scholarly journals Front Cover: Designing Anticancer Peptides by Constructive Machine Learning (ChemMedChem 13/2018)

ChemMedChem ◽  
2018 ◽  
Vol 13 (13) ◽  
pp. 1260-1260
Author(s):  
Francesca Grisoni ◽  
Claudia S. Neuhaus ◽  
Gisela Gabernet ◽  
Alex T. Müller ◽  
Jan A. Hiss ◽  
...  
Keyword(s):  
Machine Learning ◽  
Front Cover ◽  
Anticancer Peptides

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

scholarly journals Front Cover: Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data (ChemCatChem 18/2019)

ChemCatChem ◽  
2019 ◽  
Vol 11 (18) ◽  
pp. 4443-4443
Author(s):  
Keisuke Suzuki ◽  
Takashi Toyao ◽  
Zen Maeno ◽  
Satoru Takakusagi ◽  
Ken‐ichi Shimizu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Heterogeneous Catalysts ◽  
Published Data ◽  
Front Cover

scholarly journals Front Cover: Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning (ChemistryOpen 1/2019)

ChemistryOpen ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 1-1 ◽  
Author(s):  
Daniel Merk ◽  
Francesca Grisoni ◽  
Kay Schaller ◽  
Lukas Friedrich ◽  
Gisbert Schneider
Keyword(s):  
Machine Learning ◽  
Farnesoid X Receptor ◽  
Front Cover ◽  
Ensemble Machine Learning ◽  
Receptor Modulators

Designing Anticancer Peptides by Constructive Machine Learning

ChemMedChem ◽  
2018 ◽  
Vol 13 (13) ◽  
pp. 1300-1302 ◽  
Author(s):  
Francesca Grisoni ◽  
Claudia S. Neuhaus ◽  
Gisela Gabernet ◽  
Alex T. Müller ◽  
Jan A. Hiss ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anticancer Peptides

Comprehensive Review and Comparison for Anticancer Peptides Identification Models

2020 ◽  
Vol 21 ◽  
Author(s):  
Xiao Song ◽  
Yuanying Zhuang ◽  
Yihua Lan ◽  
Yinglai Lin ◽  
Xiaoping Min
Keyword(s):  
Machine Learning ◽  
Pathogenic Bacteria ◽  
Prediction Models ◽  
Support Vector ◽  
Comprehensive Review ◽  
Anticancer Peptides ◽  
Prediction Tools ◽  
Potential Applications ◽  
Normal Human ◽  
Comparative Results

: Anticancer peptides (ACPs) eliminate pathogenic bacteria and kill tumor cells, showing no hemolysis and no damages to normal human cells. This unique ability explores the possibility of ACPs as therapeutic delivery and its potential applications in clinical therapy. Identifying ACPs is one of the most fundamental and central problems in new antitumor drug research. During the past decades, a number of machine learning-based prediction tools have been developed to solve this important task. However, the predictions produced by various tools are difficult to quantify and compare. Therefore, in this article, we provide a comprehensive review of existing machine learning methods for ACPs prediction and fair comparison of the predictors. To evaluate current prediction tools, we conducted a comparative study and analyzed the existing ACPs predictor from 10 public literatures. The comparative results obtained suggest that Support Vector Machine-based model with features combination provided significant improvement in the overall performance, when compared to the other machine learning method-based prediction models.

scholarly journals Front Cover: Spatial Mode Correction of Single Photons Using Machine Learning (Adv. Quantum Technol. 3/2021)

2021 ◽  
Vol 4 (3) ◽  
pp. 2170031
Author(s):  
Narayan Bhusal ◽  
Sanjaya Lohani ◽  
Chenglong You ◽  
Mingyuan Hong ◽  
Joshua Fabre ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Photons ◽  
Spatial Mode ◽  
Front Cover

scholarly journals ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation

2021 ◽  
Vol 12 ◽  
Author(s):  
Xian-gan Chen ◽  
Wen Zhang ◽  
Xiaofei Yang ◽  
Chenhong Li ◽  
Hengling Chen
Keyword(s):  
Machine Learning ◽  
Cancer Treatment ◽  
Computational Methods ◽  
Data Augmentation ◽  
Feature Learning ◽  
Feature Space ◽  
Anticancer Peptides ◽  
Machine Learning Model ◽  
Using Data ◽  
Promising Perspective

Anticancer peptides (ACPs) have provided a promising perspective for cancer treatment, and the prediction of ACPs is very important for the discovery of new cancer treatment drugs. It is time consuming and expensive to use experimental methods to identify ACPs, so computational methods for ACP identification are urgently needed. There have been many effective computational methods, especially machine learning-based methods, proposed for such predictions. Most of the current machine learning methods try to find suitable features or design effective feature learning techniques to accurately represent ACPs. However, the performance of these methods can be further improved for cases with insufficient numbers of samples. In this article, we propose an ACP prediction model called ACP-DA (Data Augmentation), which uses data augmentation for insufficient samples to improve the prediction performance. In our method, to better exploit the information of peptide sequences, peptide sequences are represented by integrating binary profile features and AAindex features, and then the samples in the training set are augmented in the feature space. After data augmentation, the samples are used to train the machine learning model, which is used to predict ACPs. The performance of ACP-DA exceeds that of existing methods, and ACP-DA achieves better performance in the prediction of ACPs compared with a method without data augmentation. The proposed method is available at http://github.com/chenxgscuec/ACPDA.

Sign in / Sign up

Export Citation Format

Share Document