High-Quality Conformer Generation with CONFORGE: Algorithm and Performance Assessment

Seidel, Thomas; Permann, Christian; Wieder, Oliver; Kohlbacher, Stefan M.; Langer, Thierry

doi:10.1021/acs.jcim.3c00563

Cited by 7 publications

(7 citation statements)

References 72 publications

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“…Afterwards, protonation states of nitrogen atoms were adjusted for pH 7.4 according to their pKa values calculated by means of MarvinSketch ( https://docs.chemaxon.com/display/docs/pka-plugin.md , V17.27.0; default settings). Finally, for each ligand a conformer ensemble was generated using the software CONFORGE V1.0.0 ( Seidel et al, 2023 ). CONFORGE was executed with the following settings: max.…”

Section: Methodsmentioning

confidence: 99%

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Probes for the heterogeneity of muscimol binding sites in rat brain

Müller,

Ernst,

Baykuchkarova

et al. 2024

Front. Pharmacol.

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Introduction: The plant-based alkaloid muscimol is a potent agonist of inhibitory GABAA-neurotransmitter receptors. GABAA receptors are a heterogeneous family of pentameric complexes, with 5 out of 19 subunits assembling around the central anion pore. Muscimol is considered to bind to all receptor subtypes at the orthosteric drug binding site at the β+/α− interface. Recently, we observed that the antipsychotic drugs clozapine (CLZ), loxapine (LOX) and chlorpromazine (CPZ) although exerting functional inhibition on multiple GABAA receptor subtypes showed diverging results in displacing 3H-muscimol. While a complete displacement could be observed in hippocampal membranes by bicuculline (BIC), and no displacement with CPZ, the compounds CLZ and LOX competed partially. Non-sigmoidal, complex dose response curves were indicative of multiple sites. In the current study we now aimed to investigate more extensively this heterogeneity of bicuculline sensitive muscimol sites in rat brain.Methods: We tested membranes from four different brain regions (hippocampus, cerebellum, thalamus and striatum) and selected recombinantly expressed subunit combinations with displacement assays. 3H-muscimol displacement was tested with BIC, LOX, CLZ and CPZ. In silico ligand structural analysis and computational docking was performed.Results: We observed a unique pharmacology of each tested compound in the studied brain regions. Combining two of the tested ligands suggests that in striatum all CLZ sites are contained in the pool of LOX sites, while the CPZ sites may in part be non-overlapping with LOX sites. Experiments on recombinantly expressed receptors indicate, that BIC can displace 3H-muscimol from all tested receptors, while LOX and CLZ display different and variable competition indicative of multiple sites. Molecular docking produced structural correlates of the observed diversity of muscimol sites on the basis of bicuculline bound experimental structures.Discussion: These findings indicate that 3H-muscimol binding sites in rat brain are heterogeneous, with different populations of receptors, which are CPZ, LOX or CLZ sensitive or insensitive. These binding sites show a varying distribution in different rat brain regions. Molecular docking suggests that the so-called loop F region of α subunits drives the observed differences.

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Section: Methodsmentioning

confidence: 99%

“…com/display/docs/pka-plugin.md, V17.27.0; default settings). Finally, for each ligand a conformer ensemble was generated using the software CONFORGE V1.0.0 (Seidel et al, 2023). CONFORGE was executed with the following settings: max.…”

Section: Ligand Preparation For Docking Analysismentioning

confidence: 99%

Probes for the heterogeneity of muscimol binding sites in rat brain

Müller,

Ernst,

Baykuchkarova

et al. 2024

Front. Pharmacol.

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“…However, more efficient conformation generation approaches may partly solve this issue and greatly reduce these costs. 150 Another issue is the complexity of different MIL representations and their processing. 9,45 Let B be the number of bags in a training set (e.g., molecules), N the number of instances (e.g., conformations), and D the dimensionality of a feature vector representing the instances.…”

Section: Computational Complexity and Costmentioning

confidence: 99%

“…Conformation generation may become a bottleneck of a whole modeling pipeline and take even more time than model building itself. However, more efficient conformation generation approaches may partly solve this issue and greatly reduce these costs 150 …”

Section: Computational Complexity and Costmentioning

confidence: 99%

Chemical complexity challenge: Is multi‐instance machine learning a solution?

Zankov,

Madzhidov,

Varnek

et al. 2023

WIREs Comput Mol Sci

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Molecules are complex dynamic objects that can exist in different molecular forms (conformations, tautomers, stereoisomers, protonation states, etc.) and often it is not known which molecular form is responsible for observed physicochemical and biological properties of a given molecule. This raises the problem of the selection of the correct molecular form for machine learning modeling of target properties. The same problem is common to biological molecules (RNA, DNA, proteins)—long sequences where only key segments, which often cannot be located precisely, are involved in biological functions. Multi‐instance machine learning (MIL) is an efficient approach for solving problems where objects under study cannot be uniquely represented by a single instance, but rather by a set of multiple alternative instances. Multi‐instance learning was formalized in 1997 and motivated by the problem of conformation selection in drug activity prediction tasks. Since then MIL has found a lot of applications in various domains, such as information retrieval, computer vision, signal processing, bankruptcy prediction, and so on. In the given review we describe the MIL framework and its applications to the tasks associated with ambiguity in the representation of small and biological molecules in chemoinformatics and bioinformatics. We have collected examples that demonstrate the advantages of MIL over the traditional single‐instance learning (SIL) approach. Special attention was paid to the ability of MIL models to identify key instances responsible for a modeling property.This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

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“…These algorithms facilitate conformational sampling in both the gas and solution phases, allowing for a more thorough exploration of molecular flexibility. These include Confort, 86 ROTATE, 87 CON-FECT, 88 Catalyst, 89,90 MED-3DMC, 91 Multiconf-DOCK, 92 CONFECT, 93 BRIKARD, 94 ForceGen, 95 TCG (TrixX Conformer Generator), 96 and Cxcalc (ChemAxon). 97 These tools utilize a range of algorithms and methodologies to explore the conformational space of molecules and generate conformational ensembles.…”

Section: ■ Introductionmentioning

confidence: 99%

Molecular Gas-Phase Conformational Ensembles

Das,

Merz

2024

J. Chem. Inf. Model.

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Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility–mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.

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High-Quality Conformer Generation with CONFORGE: Algorithm and Performance Assessment

Cited by 7 publications

References 72 publications

Probes for the heterogeneity of muscimol binding sites in rat brain

Probes for the heterogeneity of muscimol binding sites in rat brain

Chemical complexity challenge: Is multi‐instance machine learning a solution?

Molecular Gas-Phase Conformational Ensembles

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