Ambit-SMIRKS: a software module for reaction representation, reaction search and structure transformation

Kochev, Nikolay; Avramova, Svetlana; Jeliazkova, Nina

doi:10.1186/s13321-018-0295-6

Cited by 18 publications

(17 citation statements)

References 26 publications

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“…The transformations of parent molecules into predicted metabolites based on the defined SMIRKS strings were performed using Ambit-SMIRKS [Kochev et al, 2018; Ambit-SMARTS Java Library, version 3.1.0. http://ambit.sourceforge.net/smirks.html (accessed Oct 4, 2017)].…”

Section: Methodsmentioning

confidence: 99%

GLORY: Generator of the Structures of Likely Cytochrome P450 Metabolites Based on Predicted Sites of Metabolism

et al. 2019

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Computational prediction of xenobiotic metabolism can provide valuable information to guide the development of drugs, cosmetics, agrochemicals, and other chemical entities. We have previously developed FAME 2, an effective tool for predicting sites of metabolism (SoMs). In this work, we focus on the prediction of the chemical structures of metabolites, in particular metabolites of xenobiotics. To this end, we have developed a new tool, GLORY, which combines SoM prediction with FAME 2 and a new collection of rules for metabolic reactions mediated by the cytochrome P450 enzyme family. GLORY has two modes: MaxEfficiency and MaxCoverage. For MaxEfficiency mode, the use of predicted SoMs to restrict the locations in the molecule at which the reaction rules could be applied was explored. For MaxCoverage mode, the predicted SoM probabilities were instead used to develop a new scoring approach for the predicted metabolites. With this scoring approach, GLORY achieves a recall of 0.83 and can predict at least one known metabolite within the top three ranked positions for 76% of the molecules of a new, manually curated test set. GLORY is freely available as a web server at https://acm.zbh.uni-hamburg.de/glory/ , and the datasets and reaction rules are provided in the Supplementary Material .

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Section: Methodsmentioning

confidence: 99%

GLORY: Generator of the Structures of Likely Cytochrome P450 Metabolites Based on Predicted Sites of Metabolism

et al. 2019

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“…https://livertox.univie.ac.at/ Machine learning classification model Identify pharmacokinetic properties [ 405 ] Ambit-SMIRKS A software module for reaction representation, reaction search and structure transformation. http://ambit.sourceforge.net/smirks The Chemistry Development Kit Standardization of large chemical databases and pathway transformation database and prediction [ 406 ] COSMOfrag A Novel Tool for High-Throughput ADME Property Prediction and Similarity Screening Quantum Chemistry In the COSMO − RS picture, any molecular information is gathered in the so-called σ profiles, COSMOfrag replaces the single σ profile with a composition of partial σ profiles, selected by the use of extensive similarity searching algorithms [ 407 ] RosENet Predicting the absolute binding affinity of protein–ligand complexes Convolutional neural networks Combines voxelized molecular mechanics energies and molecular descriptors [ 408 ] MDeePred Novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery. https://github.com/cansyl/MDeePred Deep learning MDeePred is a scalable method with sufficiently high predictive performance [ 409 ] Mode of action and toxicity of compounds ProTox-II Webserver for the prediction of toxicity of chemicals.…”

Section: Applications Of Artificial Intelligence In Drug Development mentioning

confidence: 99%

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

et al. 2021

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Graphic abstract Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screeni...

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“…92 Lastly, we recall the input feature representation of reactions as text strings (SMILES), changes in molecular patterns (SMARTS), and reaction mechanisms (SMIRKS). 93 Each of the described input feature representations led to a trained ML algorithm which accurately predicted the activation energy, yield or reaction rate constant. It is encouraging to see the accuracy of ML predictors trained using system-size independent input features 84 or machine learned difference fingerprint input features.…”

Section: Kinetic Input Features Defined and Tested For Regressionmentioning

confidence: 99%

Progress towards machine learning reaction rate constants

Komp

Janulaitis

Valleau

2022

Phys. Chem. Chem. Phys.

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Ambit-SMIRKS: a software module for reaction representation, reaction search and structure transformation

Cited by 18 publications

References 26 publications

GLORY: Generator of the Structures of Likely Cytochrome P450 Metabolites Based on Predicted Sites of Metabolism

GLORY: Generator of the Structures of Likely Cytochrome P450 Metabolites Based on Predicted Sites of Metabolism

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Progress towards machine learning reaction rate constants

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