PyGOLD: a python based API for docking based virtual screening workflow generation

Patel, Hitesh; Brinkjost, Tobias; Koch, Oliver

doi:10.1093/bioinformatics/btx197

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…This can be further applied in a straightforward manner to several other proteins from the PDB databank with ChEMBL bioactivity data. Even the docking procedure could be automated using PyGOLD, a python-based API for automated docking workflow generation . This would lead to a large coverage of targets and the development of a complete prediction tool.…”

Section: Discussionmentioning

confidence: 99%

“…Even the docking procedure could be automated using PyGOLD, a python-based API for automated docking workflow generation. 65 This would lead to a large coverage of targets and the development of a complete prediction tool. In addition, it should also be possible to train multitarget models using different protein structures of the same target to represent different conformations and protein flexibility.…”

Section: ■ Summary and Conclusionmentioning

confidence: 99%

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The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction

Nogueira

Koch

2019

J. Chem. Inf. Model.

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The identification of possible targets for a known bioactive compound is of the utmost importance for drug design and development. Molecular docking is one possible approach for in-silico protein target prediction, whereas a molecule is docked into several different protein structures to identify potential targets. This reverse docking approach is hampered by the limitation of current scoring functions to correctly discriminate between targets and nontargets. In this work, a development of target-specific scoring functions is described that showed improved prediction performances for the correct target prediction of both actives and decoys on three validation data sets. In contrast to pure ligand-based approaches, that are in general faster and include a greater target space, docking-based approaches can cover also unknown chemical space that lies outside the known bioactivity data. These target-specific scoring functions are based on known bioactivity data retrieved from ChEMBL and supervised machine learning approaches. Neural Networks and Support Vector Machines (SVMs) models were trained for 20 different protein targets. Our protein–ligand interaction fingerprint PADIF (Protein Atom Score Contributions Derived Interaction Fingerprint) represents the input for training, whereas the PADIFs are calculated based on docking poses of active and inactive compounds. Different data sets of previously unseen molecules were used for the final evaluation and analysis of the prediction performance of the created models. For a single-target selectivity data set, the correct target model returns in most of the cases the highest probabilities scores for their active molecules and with statistically significant differences from the other targets. These probability scores were also predicted and successfully used to rank the targets for molecules of a multitarget data set with activity data described simultaneously for two, three, and four to seven protein targets.

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Section: Discussionmentioning

confidence: 99%

Section: ■ Summary and Conclusionmentioning

confidence: 99%

The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction

Nogueira

Koch

2019

J. Chem. Inf. Model.

Self Cite

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“…The computational VS methods can be grouped into two categories according to the information they use: protein-centric methods and ligand-centric methods (Roy and Skolnick, 2015). Starting from protein structures, the protein-centric methods can often achieve a better screening performance, as they enable the explicit evaluation of protein-ligand binding interactions through docking (Forli et al, 2016;Patel et al, 2017). Nevertheless, the performance highly depends on the quality of the receptor structure, as low-resolution models from protein structure predictions can often degrade the accuracy of the process when the experimental structure is not available (Zhang, 2009).…”

Section: Introductionmentioning

confidence: 99%

LS-align: an atom-level, flexible ligand structural alignment algorithm for high-throughput virtual screening

et al. 2018

Bioinformatics

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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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PyGOLD: a python based API for docking based virtual screening workflow generation

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 4 publications

References 9 publications

The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction

The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction

LS-align: an atom-level, flexible ligand structural alignment algorithm for high-throughput virtual screening

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

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