Use of machine learning approaches for novel drug discovery

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.

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Machine Learning Methods in Drug Discovery

Molecules ◽

10.3390/molecules25225277 ◽

2020 ◽

Vol 25 (22) ◽

pp. 5277

Author(s):

Lauv Patel ◽

Tripti Shukla ◽

Xiuzhen Huang ◽

David W. Ussery ◽

Shanzhi Wang

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Drug Discovery ◽

High Throughput Screening ◽

Drug Targets ◽

Learning Algorithms ◽

Machine Learning Techniques ◽

Online Information ◽

Drug Candidates ◽

Novel Drug

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.

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Improving drug discovery using image-based multiparametric analysis of epigenetic landscape

10.1101/541151 ◽

2019 ◽

Author(s):

Chen Farhy ◽

Luis Orozco ◽

Fu-Yue Zeng ◽

Ian Pass ◽

Jarkko Ylanko ◽

...

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Nuclear Staining ◽

Response Curves ◽

Efficient Detection ◽

Novel Approach ◽

Novel Drug ◽

Multiple Cells ◽

Screening Approaches ◽

Target Effects

AbstractWith the advent of automatic cell imaging and machine learning, high-content phenotypic screening has become the approach of choice for drug discovery due to its ability to extract drug specific multilayered data and compare it to known profiles. In the field of epigenetics such screening approaches has suffered from the lack of tools sensitive to selective epigenetic perturbations. Here we describe a novel approach Microscopic Imaging of Epigenetic Landscapes (MIEL) that captures patterns of nuclear staining of epigenetic marks (e.g. acetylated and methylated histones) and employs machine learning to accurately distinguish between such patterns (1). We demonstrated that MIEL has superior resolution compared to conventional intensity thresholding techniques and enables efficient detection of epigenetically active compounds, function-based classification, flagging possible off-target effects and even predict novel drug function. We validated MIEL platform across multiple cells lines and using dose-response curves to insure the robustness of this approach for the high content high throughput drug discovery.

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16. Cheminformatics techniques in antimalarial drug discovery and development from natural products 2: Molecular scaffold and machine learning approaches

Fundamental Concepts ◽

10.1515/9783110579352-017 ◽

2020 ◽

pp. 397-416

Keyword(s):

Machine Learning ◽

Natural Products ◽

Drug Discovery ◽

Antimalarial Drug ◽

Learning Approaches ◽

Molecular Scaffold ◽

Drug Discovery And Development

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

Molecular Pharmaceutics ◽

10.1021/acs.molpharmaceut.6b00471 ◽

2016 ◽

Vol 13 (8) ◽

pp. 2855-2866 ◽

Cited By ~ 44

Author(s):

Shuangquan Wang ◽

Huiyong Sun ◽

Hui Liu ◽

Dan Li ◽

Youyong Li ◽

...

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Learning Approaches

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

Current Drug Discovery Technologies ◽

10.2174/1570163817666200806165934 ◽

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.

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Molecular Docking for Drug Discovery: Machine-Learning Approaches for Native Pose Prediction of Protein-Ligand Complexes

Computational Intelligence Methods for Bioinformatics and Biostatistics - Lecture Notes in Computer Science ◽

10.1007/978-3-319-09042-9_2 ◽

2014 ◽

pp. 15-32

Author(s):

Hossam M. Ashtawy ◽

Nihar R. Mahapatra

Keyword(s):

Machine Learning ◽

Molecular Docking ◽

Drug Discovery ◽

Pose Prediction ◽

Learning Approaches

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ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches

Molecular Pharmaceutics ◽

10.1021/acs.molpharmaceut.7b00631 ◽

2017 ◽

Vol 14 (11) ◽

pp. 3935-3953 ◽

Cited By ~ 26

Author(s):

Tailong Lei ◽

Huiyong Sun ◽

Yu Kang ◽

Feng Zhu ◽

Hui Liu ◽

...

Keyword(s):

Machine Learning ◽

Urinary Tract ◽

Drug Discovery ◽

Learning Approaches ◽

Reliable Prediction

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

International Journal of Molecular Sciences ◽

10.3390/ijms21103585 ◽

2020 ◽

Vol 21 (10) ◽

pp. 3585 ◽

Cited By ~ 3

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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Trends in application of advancing computational approaches in GPCR ligand discovery

Experimental Biology and Medicine ◽

10.1177/1535370221993422 ◽

2021 ◽

pp. 153537022199342

Author(s):

Siyu Zhu ◽

Meixian Wu ◽

Ziwei Huang ◽

Jing An

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Olfactory Receptors ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Drug Induced ◽

Protein Targets ◽

Hematological Disorders ◽

Biased Agonism ◽

Ligand Discovery

G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.

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