Fingerprint-based consensus virtual screening towards structurally new 5-HT6R ligands

Smusz, Sabina; Kurczab, Rafał; Satała, Grzegorz; Bojarski, Andrzej J.

doi:10.1016/j.bmcl.2015.03.049

Cited by 17 publications

(15 citation statements)

References 61 publications

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“…Apart from ANN-driven LBVS, there are many instances in which different algorithms, such as the SVM method, − and Bayesian inference − have played an active role in drug classification. There are many publications relating to the successful application of Bayesian models, for example, in the discovery of novel vascular endothelial growth factor receptor-2 scaffolds and in identifying potential farnesoid X receptor agonists, liver X receptor β agonists, ROCK II inhibitors, cyclin-dependent kinase inhibitors, DNA gyrase inhibitors, breast cancer resistance protein (BCRP) inhibitors, , dipeptidyl peptidase IV (DPP-IV) inhibitors, butyrylcholinesterase (BuChE) inhibitors, and inhibitors of β-hematin formation .…”

Section: Artificial Intelligence Approaches In Ligand-based Virtual S...mentioning

confidence: 99%

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

et al. 2019

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Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

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Section: Artificial Intelligence Approaches In Ligand-based Virtual S...mentioning

confidence: 99%

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

et al. 2019

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“…Successes in application of ML-based methods to pharmacological tasks include toxicity 273 or ligand search for serotonin receptors such as 5-HT 6 . 274 However, one of the main obstacles in broader application of ML-based methods, specifically for deep learning approaches, is the weak link between the findings of an ML-based treatment of the problem and the chemical reasoning, i.e. the "cause and effect" problem.…”

Section: Future Developmentsmentioning

confidence: 99%

Machine Learning for Chemical Reactions

2021

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Machine learning (ML) techniques applied to chemical reactions have a long history. The present contribution discusses applications ranging from small molecule reaction dynamics to computational platforms for reaction planning. ML-based techniques can be particularly relevant for problems involving both computation and experiments. For one, Bayesian inference is a powerful approach to develop models consistent with knowledge from experiments. Second, ML-based methods can also be used to handle problems that are formally intractable using conventional approaches, such as exhaustive characterization of state-to-state information in reactive collisions. Finally, the explicit simulation of reactive networks as they occur in combustion has become possible using machine-learned neural network potentials. This review provides an overview of the questions that can and have been addressed using machine learning techniques, and an outlook discusses challenges in this diverse and stimulating field. It is concluded that ML applied to chemistry problems as practiced and conceived today has the potential to transform the way with which the field approaches problems involving chemical reactions, in both research and academic teaching.

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“…Similar studies using machine learning models for drug discovery have increased considerably over the past decade. Nowadays machine learning algorithms like deep learning are garnering considerable attention because of success with complex data, while there are several classical machine learning approaches that are currently also being used with equal or similar predictive power on external sets. , Among them, support vector machines (svm), − naïve-Bayesian classifier, − and random forests (rf) , are the most popular methods.…”

Section: Introductionmentioning

confidence: 99%

Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus

Gawriljuk

Foil

Puhl

et al. 2021

J. Chem. Inf. Model.

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Yellow fever (YF) is an acute viral hemorrhagic disease transmitted by infected mosquitoes. Large epidemics of YF occur when the virus is introduced into heavily populated areas with high mosquito density and low vaccination coverage. The lack of a specific small molecule drug treatment against YF as well as for homologous infections, such as zika and dengue, highlights the importance of these flaviviruses as a public health concern. With the advancement in computer hardware and bioactivity data availability, new tools based on machine learning methods have been introduced into drug discovery, as a means to utilize the growing high throughput screening (HTS) data generated to reduce costs and increase the speed of drug development. The use of predictive machine learning models using previously published data from HTS campaigns or data available in public databases, can enable the selection of compounds with desirable bioactivity and absorption, distribution, metabolism, and excretion profiles. In this study, we have collated cell-based assay data for yellow fever virus from the literature and public databases. The data were used to build predictive models with several machine learning methods that could prioritize compounds for in vitro testing. Five molecules were prioritized and tested in vitro from which we have identified a new pyrazolesulfonamide derivative with EC50 3.2 μM and CC50 24 μM, which represents a new scaffold suitable for hit-to-lead optimization that can expand the available drug discovery candidates for YF.

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Fingerprint-based consensus virtual screening towards structurally new 5-HT6R ligands

Cited by 17 publications

References 61 publications

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Machine Learning for Chemical Reactions

Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus

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