Structure and ligand-based drug discovery of IL-4 inhibitors via interaction-energy-based learning approaches

2021 ◽  
pp. 1-19
Author(s):  
Sehrish Naz ◽  
Sajda Ashraf ◽  
Mohammad K. Parvez ◽  
Mohammed S. Al-Dosari ◽  
Zaheer Ul-Haq
Keyword(s):  
Drug Discovery ◽  
Interaction Energy ◽  
Learning Approaches

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 Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Ignacio Ponzoni ◽  
Víctor Sebastián-Pérez ◽  
Carlos Requena-Triguero ◽  
Carlos Roca ◽  
María J. Martínez ◽  
...  
Keyword(s):  
Feature Selection ◽  
Drug Discovery ◽  
Feature Learning ◽  
Learning Approaches ◽  
Qsar Modeling

ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches

2016 ◽  
Vol 13 (8) ◽  
pp. 2855-2866 ◽  
Author(s):  
Shuangquan Wang ◽  
Huiyong Sun ◽  
Hui Liu ◽  
Dan Li ◽  
Youyong Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Learning Approaches

Insights into Machine Learning-based approaches for Virtual Screening in Drug Discovery: Existing strategies and streamlining through FP-CADD

2020 ◽  
Vol 17 ◽  
Author(s):  
Waqar Hussain ◽  
Nouman Rasool ◽  
Yaser Daanial Khan
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Swot Analysis ◽  
Peer Review Process ◽  
Mining Machine ◽  
Support Vector ◽  
Learning Approaches ◽  
Computer Aided ◽  
Drug Designing

Background: Machine learning is an active area of research in computer science by the availability of big data collection of all sorts prompting interest in the development of novel tools for data mining. Machine learning methods have wide applications in computer-aided drug discovery methods. Most incredible approaches to machine learning are used in drug designing, which further aid the process of biological modelling in drug discovery. Mainly, two main categories are present which are Ligand-Based Virtual Screening (LBVS) and Structure-Based Virtual Screening (SBVS), however, the machine learning approaches fall mostly in the category of LBVS. Objectives: This study exposits the major machine learning approaches being used in LBVS. Moreover, we have introduced a protocol named FP-CADD which depicts a 4-steps rule of thumb for drug discovery, the four protocols of computer-aided drug discovery (FP-CADD). Various important aspects along with SWOT analysis of FP-CADD are also discussed in this article. Conclusions: By this thorough study, we have observed that in LBVS algorithms, Support vector machines (SVM) and Random forest (RF) are those which are widely used due to high accuracy and efficiency. These virtual screening approaches have the potential to revolutionize the drug designing field. Also, we believe that the process flow presented in this study, named FP-CADD, can streamline the whole process of computer-aided drug discovery. By adopting this rule, the studies related to drug discovery can be made homogeneous and this protocol can also be considered as an evaluation criterion in the peer-review process of research articles.

Use of machine learning approaches for novel drug discovery

2016 ◽  
Vol 11 (3) ◽  
pp. 225-239 ◽  
Author(s):  
Angélica Nakagawa Lima ◽  
Eric Allison Philot ◽  
Gustavo Henrique Goulart Trossini ◽  
Luis Paulo Barbour Scott ◽  
Vinícius Gonçalves Maltarollo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Learning Approaches ◽  
Novel Drug

scholarly journals Comprehensive ensemble in QSAR prediction for drug discovery

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Sunyoung Kwon ◽  
Ho Bae ◽  
Jeonghee Jo ◽  
Sungroh Yoon
Keyword(s):  
Drug Discovery ◽  
Biological Activities ◽  
Quantitative Structure Activity Relationship ◽  
Ensemble Method ◽  
Single Subject ◽  
Learning Approaches ◽  
Qsar Modeling ◽  
Prediction Limit ◽  
Meta Learning ◽  
Individual Classifier

Abstract Background Quantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities. QSAR modeling is essential for drug discovery, but it has many constraints. Ensemble-based machine learning approaches have been used to overcome constraints and obtain reliable predictions. Ensemble learning builds a set of diversified models and combines them. However, the most prevalent approach random forest and other ensemble approaches in QSAR prediction limit their model diversity to a single subject. Results The proposed ensemble method consistently outperformed thirteen individual models on 19 bioassay datasets and demonstrated superiority over other ensemble approaches that are limited to a single subject. The comprehensive ensemble method is publicly available at http://data.snu.ac.kr/QSAR/. Conclusions We propose a comprehensive ensemble method that builds multi-subject diversified models and combines them through second-level meta-learning. In addition, we propose an end-to-end neural network-based individual classifier that can automatically extract sequential features from a simplified molecular-input line-entry system (SMILES). The proposed individual models did not show impressive results as a single model, but it was considered the most important predictor when combined, according to the interpretation of the meta-learning.

scholarly journals Similarity-Based Methods and Machine Learning Approaches for Target Prediction in Early Drug Discovery: Performance and Scope

2020 ◽  
Vol 21 (10) ◽  
pp. 3585 ◽  
Author(s):  
Neann Mathai ◽  
Johannes Kirchmair
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Target Prediction ◽  
Training Data ◽  
Target Space ◽  
Learning Approach ◽  
Learning Approaches ◽  
Individual Test ◽  
Machine Learning Approach ◽  
The Individual

Computational methods for predicting the macromolecular targets of drugs and drug-like compounds have evolved as a key technology in drug discovery. However, the established validation protocols leave several key questions regarding the performance and scope of methods unaddressed. For example, prediction success rates are commonly reported as averages over all compounds of a test set and do not consider the structural relationship between the individual test compounds and the training instances. In order to obtain a better understanding of the value of ligand-based methods for target prediction, we benchmarked a similarity-based method and a random forest based machine learning approach (both employing 2D molecular fingerprints) under three testing scenarios: a standard testing scenario with external data, a standard time-split scenario, and a scenario that is designed to most closely resemble real-world conditions. In addition, we deconvoluted the results based on the distances of the individual test molecules from the training data. We found that, surprisingly, the similarity-based approach generally outperformed the machine learning approach in all testing scenarios, even in cases where queries were structurally clearly distinct from the instances in the training (or reference) data, and despite a much higher coverage of the known target space.

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