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Large-Scale QSAR in Target Prediction and Phenotypic HTS Assessment
1Developmental and Molecular Pathways, Quantitative Biology, Novartis Institutes for BioMedical Research, 220 Massachusetts Ave., Cambridge, MA 02139 phone: 617-871-7155. jeremy.jenkins@novartis.com.
Molecular Informatics
|August 2, 2016
Summary
In silico compound target prediction transforms high-throughput screening (HTS) by using quantitative structure-activity relationship (QSAR) models. This enables sophisticated annotation of HTS hits with predicted targets for better drug discovery.
Area of Science:
- Computational chemistry and cheminformatics
- Drug discovery and development
- Bioinformatics and systems biology
Background:
- Traditional quantitative structure-activity relationship (QSAR) models are primarily used for lead optimization and binding affinity predictions.
- Large-scale bioactivity data integration and standardization are crucial for advancing computational approaches in drug discovery.
- Phenotypic high-throughput screening (HTS) generates vast amounts of data that require efficient analysis and interpretation.
Purpose of the Study:
- To explore the paradigm shift offered by in silico compound target prediction in understanding and strategically utilizing large compound collections.
- To enable sophisticated hit assessment in phenotypic HTS by annotating hits with both known and predicted targets.
- To leverage large-scale QSAR models for global, probabilistic target predictions across thousands of human proteins.
Main Methods:
- Development and application of large-scale quantitative structure-activity relationship (QSAR) models.
- Integration and standardization of massive bioactivity datasets.
- In silico annotation of phenotypic high-throughput screening (HTS) hits with predicted compound targets.
Main Results:
- Demonstrated the potential of in silico target prediction to significantly enhance hit assessment in HTS.
- Enabled the annotation of phenotypic HTS hits with predicted targets beyond experimentally validated ones.
- Facilitated a shift from traditional QSAR applications to global, probabilistic target predictions for numerous human proteins.
Conclusions:
- In silico compound target prediction represents a significant advancement in drug discovery, moving beyond traditional QSAR roles.
- The strategic use of predicted targets enhances the interpretation and utility of phenotypic HTS data.
- Continued efforts in data integration and model development will further empower computational approaches in identifying novel therapeutic agents.
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