structure data files sdf Search Results


sdf files of structurally diverse small molecules  (Chemdiv Inc)
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structured data file (sdf) format  (MolPort Inc)
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Structured Data File (Sdf) Format, supplied by MolPort Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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sdf (structure-data file) data  (Namiki Shoji Co)
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Sdf (Structure Data File) Data, supplied by Namiki Shoji Co, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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2d compound structure definition files (sdf)  (Chemdiv Inc)
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structure-data file (sdf)  (AUTODOCK GmbH)
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AUTODOCK GmbH structure-data file (sdf)
Structure Data File (Sdf), supplied by AUTODOCK GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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2d structural data format (sdf)  (Chemdiv Inc)
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Chemdiv Inc 2d structural data format (sdf)
2d Structural Data Format (Sdf), supplied by Chemdiv Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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metabolite structures  (Human Metabolome Technologies America)
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Human Metabolome Technologies America metabolite structures
A , Atlas construction workflow. The Human Metabolome Database (HMDB; ∼217,000 compounds) was screened against the human GPCR superfamily using G-LEAP, generating over 120 million putative endogenous ligand–receptor interaction predictions to construct a global atlas. B, Recall performance on known endogenous ligand–receptor pairs. Using 252 annotated pairs from the IUPHAR/BPS Guide to Pharmacology as ground truth, G-LEAP successfully retrieves the correct ligand within the top 1% of the ranked metabolome list for 33.7% of test cases (known pairs), and within the top 5% for 47.2% of cases. This represents a ∼34-fold enrichment over random selection. C, Validation with well-characterized receptor–ligand pairs. Scatter plots show G-LEAP prediction scores (y-axis) versus cross-validation standard deviation (CV-SD, x-axis) for all ∼217,000 HMDB metabolites screened against melatonin receptor type 1A (MTNR1A, left), and serotonin receptor 2A (HTR2A, right). Blue dots represent individual metabolites. The known endogenous ligands (melatonin and serotonin) are indicated as red stars positioned at the top-left of each plot, exhibiting high prediction scores (> 0.95) and low uncertainty (CV-SD < 0.05). This demonstrates G-LEAP’s ability to correctly prioritize true ligands from large <t>metabolite</t> libraries. D, Structural specificity of GPR32, for which Resolvin D1 has been proposed as a candidate endogenous ligand . Chemical structures are shown for Resolvin D1 (left; prediction score: 0.953 ± 0.029) and structurally related lipid mediators enriched among top-ranked candidates: Resolvin D5 (middle; score: 0.953 ± 0.033; Tanimoto similarity to Resolvin D1: 0.714) and 15-Epi-lipoxin B5 (right; score: 0.953 ± 0.034; Tanimoto similarity: 0.531). Tanimoto similarity scores reflect chemical relatedness to Resolvin D1, confirming G-LEAP extracts pharmacophore-like features aligned with structural constraints of lipid-binding GPCRs. E, High-confidence predictions for orphan receptors. Top-ranked metabolite candidates with high prediction scores and low uncertainty for three representative Class A orphan receptors, namely GPR26, GPR20, and GPR176. These predictions align with phylogenetic relationships and provide testable hypotheses for experimental de-orphanization efforts.
Metabolite Structures, supplied by Human Metabolome Technologies America, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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structure data file (.sdf) format  (Leadscope Inc)
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Leadscope Inc structure data file (.sdf) format
A , Atlas construction workflow. The Human Metabolome Database (HMDB; ∼217,000 compounds) was screened against the human GPCR superfamily using G-LEAP, generating over 120 million putative endogenous ligand–receptor interaction predictions to construct a global atlas. B, Recall performance on known endogenous ligand–receptor pairs. Using 252 annotated pairs from the IUPHAR/BPS Guide to Pharmacology as ground truth, G-LEAP successfully retrieves the correct ligand within the top 1% of the ranked metabolome list for 33.7% of test cases (known pairs), and within the top 5% for 47.2% of cases. This represents a ∼34-fold enrichment over random selection. C, Validation with well-characterized receptor–ligand pairs. Scatter plots show G-LEAP prediction scores (y-axis) versus cross-validation standard deviation (CV-SD, x-axis) for all ∼217,000 HMDB metabolites screened against melatonin receptor type 1A (MTNR1A, left), and serotonin receptor 2A (HTR2A, right). Blue dots represent individual metabolites. The known endogenous ligands (melatonin and serotonin) are indicated as red stars positioned at the top-left of each plot, exhibiting high prediction scores (> 0.95) and low uncertainty (CV-SD < 0.05). This demonstrates G-LEAP’s ability to correctly prioritize true ligands from large <t>metabolite</t> libraries. D, Structural specificity of GPR32, for which Resolvin D1 has been proposed as a candidate endogenous ligand . Chemical structures are shown for Resolvin D1 (left; prediction score: 0.953 ± 0.029) and structurally related lipid mediators enriched among top-ranked candidates: Resolvin D5 (middle; score: 0.953 ± 0.033; Tanimoto similarity to Resolvin D1: 0.714) and 15-Epi-lipoxin B5 (right; score: 0.953 ± 0.034; Tanimoto similarity: 0.531). Tanimoto similarity scores reflect chemical relatedness to Resolvin D1, confirming G-LEAP extracts pharmacophore-like features aligned with structural constraints of lipid-binding GPCRs. E, High-confidence predictions for orphan receptors. Top-ranked metabolite candidates with high prediction scores and low uncertainty for three representative Class A orphan receptors, namely GPR26, GPR20, and GPR176. These predictions align with phylogenetic relationships and provide testable hypotheses for experimental de-orphanization efforts.
Structure Data File (.Sdf) Format, supplied by Leadscope Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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sdf file containing energy optimized threedimensional structures  (ChemAxon LLC)
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ChemAxon LLC sdf file containing energy optimized threedimensional structures
A , Atlas construction workflow. The Human Metabolome Database (HMDB; ∼217,000 compounds) was screened against the human GPCR superfamily using G-LEAP, generating over 120 million putative endogenous ligand–receptor interaction predictions to construct a global atlas. B, Recall performance on known endogenous ligand–receptor pairs. Using 252 annotated pairs from the IUPHAR/BPS Guide to Pharmacology as ground truth, G-LEAP successfully retrieves the correct ligand within the top 1% of the ranked metabolome list for 33.7% of test cases (known pairs), and within the top 5% for 47.2% of cases. This represents a ∼34-fold enrichment over random selection. C, Validation with well-characterized receptor–ligand pairs. Scatter plots show G-LEAP prediction scores (y-axis) versus cross-validation standard deviation (CV-SD, x-axis) for all ∼217,000 HMDB metabolites screened against melatonin receptor type 1A (MTNR1A, left), and serotonin receptor 2A (HTR2A, right). Blue dots represent individual metabolites. The known endogenous ligands (melatonin and serotonin) are indicated as red stars positioned at the top-left of each plot, exhibiting high prediction scores (> 0.95) and low uncertainty (CV-SD < 0.05). This demonstrates G-LEAP’s ability to correctly prioritize true ligands from large <t>metabolite</t> libraries. D, Structural specificity of GPR32, for which Resolvin D1 has been proposed as a candidate endogenous ligand . Chemical structures are shown for Resolvin D1 (left; prediction score: 0.953 ± 0.029) and structurally related lipid mediators enriched among top-ranked candidates: Resolvin D5 (middle; score: 0.953 ± 0.033; Tanimoto similarity to Resolvin D1: 0.714) and 15-Epi-lipoxin B5 (right; score: 0.953 ± 0.034; Tanimoto similarity: 0.531). Tanimoto similarity scores reflect chemical relatedness to Resolvin D1, confirming G-LEAP extracts pharmacophore-like features aligned with structural constraints of lipid-binding GPCRs. E, High-confidence predictions for orphan receptors. Top-ranked metabolite candidates with high prediction scores and low uncertainty for three representative Class A orphan receptors, namely GPR26, GPR20, and GPR176. These predictions align with phylogenetic relationships and provide testable hypotheses for experimental de-orphanization efforts.
Sdf File Containing Energy Optimized Threedimensional Structures, supplied by ChemAxon LLC, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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A , Atlas construction workflow. The Human Metabolome Database (HMDB; ∼217,000 compounds) was screened against the human GPCR superfamily using G-LEAP, generating over 120 million putative endogenous ligand–receptor interaction predictions to construct a global atlas. B, Recall performance on known endogenous ligand–receptor pairs. Using 252 annotated pairs from the IUPHAR/BPS Guide to Pharmacology as ground truth, G-LEAP successfully retrieves the correct ligand within the top 1% of the ranked metabolome list for 33.7% of test cases (known pairs), and within the top 5% for 47.2% of cases. This represents a ∼34-fold enrichment over random selection. C, Validation with well-characterized receptor–ligand pairs. Scatter plots show G-LEAP prediction scores (y-axis) versus cross-validation standard deviation (CV-SD, x-axis) for all ∼217,000 HMDB metabolites screened against melatonin receptor type 1A (MTNR1A, left), and serotonin receptor 2A (HTR2A, right). Blue dots represent individual metabolites. The known endogenous ligands (melatonin and serotonin) are indicated as red stars positioned at the top-left of each plot, exhibiting high prediction scores (> 0.95) and low uncertainty (CV-SD < 0.05). This demonstrates G-LEAP’s ability to correctly prioritize true ligands from large metabolite libraries. D, Structural specificity of GPR32, for which Resolvin D1 has been proposed as a candidate endogenous ligand . Chemical structures are shown for Resolvin D1 (left; prediction score: 0.953 ± 0.029) and structurally related lipid mediators enriched among top-ranked candidates: Resolvin D5 (middle; score: 0.953 ± 0.033; Tanimoto similarity to Resolvin D1: 0.714) and 15-Epi-lipoxin B5 (right; score: 0.953 ± 0.034; Tanimoto similarity: 0.531). Tanimoto similarity scores reflect chemical relatedness to Resolvin D1, confirming G-LEAP extracts pharmacophore-like features aligned with structural constraints of lipid-binding GPCRs. E, High-confidence predictions for orphan receptors. Top-ranked metabolite candidates with high prediction scores and low uncertainty for three representative Class A orphan receptors, namely GPR26, GPR20, and GPR176. These predictions align with phylogenetic relationships and provide testable hypotheses for experimental de-orphanization efforts.

Journal: bioRxiv

Article Title: Geometric-Evolutionary Deep Learning Decodes the Human GPCR-Metabolome Interactome and Enables Systematic De-Orphanization

doi: 10.64898/2026.02.21.707232

Figure Lengend Snippet: A , Atlas construction workflow. The Human Metabolome Database (HMDB; ∼217,000 compounds) was screened against the human GPCR superfamily using G-LEAP, generating over 120 million putative endogenous ligand–receptor interaction predictions to construct a global atlas. B, Recall performance on known endogenous ligand–receptor pairs. Using 252 annotated pairs from the IUPHAR/BPS Guide to Pharmacology as ground truth, G-LEAP successfully retrieves the correct ligand within the top 1% of the ranked metabolome list for 33.7% of test cases (known pairs), and within the top 5% for 47.2% of cases. This represents a ∼34-fold enrichment over random selection. C, Validation with well-characterized receptor–ligand pairs. Scatter plots show G-LEAP prediction scores (y-axis) versus cross-validation standard deviation (CV-SD, x-axis) for all ∼217,000 HMDB metabolites screened against melatonin receptor type 1A (MTNR1A, left), and serotonin receptor 2A (HTR2A, right). Blue dots represent individual metabolites. The known endogenous ligands (melatonin and serotonin) are indicated as red stars positioned at the top-left of each plot, exhibiting high prediction scores (> 0.95) and low uncertainty (CV-SD < 0.05). This demonstrates G-LEAP’s ability to correctly prioritize true ligands from large metabolite libraries. D, Structural specificity of GPR32, for which Resolvin D1 has been proposed as a candidate endogenous ligand . Chemical structures are shown for Resolvin D1 (left; prediction score: 0.953 ± 0.029) and structurally related lipid mediators enriched among top-ranked candidates: Resolvin D5 (middle; score: 0.953 ± 0.033; Tanimoto similarity to Resolvin D1: 0.714) and 15-Epi-lipoxin B5 (right; score: 0.953 ± 0.034; Tanimoto similarity: 0.531). Tanimoto similarity scores reflect chemical relatedness to Resolvin D1, confirming G-LEAP extracts pharmacophore-like features aligned with structural constraints of lipid-binding GPCRs. E, High-confidence predictions for orphan receptors. Top-ranked metabolite candidates with high prediction scores and low uncertainty for three representative Class A orphan receptors, namely GPR26, GPR20, and GPR176. These predictions align with phylogenetic relationships and provide testable hypotheses for experimental de-orphanization efforts.

Article Snippet: For the systematic de-orphanization of GPCRs, we retrieved metabolite structures from the Human Metabolome Database [ ] (accessed July 25, 2025).

Techniques: Construct, Selection, Biomarker Discovery, Standard Deviation, Binding Assay