Machine Learning Empowered Drug Screen

Abstract submission deadline

30 June 2025

Manuscript submission deadline

31 August 2025

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Topic Information

Dear Colleagues,

Drug design is a lengthy, costly, difficult, and inefficient process in spite of advances in biotechnology and the understanding of biological systems. Finding efficient drug pathways is crucial in the fight against future outbreaks, and much effort has been devoted to it. Computer-aided drug design (CADD) plays a vital role in accelerating the discovery of potential lead compounds and the optimization of their structure for the following pharmacological tests. In CADD, machine learning is widely used to train a model to predict the target properties including their potency and toxicity. Thus, machine learning methods are required to better accelerate the design of drugs. In this Special Issue on “Machine Learning-Empowered Drug Screen”, we will discuss various aspects of drug screen using machine learning methods.

Dr. Teng Zhou
Dr. Jiaqi Wang
Dr. Youyi Song
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	1.8	4.1	2008	18.9 Days	CHF 1600	Submit
Big Data and Cognitive Computing BDCC	3.7	7.1	2017	25.3 Days	CHF 1800	Submit
BioMedInformatics biomedinformatics	-	1.7	2021	22 Days	CHF 1000	Submit
Information information	2.4	6.9	2010	16.4 Days	CHF 1600	Submit
Mathematics mathematics	2.3	4.0	2013	18.3 Days	CHF 2600	Submit

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Published Papers (3 papers)

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All Algorithms BDCC BioMedInformatics Information Mathematics

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Supplementary material:
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Open AccessArticle

Breast Cancer Drugs Screening Model Based on Graph Convolutional Network and Ensemble Method

by Jia Li, Yun Zhao, Guoxing Shi and Xuewen Tan

Mathematics 2024, 12(12), 1779; https://doi.org/10.3390/math12121779 - 7 Jun 2024

Viewed by 1249

Abstract

Breast cancer is the first cancer incidence and the second cancer mortality in women. Therefore, for the life and health of breast cancer patients, the research and development of breast cancer drugs should be accelerated. In drug development, the search for compounds with [...] Read more.

Breast cancer is the first cancer incidence and the second cancer mortality in women. Therefore, for the life and health of breast cancer patients, the research and development of breast cancer drugs should be accelerated. In drug development, the search for compounds with good bioactivity, pharmacokinetics, and safety, including Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET), has always been a time-consuming and labor-intensive process. In this paper, the relationship between the molecular descriptor and ADMET properties of compounds is studied. Aiming at the problem of composite ADMET attribute classification, a Stacking Algorithm based on Graph Convolutional Network (SA-GCN) was proposed. Firstly, feature selection was performed in the data of molecular descriptors. Then the SA-GCN is developed by integrating the advantages of ten classical classification algorithms. Finally, various performance indicators were used to conduct comparative experiments. Experiments show that the SA-GCN is superior to other classifiers in the classification performance of ADMET, and the classification accuracy is 97.6391%, 98.1450%, 94.4351%, 96.4587%, and 97.9764% compared to other classifiers. Therefore, this method can be well applied to the classification of ADMET properties of compounds and then could provide some help to screen out compounds with good biological activities. Full article

(This article belongs to the Topic Machine Learning Empowered Drug Screen)

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13 pages, 1028 KiB  

Open AccessArticle

Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures

by Kannan Mayuri, Durairaj Varalakshmi, Mayakrishnan Tharaheswari, Chaitanya Sree Somala, Selvaraj Sathya Priya, Nagaraj Bharathkumar, Renganathan Senthil, Raja Babu Singh Kushwah, Sundaram Vickram, Thirunavukarasou Anand and Konda Mani Saravanan

BioMedInformatics 2024, 4(1), 347-359; https://doi.org/10.3390/biomedinformatics4010020 - 1 Feb 2024

Cited by 3 | Viewed by 2472

Abstract

The fat mass and obesity-associated (FTO) protein catalyzes metal-dependent modifications of nucleic acids, namely the demethylation of methyl adenosine inside mRNA molecules. The FTO protein has been identified as a potential target for developing anticancer therapies. Identifying a suitable ligand-targeting FTO protein is [...] Read more.

The fat mass and obesity-associated (FTO) protein catalyzes metal-dependent modifications of nucleic acids, namely the demethylation of methyl adenosine inside mRNA molecules. The FTO protein has been identified as a potential target for developing anticancer therapies. Identifying a suitable ligand-targeting FTO protein is crucial to developing chemotherapeutic medicines to combat obesity and cancer. Scientists worldwide have employed many methodologies to discover a potent inhibitor for the FTO protein. This study uses deep learning-based methods and molecular docking techniques to investigate the FTO protein as a target. Our strategy involves systematically screening a database of small chemical compounds. By utilizing the crystal structures of the FTO complexed with ligands, we successfully identified three small-molecule chemical compounds (ZINC000003643476, ZINC000000517415, and ZINC000001562130) as inhibitors of the FTO protein. The identification process was accomplished by employing a combination of screening techniques, specifically deep learning (DeepBindGCN) and Autodock vina, on the ZINC database. These compounds were subjected to comprehensive analysis using 100 nanoseconds of molecular dynamics and binding free energy calculations. The findings of our study indicate the identification of three candidate inhibitors that might effectively target the human fat mass and obesity protein. The results of this study have the potential to facilitate the exploration of other chemicals that can interact with FTO. Conducting biochemical studies to evaluate these compounds’ effectiveness may contribute to improving fat mass and obesity treatment strategies. Full article

(This article belongs to the Topic Machine Learning Empowered Drug Screen)

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17 pages, 1167 KiB  

Open AccessArticle

HSIC_CR: A Lightweight Scoring Criterion Based on Measuring the Degree of Causality for the Detection of SNP Interactions

by Junxi Zheng, Juan Zeng, Xinyang Wang, Gang Li, Jiaxian Zhu, Fanghong Wang and Deyu Tang

Mathematics 2022, 10(21), 4134; https://doi.org/10.3390/math10214134 - 5 Nov 2022

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Abstract

Recently, research on detecting SNP interactions has attracted considerable attention, which is of great significance for exploring complex diseases. The formulation of effective swarm intelligence optimization algorithms is a primary resolution to this issue. To achieve this goal, an important problem needs to [...] Read more.

Recently, research on detecting SNP interactions has attracted considerable attention, which is of great significance for exploring complex diseases. The formulation of effective swarm intelligence optimization algorithms is a primary resolution to this issue. To achieve this goal, an important problem needs to be solved in advance; that is, designing and selecting lightweight scoring criteria that can be calculated in $O(m)$ time and can accurately estimate the degree of association between SNP combinations and disease status. In this study, we propose a high-accuracy scoring criterion (HSIC${}_{CR}$) by measuring the degree of causality dedicated to assessing the degree. First, we approximate two kinds of dependencies according to the structural equation of the causal relationship between epistasis SNP combination and disease status. Then, inspired by these dependencies, we put forward this scoring criterion that integrates a widely used method of measuring statistical dependencies based on kernel functions (HSIC). However, the computing time complexity of HSIC is $O(m^2)$, which is too costly to be an integral part of the scoring criterion. Since the sizes of the sample space of the disease status, SNP loci and SNP combination are small enough, we propose an efficient method of computing HSIC for variables with a small sample in $O(m)$ time. Eventually, HSIC${}_{CR}$ can be computed in $O(m)$ time in practice. Finally, we compared HSIC${}_{CR}$ with five representative high-accuracy scoring criteria that detect SNP interactions for 49 simulation disease models. The experimental results show that the accuracy of our proposed scoring criterion is, overall, state-of-the-art. Full article

(This article belongs to the Topic Machine Learning Empowered Drug Screen)

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