Introduction
PaDEL-Descriptor is a software for calculating molecular descriptors and fingerprints. The software currently calculates 797 descriptors (663 1D, 2D descriptors, and 134 3D descriptors) and 10 types of fingerprints. These descriptors and fingerprints are calculated mainly using The Chemistry Development Kit. Some additional descriptors and fingerprints were added, which include atom type electrotopological state descriptors, McGowan volume, molecular linear free energy relation descriptors, ring counts, count of chemical substructures identified by Laggner, and binary fingerprints and count of chemical substructures identified by Klekota and Roth.

Methods
PaDEL-Descriptor was developed using the Java language and consists of a library component and an interface component. The library component allows it to be easily integrated into quantitative structure activity relationship software to provide the descriptor calculation feature while the interface component allows it to be used as a standalone software. The software uses a Master/Worker pattern to take advantage of the multiple CPU cores that are present in most modern computers to speed up calculations of molecular descriptors.

Results
The software has several advantages over existing standalone molecular descriptor calculation software. It is free and open source, has both graphical user interface and command line interfaces, can work on all major platforms (Windows, Linux, MacOS), supports more than 90 different molecular file formats, and is multithreaded.

Conclusion
PaDEL-Descriptor is a useful addition to the currently available molecular descriptor calculation software. The software can be downloaded at http://padel.nus.edu.sg/software/padeldescriptor. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2011

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcc.21707

PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints

High throughput artificial membrane permeability assay for blood-brain barrier

A set of simple, consistent structure-property guides have been determined from an analysis of a number of key ADMET assays run within GSK: solubility, permeability, bioavailability, volume of distribution, plasma protein binding, CNS penetration, brain tissue binding, P-gp efflux, hERG inhibition, and cytochrome P450 1A2/2C9/2C19/2D6/3A4 inhibition. The rules have been formulated using molecular properties that chemists intuitively know how to alter in a molecule, namely, molecular weight, logP, and ionization state. The rules supplement the more predictive black-box models available to us by clearly illustrating the key underlying trends, which are in line with reports in the literature. It is clear from the analyses reported herein that almost all ADMET parameters deteriorate with either increasing molecular weight, logP, or both, with ionization state playing either a beneficial or detrimental affect depending on the parameter in question. This study re-emphasizes the need to focus on a lower molecular weight and logP area of physicochemical property space to obtain improved ADMET parameters.

Generation of a set of simple, interpretable ADMET rules of thumb.

Prediction of drug solubility from structure.

Purpose To classify the dissolution and diffusion rate-limited drugs and establish quantitative relationships between absorption and molecular descriptors

Rate-limited steps of human oral absorption and QSAR studies.

Additive models for the estimation of Abraham's molecular descriptors R2, π2H, Σα2H, Σβ2H, Σβ2O, and log L16 have been developed. For five of the six descriptors, one set of 81 atom and functional group fragments is capable of reproducing experimentally derived results with correlation coefficients ranging from 0.95 to 0.99. However, one descriptor, Σα2H, required an entirely separate set of 51 fragments to be developed, resulting in a correlation coefficient of 0.97. Of particular importance is the speed of calculation (approximately 700 molecules/min), allowing so-called “high-throughput screening”. Several applications of this model for molecules containing intramolecular interactions are discussed.

Estimation of Molecular Linear Free Energy Relation Descriptors Using a Group Contribution Approach

Correlation and prediction of a large blood–brain distribution data set—an LFER study

A previously published method for the prediction of molecular linear free energy relationship descriptors is tested against experimentally determined partition coefficients in various solvent systems. Sets of partition data between water and octanol, cyclohexane, and chloroform were taken from the literature. For each set of partition data used, r2 values ranged from 0.8 to 0.9 and RMS errors from 0.7 to 1.0 log unit, comparable to errors obtained with previously published models for octanol-water partition. Modified solvation equations for water-octanol and water-cyclohexane partition are presented, and their implications discussed. The possibility of applying the current approach to a wide range of solvation and transport properties is put forward.

Estimation of molecular linear free energy relationship descriptors by a group contribution approach. 2. Prediction of partition coefficients

This paper describes the development of an aqueous solubility model based on solubility data from the Syracuse database, calculated octanol-water partition coefficient, and 51 2D molecular descriptors. Two different statistical packages, SIMCA and Cubist, were used and the results were compared. The Cubist model, which comprises a collection of rules, each of which has an associated Multiple Linear Regression model (MLR), gave better overall results on a test set of 640 compounds with an overall squared correlation coefficient of 0.74 and an absolute average error of 0.68 log units. Both training and independent test sets had similar distributions of structures in terms of the different functionalities present-60% neutral, 14% acidic, 8% phenolic, 11% monobasic, 4% polybasic, and 3% zwitterionic molecules. Sets were designed by random selection, with 2688 (81%) and 640 (19%) molecules, respectively, forming the training and the test sets.

Modeling aqueous solubility.

Purpose. The passage of molecules across cell membranes is acrucial step in many physiological processes. We therefore seek physicalmodels of this process, in order to predict permeation for new molecules,and to better understand the important interactions which determinethe rate of permeation.

Darko Butina

Papers

Estimation of Molecular Linear Free Energy Relation Descriptors Using a Group Contribution Approach

Correlation and prediction of a large blood–brain distribution data set—an LFER study

Estimation of molecular linear free energy relationship descriptors by a group contribution approach. 2. Prediction of partition coefficients

Modeling aqueous solubility.

Estimation of Molecular Linear Free Energy Relationship Descriptors. 4. Correlation and Prediction of Cell Permeation