| scholarly article | Q13442814 |
| P356 | DOI | 10.1016/J.EJMECH.2019.111981 |
| P698 | PubMed publication ID | 31978780 |
| P50 | author | Mario Roberto Senger | Q48265449 |
| Lauro Ribeiro de Souza Neto | Q92919575 | ||
| P2093 | author name string | Sabrina Baptista Ferreira | |
| Carolina Horta Andrade | |||
| Alfeu Zanotto-Filho | |||
| Bruno Junior Neves | |||
| Floriano Paes Silva-Junior | |||
| Jonathan Paulo Agnes | |||
| Marcelo do Nascimento Gomes | |||
| Marcio Roberto Henriques Donza | |||
| Marina Delgobo | |||
| Rosângela Mayer Gonçalves | |||
| P2860 | cites work | KNIME-CDK: Workflow-driven cheminformatics | Q21284307 |
| QSAR modeling: where have you been? Where are you going to? | Q28222668 | ||
| Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds | Q28386501 | ||
| Open-source platform to benchmark fingerprints for ligand-based virtual screening | Q28681290 | ||
| Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research | Q28748220 | ||
| The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service | Q28837925 | ||
| Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52 | Q30066516 | ||
| Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation | Q31106801 | ||
| Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization | Q35533343 | ||
| QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity | Q38743096 | ||
| Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening | Q39300204 | ||
| Curation of chemogenomics data | Q44858653 | ||
| Random forest: a classification and regression tool for compound classification and QSAR modeling | Q47370701 | ||
| P4510 | describes a project that uses | machine learning | Q2539 |
| P921 | main subject | machine learning | Q2539 |
| P304 | page(s) | 111981 | |
| P577 | publication date | 2019-12-19 | |
| P1433 | published in | European Journal of Medicinal Chemistry | Q3008624 |
| P1476 | title | Efficient identification of novel anti-glioma lead compounds by machine learning models | |
| P478 | volume | 189 |
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