There are many of pathogen parasite species with different susceptibility profile to antiparasitic drugs. Unfortunately, almost QSAR models predict the biological activity of drugs against only one parasite species. Consequently, predicting the probability with which a drug is active against different species with a single unify model is a goal of the major importance. In so doing, we use Markov more ..
BioActive Compounds: 10
Description:
Title: Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species.
Abstract: There are many of pathogen parasite species with different susceptibility profile to antiparasitic drugs. Unfortunately, almost QSAR models predict the biological activity of drugs against only one parasite species. Consequently, predicting the probability with which a drug is active against different species with a single unify model is a goal of the major importance. In so doing, we use Markov Chains theory to calculate new multi-target spectral moments to fit a QSAR model that predict by the first time a mt-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using spectral moments. The data was processed by linear discriminant analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 311 out of 358 active compounds (86.9%) and 2328 out of 2577 non-active compounds (90.3%) in training series. Overall training performance was 89.9%. Validation of the model was carried out by means of external predicting series. In these series the model classified correctly 157 out 190, 82.6% of antiparasitic compounds and 1151 out of 1277 non-active compounds (90.1%). Overall predictability performance was 89.2%. In addition we developed four types of non Linear Artificial neural networks (ANN) and we compared with the mt-QSAR model. The improved ANN model had an overall training performance was 87%. The present work report the first attempts to calculate within a unify framework probabilities of antiparasitic action of drugs against different parasite species based on spectral moment analysis.
(PMID: 20185316)
Abstract: There are many of pathogen parasite species with different susceptibility profile to antiparasitic drugs. Unfortunately, almost QSAR models predict the biological activity of drugs against only one parasite species. Consequently, predicting the probability with which a drug is active against different species with a single unify model is a goal of the major importance. In so doing, we use Markov Chains theory to calculate new multi-target spectral moments to fit a QSAR model that predict by the first time a mt-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using spectral moments. The data was processed by linear discriminant analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 311 out of 358 active compounds (86.9%) and 2328 out of 2577 non-active compounds (90.3%) in training series. Overall training performance was 89.9%. Validation of the model was carried out by means of external predicting series. In these series the model classified correctly 157 out 190, 82.6% of antiparasitic compounds and 1151 out of 1277 non-active compounds (90.1%). Overall predictability performance was 89.2%. In addition we developed four types of non Linear Artificial neural networks (ANN) and we compared with the mt-QSAR model. The improved ANN model had an overall training performance was 87%. The present work report the first attempts to calculate within a unify framework probabilities of antiparasitic action of drugs against different parasite species based on spectral moment analysis.
(PMID: 20185316)
Comment
Compounds with activity <= 50uM or explicitly reported as active by ChEMBL are flagged as active in this PubChem assay presentation.
Putative Target:
ChEMBL Target ID: 50467
Target Type: ORGANISM
Pref Name: Trichomonas vaginalis
Organism: Trichomonas vaginalis
Tax ID: 5722
Confidence: Target assigned is non-molecular
Relationship Type: Non-molecular target assigned
Putative Target:
ChEMBL Target ID: 50467
Target Type: ORGANISM
Pref Name: Trichomonas vaginalis
Organism: Trichomonas vaginalis
Tax ID: 5722
Confidence: Target assigned is non-molecular
Relationship Type: Non-molecular target assigned
Categorized Comment
ChEMBL Assay Type: Functional
ChEMBL Assay Data Source: Scientific Literature
ChEMBL Assay Data Source: Scientific Literature
Result Definitions
| TID | Name | Description | Histogram | Type | Unit | |
|---|---|---|---|---|---|---|
| Outcome | The BioAssay activity outcome | Outcome | ||||
| 1 | IC50* | IC50 PubChem standard value |  | Float | μM | |
| 2 | IC50 activity comment | IC50 activity comment | String | |||
| 3 | IC50 standard flag | IC50 standard flag | | Integer | ||
| 4 | IC50 qualifier | IC50 qualifier | String | |||
| 5 | IC50 published value | IC50 published value | | Float | nM | |
| 6 | IC50 standard value | IC50 standard value | | Float | nM | |
| 7 | IC50 data validity | IC50 data validity | String |
* Activity Concentration.
Data Table (Concise)
PageFrom: