| editorial | Q871232 |
| scholarly article | Q13442814 |
| P356 | DOI | 10.1002/MINF.201681031 |
| P698 | PubMed publication ID | 28124833 |
| P50 | author | Gisbert Schneider | Q51615601 |
| David Winkler | Q52234967 | ||
| Kimito Funatsu | Q60230138 | ||
| P2093 | author name string | Yasushi Okuno | |
| P2860 | cites work | In silico toxicology: computational methods for the prediction of chemical toxicity | Q26749472 |
| Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening | Q26750642 | ||
| Uniting cheminformatics and chemical theory to predict the intrinsic aqueous solubility of crystalline druglike molecules | Q28660680 | ||
| Understanding the Roles of the "Two QSARs". | Q30383413 | ||
| Voyages to the (un)known: adaptive design of bioactive compounds | Q33384049 | ||
| Scoring functions for prediction of protein-ligand interactions | Q34422925 | ||
| Computer-based de novo design of drug-like molecules | Q34438895 | ||
| De Novo Design at the Edge of Chaos | Q35924943 | ||
| Descriptors and their selection methods in QSAR analysis: paradigm for drug design | Q36057604 | ||
| Going further than Lipinski's rule in drug design | Q37999092 | ||
| Free energy calculations to estimate ligand-binding affinities in structure-based drug design. | Q38129100 | ||
| Comprehension of drug toxicity: software and databases | Q38183508 | ||
| Applying machine learning techniques for ADME-Tox prediction: a review | Q38273839 | ||
| Predicting drug metabolism: experiment and/or computation? | Q38439385 | ||
| Quantitative structure-activity relationship: promising advances in drug discovery platforms | Q38584487 | ||
| Deep Learning in Drug Discovery. | Q38918718 | ||
| A Bright Future for Evolutionary Methods in Drug Design. | Q38997437 | ||
| Quantifying the chemical beauty of drugs. | Q42414958 | ||
| The Calculation of Molecular Structural Similarity: Principles and Practice | Q53787277 | ||
| P433 | issue | 1-2 | |
| P577 | publication date | 2017-01-01 | |
| P1433 | published in | Molecular Informatics | Q3319476 |
| P1476 | title | De novo Drug Design - Ye olde Scoring Problem Revisited | |
| P478 | volume | 36 |
| Q47780833 | Automating drug discovery |
| Q52590560 | Finding the molecular scaffold of nuclear receptor inhibitors through high-throughput screening based on proteochemometric modelling. |
| Q58880965 | Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators |
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