DrugAI_Drug-Likeness

KO | EN

gitlite — search

#python #java #python3 #arduino #golang #machine-learning #rust #html #flask #javascript #seismology #nodejs

DrugAI_Drug-Likeness

★ 16 Open GitHub ↗

Drug-Likeness

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# DrugAI_Drug-Likeness

Druglikeness may be defined as a complex balance of various molecular properties and structure features which determine whether particular molecule is similar to the known drugs. These properties, mainly hydrophobicity, electronic distribution, hydrogen bonding characteristics, molecule size and flexibility and of course presence of various pharmacophoric features influence the behavior of molecule in a living organism, including bioavailability, transport properties, affinity to proteins, reactivity, toxicity, metabolic stability and many others.

## Requirements
```python
Python==3.7
RDKit==2020.09.1
```

## Drug-Likeness

### QED
**quantitative estimation of drug-likeness**

Bickerton, G., Paolini, G., Besnard, J. et al. Quantifying the chemical beauty of drugs. Nature Chem 4, 90–98 (2012). https://doi.org/10.1038/nchem.1243

### QEPPI
**quantitative estimate of protein-protein interaction targeting drug-likeness**

https://github.com/ohuelab/QEPPI

Kosugi T, Ohue M. Quantitative estimate index for early-stage screening of compounds targeting protein-protein interactions. International Journal of Molecular Sciences, 22(20): 10925, 2021. doi: 10.3390/ijms222010925
Another QEPPI publication (conference paper)

Kosugi T, Ohue M. Quantitative estimate of protein-protein interaction targeting drug-likeness. In Proceedings of The 18th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2021), 2021. doi: 10.1109/CIBCB49929.2021.9562931 (PDF) * © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

### Rule of 5 : Lipinski
Molecular mass less than 500 Dalton

High lipophilicity (expressed as LogP less than 5)

Less than 5 hydrogen bond donors

Less than 10 hydrogen bond acceptors

Molar refractivity should be between 40-130

### Rule of 4
**“rule-of-four” (RO4) to evaluate PPI inhibitors**

MW must be higher than 400;

ALogP must be higher than 4;

HBA must be higher than 4;

The number of rings (RING) must be higher than 4.

### Rule of 3
**"Rule of Three (Ro3)" compliant fragments (fragment-likeness)**
Jhoti, H., Williams, G., Rees, D. et al. The 'rule of three' for fragment-based drug discovery: where are we now?. Nat Rev Drug Discov 12, 644 (2013). https://doi.org/10.1038/nrd3926-c1

### Ghose Filter
**This filter defines drug-likeness constraints as follows:**

calculated log P is between -0.4 and 5.6,

molecular weight is between 160 and 480,

molar refractivity  is between 40 and 130,

total number of atoms is between 20 and 70.

### Veber Filter

The Veber filter is a rule of thumb filter for orally active drugs described in Veber et. al., J Med Chem. 2002; 45(12): 2615-23.

### REOS (Rapid Elimination Of Swill)
Walters, W., Namchuk, M. Designing screens: how to make your hits a hit. Nat Rev Drug Discov 2, 259–266 (2003). https://doi.org/10.1038/nrd1063

DrugAI_Drug-Likeness

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README