ADME evaluation in drug discovery

By Tingjun Hou, Xiaojie Xu. Journal of Molecular Modeling, 2002

652 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Organic Chemistry, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/69e83c49e04f021333063752aa554e5bf8a71482

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. John C. Dearden
2. Eugene N. Muratov
3. Denis Fourches
4. Alexander Tropsha
5. Rodolpho C. Braga
6. Carolina Horta Andrade
7. Jens Meiler
8. Sandeepkumar Kothiwale

In Organic Chemistry

1. John C. Dearden
2. Jens Meiler
3. Sandeepkumar Kothiwale
4. E. W. Lowe
5. Gregory Sliwoski
6. Alexander Tropsha
7. John A. Arnott
8. S. Planey

In Spectroscopy

1. John C. Dearden
2. Sandeepkumar Kothiwale
3. E. W. Lowe
4. Jens Meiler
5. Gregory Sliwoski
6. Alexander Tropsha
7. John A. Arnott
8. S. Planey

Citations in the corpus

• Tuning hERG out: Antitarget QSAR Models for Drug Development (2014, background): “Threshold values for the blocker/non-blocker classification vary in the literature from 1 μM to 40 μM [56, 66, 69, 111].”
• The influence of lipophilicity in drug discovery and design (2012, background): “In humans, 13 rotatable bonds have been identified as an upper limit to predict ‡ 20% oral bioavailability based on an analysis of 1,014 marketed drugs [6].”
• In silico prediction of aqueous solubility (2006, methodology): “It must also be pointed out that, despite the gloomy prediction of Yalkowsky and Mishra [88] that group contribution methods for aqueous solubility prediction will not work, two methods [94,99] based on atom contributions appear to work very well, as do several methods [4,92,93,100] listed in Table ”
• Computational Methods in Drug Discovery (2014, methodology): “Therefore, SB-CADD techniques have mainly relied on docking with homology models, and this method has not been validated with large, highly diverse datasets (Wang et al., 2012).”
• Intranasal drug delivery: how, why and what for? (2009, background): “Therefore, it is not surprising that the prediction of human oral bioavailability of new drug candidates is currently targeted from the earliest stages of drug discovery and development programmes (1, 2).”
• Computational Approaches in Preclinical Studies on Drug Discovery and Development (2020, methodology): “Originally, some such software programs were specifically modeled for predicting a specific property of the ADME process, such as absorption (GastroPlus, Tian et al., 2011) or metabolism (SimCYP, Jamei et al., 2009).”
• Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models (2020, background): “However, it is well known that the performance of descriptor-based models is highly depending on the descriptors used in training and many previous studies have highlighted that ML models only based on molecular fingerprints are not such well-performing[5, 6, 39, 40].”