| scholarly article | Q13442814 |
| P2093 | author name string | Hao Zhu | |
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| Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do. | Q38687125 | ||
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| Deep-Learning-Based Drug-Target Interaction Prediction | Q39586701 | ||
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| Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening | Q40399170 | ||
| High-throughput screening for drug discovery | Q40920298 | ||
| HIV-1 reverse transcriptase inhibitor design using artificial neural networks | Q41449185 | ||
| Deep neural nets as a method for quantitative structure-activity relationships | Q41506565 | ||
| Alzheimer's disease: From big data to mechanism | Q42590102 | ||
| Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis | Q42646937 | ||
| Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection | Q44704169 | ||
| Impact of high-throughput screening in biomedical research | Q45352809 | ||
| Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction. | Q45945075 | ||
| From machine learning to deep learning: progress in machine intelligence for rational drug discovery. | Q45945808 | ||
| Identification of possible sources of nanotoxicity from carbon nanotubes inserted into membrane bilayers using membrane interaction quantitative structure--activity relationship analysis | Q46812428 | ||
| P921 | main subject | artificial intelligence | Q11660 |
| big data | Q858810 | ||
| drug discovery | Q1418791 | ||
| P304 | page(s) | 573-589 | |
| P577 | publication date | 2019-09-13 | |
| P1433 | published in | Annual Review of Pharmacology and Toxicology | Q567365 |
| P1476 | title | Big Data and Artificial Intelligence Modeling for Drug Discovery | |
| P478 | volume | 60 |
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| Q114369129 | GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics |
| Q98303153 | Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches |
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