Drug Discovery: An Overview

Terfloth, Lothar; Spycher, Simon; Gasteiger, Johann

doi:10.1002/9783527806539.ch6a

Cited by 4 publications

(3 citation statements)

References 59 publications

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“…An overview is available. [58] Quite many books have appeared that deal with computer-assisted drug discovery and development; in fact, so many excellent books have been published that none will here be picked out for recommendation. The reader is advised to search the internet for a book that will meet his/her interest most.…”

Section: Drug Discoverymentioning

confidence: 99%

Chemistry in Times of Artificial Intelligence

Gasteiger

2020

ChemPhysChem

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Chemists have to a large extent gained their knowledge by doing experiments and thus gather data. By putting various data together and then analyzing them, chemists have fostered their understanding of chemistry. Since the 1960s, computer methods have been developed to perform this process from data to information to knowledge. Simultaneously, methods were developed for assisting chemists in solving their fundamental questions such as the prediction of chemical, physical, or biological properties, the design of organic syntheses, and the elucidation of the structure of molecules. This eventually led to a discipline of its own: chemoinformatics. Chemoinformatics has found important applications in the fields of drug discovery, analytical chemistry, organic chemistry, agrichemical research, food science, regulatory science, material science, and process control. From its inception, chemoinformatics has utilized methods from artificial intelligence, an approach that has recently gained more momentum.

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Section: Drug Discoverymentioning

confidence: 99%

Chemistry in Times of Artificial Intelligence

Gasteiger

2020

ChemPhysChem

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“…An acceptable drug candidate should selectively distribute to its intended target tissues (Terfloth et al, 2018). The target tissue is typically identified by the cell membrane receptors, which are further identified by gene expression data (Yang et al, 2018).…”

Section: Prediction Of Tissue Affinity and Cell Localizationmentioning

confidence: 99%

“…The second step in the drug development process is the identification of the lead molecule as a promising potential compound that can lead to a novel drug (Lo et al, 2018). A lead compound is a representative of a series of compounds with drug‐like adequate properties consist of physicochemical properties, selectivity, pharmacodynamics, non‐toxicity, and novelty to develop as a drug (Terfloth, Spycher, & Gasteiger, 2018).…”

Section: Application Of Deep Learning In Drug Discovery Pipelinementioning

confidence: 99%

Spectrum of deep learning algorithms in drug discovery

2020

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Deep learning (DL) algorithms are a subset of machine learning algorithms with the aim of modeling complex mapping between a set of elements and their classes. In parallel to the advance in revealing the molecular bases of diseases, a notable innovation has been undertaken to apply DL in data/libraries management, reaction optimizations, differentiating uncertainties, molecule constructions, creating metrics from qualitative results, and prediction of structures or interactions. From source identification to lead discovery and medicinal chemistry of the drug candidate, drug delivery, and modification, the challenges can be subjected to artificial intelligence algorithms to aid in the generation and interpretation of data. Discovery and design approach, both demand automation, large data management and data fusion by the advance in high‐throughput mode. The application of DL can accelerate the exploration of drug mechanisms, finding novel indications for existing drugs (drug repositioning), drug development, and preclinical and clinical studies. The impact of DL in the workflow of drug discovery, design, and their complementary tools are highlighted in this review. Additionally, the type of DL algorithms used for this purpose, and their pros and cons along with the dominant directions of future research are presented.

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Prediction of matrix metal proteinases-12 inhibitors by machine learning approaches

Yang

et al. 2018

Journal of Biomolecular Structure and Dynamics

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Matrix metal proteinases-12 (MMP-12) is a hot pharmaceutical target on the treatment of many human diseases. There's a crying need for designing and finding new MMP-12 inhibitors. In this work, four machine learning approaches, support vector machine, k-nearest neighbor, C4.5 decision tree, and random forest, were employed to derive statistical models from datasets with well distributed biological activities and predict a compound whether it is a MMP-12 inhibitor. The prediction accuracies of the models are in the range of 96.15-98.08% for sensitivity, 87.23-100.00% for specificity, 91.92-98.99% for the overall prediction accuracy and 0.8401-0.9800 for Matthews correlation coefficient, all producing satisfactory results. By means of diverse feature selection methods, several sets of critical descriptors with key information of inhibitory properties were selected by different models, accelerating the classification for MMP-12 inhibitors and non-inhibitors.

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Drug Discovery: An Overview

Cited by 4 publications

References 59 publications

Chemistry in Times of Artificial Intelligence

Chemistry in Times of Artificial Intelligence

Spectrum of deep learning algorithms in drug discovery

Prediction of matrix metal proteinases-12 inhibitors by machine learning approaches

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