Machine learning in drug discovery and development

AbstractThe exponential increase in our ability to harness multi-dimensional biological and clinical data from experimental to real-world settings has transformed pharmaceutical research and development in recent years, with increasing applications of artificial intelligence (AI) and machine learning (ML). Patient-centered iterative forward and reverse translation is at the heart of precision medicine discovery and development across the continuum from target validation to optimization of pharmacotherapy. Integration of advanced analytics into the practice of Translational Medicine is now a fundamental enabler to fully exploit information contained in diverse sources of big data sets such as “omics” data, as illustrated by deep characterizations of the genome, transcriptome, proteome, metabolome, microbiome, and exposome. In this commentary, we provide an overview of ML applications in drug discovery and development, aligned with the three strategic pillars of Translational Medicine (target, patient, dose) and offer perspectives on their potential to transform the science and practice of the discipline. Opportunities for integrating ML approaches into the discipline of Pharmacometrics are discussed and will revolutionize the practice of model-informed drug discovery and development. Finally, we posit that joint efforts of Clinical Pharmacology, Bioinformatics, and Biomarker Technology experts are vital in cross-functional team settings to realize the promise of AI/ML-enabled Translational and Precision Medicine.

Download Full-text

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

Download Full-text

Application of Machine Leaning in Drug Discovery and Development

Chemoinformatics and Advanced Machine Learning Perspectives ◽

10.4018/978-1-61520-911-8.ch012 ◽

2011 ◽

pp. 235-256 ◽

Cited By ~ 2

Author(s):

Shuxing Zhang

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Model Building ◽

Pharmaceutical Research ◽

Machine Learning Techniques ◽

High Dimensional ◽

Lead Discovery ◽

Drug Discovery And Development ◽

Learning Techniques ◽

Machine Leaning

Machine learning techniques have been widely used in drug discovery and development, particularly in the areas of cheminformatics, bioinformatics and other types of pharmaceutical research. It has been demonstrated they are suitable for large high dimensional data, and the models built with these methods can be used for robust external predictions. However, various problems and challenges still exist, and new approaches are in great need. In this Chapter, the authors will review the current development of machine learning techniques, and especially focus on several machine learning techniques they developed as well as their application to model building, lead discovery via virtual screening, integration with molecular docking, and prediction of off-target properties. The authors will suggest some potential different avenues to unify different disciplines, such as cheminformatics, bioinformatics and systems biology, for the purpose of developing integrated in silico drug discovery and development approaches.

Download Full-text

Applications of machine learning and computational intelligence to drug discovery and development

Drug Development Research ◽

10.1002/ddr.20402 ◽

2010 ◽

Vol 72 (1) ◽

pp. 53-65 ◽

Cited By ~ 11

Author(s):

David Hecht

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Computational Intelligence ◽

Drug Discovery And Development ◽

Applications Of Machine Learning

Download Full-text

Small Patient Datasets Reveal Genetic Drivers of Non-Small Cell Lung Cancer Subtypes using a Novel Machine Learning Approach

10.1101/2021.07.27.21261075 ◽

2021 ◽

Author(s):

Moses P Cook ◽

Bessi Qorri ◽

Amruth Baskar ◽

Jalal Ziauddin ◽

Luca Pani ◽

...

Keyword(s):

Machine Learning ◽

Lung Cancer ◽

Drug Discovery ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

Learning Approach ◽

Small Cell Lung ◽

Drug Discovery And Development ◽

Machine Learning Approach

There are many small datasets of significant value in the medical space that are being underutilized. Due to the heterogeneity of complex disorders found in oncology, systems capable of discovering patient subpopulations while elucidating etiologies is of great value as it can indicate leads for innovative drug discovery and development. Here, we report on a machine intelligence-based study that utilized a combination of two small non-small cell lung cancer (NSCLC) datasets consisting of 58 samples of adenocarcinoma (ADC) and squamous cell carcinoma (SCC) and 45 samples from the gene expression analysis of human lung cancer and control samples series (GSE18842). Utilizing a novel machine learning approach, we were able to uncover subpopulations of ADC and SCC while simultaneously extracting which genes, in combination, were significantly involved in defining the subpopulations. An interactive hypothesis-generating interface designed to work with machine learning methods allowed us to explore the hypotheses generated by the unsupervised components of the system. Using these methods, we were able to uncover genes implicated by other methods and accurately discover known subpopulations without being asked, such as different levels of aggressiveness within the SCC and ADC subtypes. Furthermore, PIGX was a novel gene implicated in this study that warrants further study due to its role in breast cancer proliferation. Here we demonstrate the ability to learn from small datasets and reveal well-established properties of NSCLC. These machine learning techniques can reveal the driving factors behind subpopulations of patients altering the approach to drug discovery and development by making precision medicine a reality.

Download Full-text

Machine Learning Techniques in Drug Discovery and Development

International Journal of Applied Research ◽

10.22271/allresearch.2021.v7.i4a.8455 ◽

2021 ◽

Vol 7 (4) ◽

pp. 21-28

Author(s):

Ravi Manne

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Machine Learning Techniques ◽

Drug Discovery And Development ◽

Learning Techniques

Download Full-text

Application of Machine Leaning in Drug Discovery and Development

Machine Learning ◽

10.4018/978-1-60960-818-7.ch517 ◽

2012 ◽

pp. 1460-1482

Author(s):

Shuxing Zhang

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Model Building ◽

Pharmaceutical Research ◽

Machine Learning Techniques ◽

High Dimensional ◽

Lead Discovery ◽

Drug Discovery And Development ◽

Learning Techniques ◽

Machine Leaning

Machine learning techniques have been widely used in drug discovery and development, particularly in the areas of cheminformatics, bioinformatics and other types of pharmaceutical research. It has been demonstrated they are suitable for large high dimensional data, and the models built with these methods can be used for robust external predictions. However, various problems and challenges still exist, and new approaches are in great need. In this Chapter, the authors will review the current development of machine learning techniques, and especially focus on several machine learning techniques they developed as well as their application to model building, lead discovery via virtual screening, integration with molecular docking, and prediction of off-target properties. The authors will suggest some potential different avenues to unify different disciplines, such as cheminformatics, bioinformatics and systems biology, for the purpose of developing integrated in silico drug discovery and development approaches.

Download Full-text