AutoQSAR - Automated Quantitative Structure-Activity Relationship | Tool name | AutoQSAR - Automated Quantitative Structure-Activity Relationship |
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| URL | https://www.schrodinger.com/products/autoqsar |
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| Important features | It automatically handles the generation of molecular descriptors, feature selection, data partitioning, and the training/scoring of multiple statistical models (e.g., partial least squares, random forests, neural networks) to find the best predictive QSAR model. |
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| Citations | N/A |
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| Year of publication | 2023 |
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| Rank by usage frequency | 99 |
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| Comments | Paid / Commercial Subscription |
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| Function | https://www.schrodinger.com/products/autoqsar, It automatically handles the generation of molecular descriptors, feature selection, data partitioning, and the training/scoring of multiple statistical models (e.g., partial least squares, random forests, neural networks) to find the best predictive QSAR model., N/A, 2023, 99, Paid / Commercial Subscription, General, Software, Tuberculosis, Bacterial, Most of the Organisms, Functional, Offline, Yes, Yes, Naidu, A., Nayak, S. S., Lulu S, S., & Sundararajan, V. (2023). Advances in computational frameworks in the fight against TB: The way forward. Frontiers in pharmacology, 14, 1152915. https://doi.org/10.3389/fphar.2023.1152915, PMID: 37077815 , PMCID: PMC10106641, Tuberculosis, Offline, 2 |
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