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Inductive transfer learning for molecular activity prediction: Next-Gen QSAR Models with MolPMoFiT
2020
Journal of Cheminformatics
Deep neural networks can directly learn from chemical structures without extensive, user-driven selection of descriptors in order to predict molecular properties/activities with high reliability. But these approaches typically require large training sets to learn the endpoint-specific structural features and ensure reasonable prediction accuracy. Even though large datasets are becoming the new normal in drug discovery, especially when it comes to high-throughput screening or metabolomics
doi:10.1186/s13321-020-00430-x
pmid:33430978
fatcat:gbvn7mtgjvaqbgovyhayib6zp4