Calculation of Structure Descriptors

Terfloth, Lothar

doi:10.1002/3527601643.ch8

Cited by 6 publications

(5 citation statements)

References 77 publications

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“…A large number of ligand descriptors has been developed for use in drug discovery and development. Ligand descriptors are typically classified by the dimensionality of the representation of the compound [ 31 ]. So-called zero-dimensional (0D) descriptors are derived from the chemical formula, and include simple atom counts and molecular weight.…”

Section: Resultsmentioning

confidence: 99%

A chemogenomics view on protein-ligand spaces

Strömbergsson

Kleywegt

2009

BMC Bioinformatics

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Background: Chemogenomics is an emerging inter-disciplinary approach to drug discovery that combines traditional ligand-based approaches with biological information on drug targets and lies at the interface of chemistry, biology and informatics. The ultimate goal in chemogenomics is to understand molecular recognition between all possible ligands and all possible drug targets. Protein and ligand space have previously been studied as separate entities, but chemogenomics studies deal with large datasets that cover parts of the joint protein-ligand space. Since drug discovery has traditionally focused on ligand optimization, the chemical space has been studied extensively. The protein space has been studied to some extent, typically for the purpose of classification of proteins into functional and structural classes. Since chemogenomics deals not only with ligands but also with the macromolecules the ligands interact with, it is of interest to find means to explore, compare and visualize protein-ligand subspaces.

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Section: Resultsmentioning

confidence: 99%

A chemogenomics view on protein-ligand spaces

Strömbergsson

Kleywegt

2009

BMC Bioinformatics

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“…0D descriptors are related to overall properties such as molecular weight, 1D descriptors include local properties such as fragment counts and number of functional groups, 2D descriptors are mostly related to topology and molecular walk counts, and 3D descriptors are geometrical descriptors and include properties such as radius of gyration and 3D Wiener index. 10 This resulted in 1481 0D-3D ligand descriptors. Of these, only 27 descriptors were used as input to the model (Supporting Information S3).…”

Section: Methodsmentioning

confidence: 99%

“…Traditionally, quantitative structure-activity relationship (QSAR) 3,9 approaches have been applied to accurately predict how strongly one series of chemically related ligands binds to a single protein target using ligand descriptors such as molecular weight and number of hydrogen bond donors/acceptors. 10,11 Unfortunately, QSAR requires large training sets for every single protein target since it does not take advantage of affinity data available for related proteins. Consequently, QSAR methods are unable to predict cross interactions between ligands and "unseen" proteins and are thus not suitable for proteome-wide modeling.…”

Section: Introductionmentioning

confidence: 99%

Interaction Model Based on Local Protein Substructures Generalizes to the Entire Structural Enzyme-Ligand Space

Strömbergsson

Daniluk

Kryshtafovych

et al. 2008

J. Chem. Inf. Model.

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Chemogenomics is a new strategy in in silico drug discovery, where the ultimate goal is to understand molecular recognition for all molecules interacting with all proteins in the proteome. To study such cross interactions, methods that can generalize over proteins that vary greatly in sequence, structure, and function are needed. We present a general quantitative approach to protein-ligand binding affinity prediction that spans the entire structural enzyme-ligand space. The model was trained on a data set composed of all available enzymes cocrystallized with druglike ligands, taken from four publicly available interaction databases, for which a crystal structure is available. Each enzyme was characterized by a set of local descriptors of protein structure that describe the binding site of the cocrystallized ligand. The ligands in the training set were described by traditional QSAR descriptors. To evaluate the model, a comprehensive test set consisting of enzyme structures and ligands was manually curated. The test set contained enzyme-ligand complexes for which no crystal structures were available, and thus the binding modes were unknown. The test set enzymes were therefore characterized by matching their entire structures to the local descriptor library constructed from the training set. Both the training and the test set contained enzyme-ligand complexes from all major enzyme classes, and the enzymes spanned a large range of sequences and folds. The experimental binding affinities (p K i) ranged from 0.5 to 11.9 (0.7-11.0 in the test set). The induced model predicted the binding affinities of the external test set enzyme-ligand complexes with an r (2) of 0.53 and an RMSEP of 1.5. This demonstrates that the use of local descriptors makes it possible to create rough predictive models that can generalize over a wide range of protein targets.

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“…Predicting biologically relevant species serves as a good starting point for suggesting species likely present in samples that have previously gone unannotated. 28,29,[79][80][81][82] Molecular docking (MD) simulations is an extension of pathway predictions that has shown promising insight for predicting interactions in drug discovery pipelines. 83 These capabilities allow for an assessment of interactions and binding efficiencies between small molecules and enzymes.…”

Section: Ai For Missing Analytesmentioning

confidence: 99%

Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments

2021

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The need for holistic molecular measurements to better understand disease initiation, development, diagnosis, and therapy has led to an increasing number of multiomic analyses. The wealth of information available from multiomic assessments, however, requires both the evaluation and interpretation of extremely large data sets, limiting analysis throughput and ease of adoption. Computational methods utilizing artificial intelligence (AI) provide the most promising way to address these challenges, yet despite the conceptual benefits of AI and its successful application in singular omic studies, the widespread use of AI in multiomic studies remains limited. Here, we discuss present and future capabilities of AI techniques in multiomic studies while introducing analytical checks and balances to validate the computational conclusions.

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Calculation of Structure Descriptors

Cited by 6 publications

References 77 publications

A chemogenomics view on protein-ligand spaces

A chemogenomics view on protein-ligand spaces

Interaction Model Based on Local Protein Substructures Generalizes to the Entire Structural Enzyme-Ligand Space

Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments

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