must be compared to existing state-of-the-art methods, using real biological data. The inclusion of experimental data is very much encouraged.

Dr. Xiangxiang Zeng
Dr. Tao Song
Guest Editors

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Keywords

intelligent drug design
machine-learning-based drug molecule structuring and restructuring
drug molecule modification
novel drug repositioning
molecular drug data processing
molecular modelling
protein–protein interactions
drug–target
protein–protein interactions
new drug efficacy analysis in silicon and in vitro
drug target binding and affinity studies
ai-based target identification and validation
machine-learning-enriched medical image processing
intelligent visualization of medical image data

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Published Papers (5 papers)

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Research

13 pages, 2708 KiB

Open AccessArticle

NEXGB: A Network Embedding Framework for Anticancer Drug Combination Prediction

by Fanjie Meng, Feng Li, Jin-Xing Liu, Junliang Shang, Xikui Liu and Yan Li

Int. J. Mol. Sci. 2022, 23(17), 9838; https://doi.org/10.3390/ijms23179838 - 30 Aug 2022

Cited by 12 | Viewed by 2382

Abstract

Compared to single-drug therapy, drug combinations have shown great potential in cancer treatment. Most of the current methods employ genomic data and chemical information to construct drug–cancer cell line features, but there is still a need to explore methods to combine topological information in the protein interaction network (PPI). Therefore, we propose a network-embedding-based prediction model, NEXGB, which integrates the corresponding protein modules of drug–cancer cell lines with PPI network information. NEXGB extracts the topological features of each protein node in a PPI network by struc2vec. Then, we combine the topological features with the target protein information of drug–cancer cell lines, to generate drug features and cancer cell line features, and utilize extreme gradient boosting (XGBoost) to predict the synergistic relationship between drug combinations and cancer cell lines. We apply our model on two recently developed datasets, the Oncology-Screen dataset (Oncology-Screen) and the large drug combination dataset (DrugCombDB). The experimental results show that NEXGB outperforms five current methods, and it effectively improves the predictive power in discovering relationships between drug combinations and cancer cell lines. This further demonstrates that the network information is valid for detecting combination therapies for cancer and other complex diseases. Full article

(This article belongs to the Special Issue Current Artificial Intelligence Approaches in Biomedical Information Processing)

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17 pages, 10053 KiB

Open AccessArticle

TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture

by Xun Wang, Zhiyuan Zhang, Chaogang Zhang, Xiangyu Meng, Xin Shi and Peng Qu

Int. J. Mol. Sci. 2022, 23(8), 4263; https://doi.org/10.3390/ijms23084263 - 12 Apr 2022

Cited by 16 | Viewed by 3289

Abstract

Protein phosphorylation is one of the most critical post-translational modifications of proteins in eukaryotes, which is essential for a variety of biological processes. Plenty of attempts have been made to improve the performance of computational predictors for phosphorylation site prediction. However, most of them are based on extra domain knowledge or feature selection. In this article, we present a novel deep learning-based predictor, named TransPhos, which is constructed using a transformer encoder and densely connected convolutional neural network blocks, for predicting phosphorylation sites. Data experiments are conducted on the datasets of PPA (version 3.0) and Phospho. ELM. The experimental results show that our TransPhos performs better than several deep learning models, including Convolutional Neural Networks (CNN), Long-term and short-term memory networks (LSTM), Recurrent neural networks (RNN) and Fully connected neural networks (FCNN), and some state-of-the-art deep learning-based prediction tools, including GPS2.1, NetPhos, PPRED, Musite, PhosphoSVM, SKIPHOS, and DeepPhos. Our model achieves a good performance on the training datasets of Serine (S), Threonine (T), and Tyrosine (Y), with AUC values of 0.8579, 0.8335, and 0.6953 using 10-fold cross-validation tests, respectively, and demonstrates that the presented TransPhos tool considerably outperforms competing predictors in general protein phosphorylation site prediction. Full article

(This article belongs to the Special Issue Current Artificial Intelligence Approaches in Biomedical Information Processing)

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14 pages, 709 KiB

Open AccessArticle

Single Cell Self-Paced Clustering with Transcriptome Sequencing Data

by Peng Zhao, Zenglin Xu, Junjie Chen, Yazhou Ren and Irwin King

Int. J. Mol. Sci. 2022, 23(7), 3900; https://doi.org/10.3390/ijms23073900 - 31 Mar 2022

Cited by 1 | Viewed by 2531

Abstract

Single cell RNA sequencing (scRNA-seq) allows researchers to explore tissue heterogeneity, distinguish unusual cell identities, and find novel cellular subtypes by providing transcriptome profiling for individual cells. Clustering analysis is usually used to predict cell class assignments and infer cell identities. However, the performance of existing single-cell clustering methods is extremely sensitive to the presence of noise data and outliers. Existing clustering algorithms can easily fall into local optimal solutions. There is still no consensus on the best performing method. To address this issue, we introduce a single cell self-paced clustering (scSPaC) method with F-norm based nonnegative matrix factorization (NMF) for scRNA-seq data and a sparse single cell self-paced clustering (sscSPaC) method with

l_{21}

-norm based nonnegative matrix factorization for scRNA-seq data. We gradually add single cells from simple to complex to our model until all cells are selected. In this way, the influences of noisy data and outliers can be significantly reduced. The proposed method achieved the best performance on both simulation data and real scRNA-seq data. A case study about human clara cells and ependymal cells scRNA-seq data clustering shows that scSPaC is more advantageous near the clustering dividing line. Full article

(This article belongs to the Special Issue Current Artificial Intelligence Approaches in Biomedical Information Processing)

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13 pages, 2229 KiB

Open AccessArticle

SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning

by Xun Wang, Jiali Liu, Chaogang Zhang and Shudong Wang

Int. J. Mol. Sci. 2022, 23(7), 3780; https://doi.org/10.3390/ijms23073780 - 29 Mar 2022

Cited by 18 | Viewed by 3514

Abstract

Identifying compound-protein (drug-target, DTI) interactions (CPI) accurately is a key step in drug discovery. Including virtual screening and drug reuse, it can significantly reduce the time it takes to identify drug candidates and provide patients with timely and effective treatment. Recently, more and more researchers have developed CPI’s deep learning model, including feature representation of a 2D molecular graph of a compound using a graph convolutional neural network, but this method loses much important information about the compound. In this paper, we propose a novel three-channel deep learning framework, named SSGraphCPI, for CPI prediction, which is composed of recurrent neural networks with an attentional mechanism and graph convolutional neural network. In our model, the characteristics of compounds are extracted from 1D SMILES string and 2D molecular graph. Using both the 1D SMILES string sequence and the 2D molecular graph can provide both sequential and structural features for CPI predictions. Additionally, we select the 1D CNN module to learn the hidden data patterns in the sequence to mine deeper information. Our model is much more suitable for collecting more effective information of compounds. Experimental results show that our method achieves significant performances with RMSE (Root Mean Square Error) = 2.24 and

R^{2}

(degree of linear fitting of the model) = 0.039 on the GPCR (G Protein-Coupled Receptors) dataset, and with RMSE = 2.64 and

R^{2}

= 0.018 on the GPCR dataset RMSE, which preforms better than some classical deep learning models, including RNN/GCNN-CNN, GCNNet and GATNet. Full article

(This article belongs to the Special Issue Current Artificial Intelligence Approaches in Biomedical Information Processing)

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21 pages, 10349 KiB

Open AccessArticle

IMGG: Integrating Multiple Single-Cell Datasets through Connected Graphs and Generative Adversarial Networks

by Xun Wang, Chaogang Zhang, Ying Zhang, Xiangyu Meng, Zhiyuan Zhang, Xin Shi and Tao Song

Int. J. Mol. Sci. 2022, 23(4), 2082; https://doi.org/10.3390/ijms23042082 - 14 Feb 2022

Cited by 10 | Viewed by 3422

Abstract

There is a strong need to eliminate batch-specific differences when integrating single-cell RNA-sequencing (scRNA-seq) datasets generated under different experimental conditions for downstream task analysis. Existing batch correction methods usually transform different batches of cells into one preselected “anchor” batch or a low-dimensional embedding space, and cannot take full advantage of useful information from multiple sources. We present a novel framework, called IMGG, i.e., integrating multiple single-cell datasets through connected graphs and generative adversarial networks (GAN) to eliminate nonbiological differences between different batches. Compared with current methods, IMGG shows excellent performance on a variety of evaluation metrics, and the IMGG-corrected gene expression data incorporate features from multiple batches, allowing for downstream tasks such as differential gene expression analysis. Full article

(This article belongs to the Special Issue Current Artificial Intelligence Approaches in Biomedical Information Processing)

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