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

This paper emphasizes the importance of rigorous validation as a crucial, integral component of Quantitative Structure Property Relationship (QSPR) model development. We consider some examples of published QSPR models, which in spite of their high fitted accuracy for the training sets and apparent mechanistic appeal, fail rigorous validation tests, and, thus, may lack practical utility as reliable screening tools. We present a set of simple guidelines for developing validated and predictive QSPR models. To this end, we discuss several validation strategies including (1) randomization of the modelled property, also called Y-scrambling, (2) multiple leave-many-out cross-validations, and (3) external validation using rational division of a dataset into training and test sets. We also highlight the need to establish the domain of model applicability in the chemical space to flag molecules for which predictions may be unreliable, and discuss some algorithms that can be used for this purpose. We advocate the broad use of these guidelines in the development of predictive QSPR models.

The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models

From the historically grown archive of protein−ligand complexes in the Protein Data Bank small organic ligands are extracted and interpreted in terms of their chemical characteristics and features. Subsequently, pharmacophores representing ligand−receptor interaction are derived from each of these small molecules and its surrounding amino acids. Based on a defined set of only six types of chemical features and volume constraints, three-dimensional pharmacophore models are constructed, which are sufficiently selective to identify the described binding mode and are thus a useful tool for in-silico screening of large compound databases. The algorithms for ligand extraction and interpretation as well as the pharmacophore creation technique from the automatically interpreted data are presented and applied to a rhinovirus capsid complex as application example.

LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters.

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

/pdf/computational-methods-in-drug-discovery-3sww6kbvd5.pdf

Computational Methods in Drug Discovery

The docking field has come of age. The time is ripe to present the principles of docking, reviewing the current state of the field. Two reasons are largely responsible for the maturity of the computational docking area. First, the early optimism that the very presence of the "correct" native conformation within the list of predicted docked conformations signals a near solution to the docking problem, has been replaced by the stark realization of the extreme difficulty of the next scoring/ranking step. Second, in the last couple of years more realistic approaches to handling molecular flexibility in docking schemes have emerged. As in folding, these derive from concepts abstracted from statistical mechanics, namely, populations. Docking and folding are interrelated. From the purely physical standpoint, binding and folding are analogous processes, with similar underlying principles. Computationally, the tools developed for docking will be tremendously useful for folding. For large, multidomain proteins, domain docking is probably the only rational way, mimicking the hierarchical nature of protein folding. The complexity of the problem is huge. Here we divide the computational docking problem into its two separate components. As in folding, solving the docking problem involves efficient search (and matching) algorithms, which cover the relevant conformational space, and selective scoring functions, which are both efficient and effectively discriminate between native and non-native solutions. It is universally recognized that docking of drugs is immensely important. However, protein-protein docking is equally so, relating to recognition, cellular pathways, and macromolecular assemblies. Proteins function when they are bound to other molecules. Consequently, we present the review from both the computational and the biological points of view. Although large, it covers only partially the extensive body of literature, relating to small (drug) and to large protein-protein molecule docking, to rigid and to flexible. Unfortunately, when reviewing these, a major difficulty in assessing the results is the non-uniformity in the formats in which they are presented in the literature. Consequently, we further propose a way to rectify it here.

Principles of docking: An overview of search algorithms and a guide to scoring functions

The water solubilities of several polynuclear aromatic and heteroaromatic hydrocarbons have been compiled and reviewed for consistency through correlations with parameters such as surface area, molecular volume, and boiling point. The carbocycles and oxygen and sulfur heterocycles were governed by the same correlative equations, thereby indicating that these heteroatoms entered into only a limited degree of hydrogen bonding. Equations representing the nitrogen heterocycles differed from their carbocyclic counterparts by an approximately constant amount, suggesting that while the solubilizing effect of the nitrogen heteroatom may be large, it tends to remain constant within a similar series of compounds.

Water Solubilities of Polynuclear Aromatic and Heteroaromatic Compounds

Following brief comments regarding the advantages of cell-based diversity algorithms and the selection of low-dimensional chemistry-space metrics needed to implement such algorithms, the notion of metric validation is discussed. Activity-seeded, structure-based clustering is presented as an ideal approach for the validation of either high- or low-dimensional chemistry-space metrics when validation by computer-graphic visualization is not possible. Whereas typical methods for reducing the dimensionality of chemistry-space inevitably discard potentially important information, we present a simple yet novel algorithm for reducing dimensionality by identifying which axes (metrics) convey information related to affinity for a given receptor and which axes can be safely discarded as being irrelevant to the given receptor. This algorithm often reveals a three- or two-dimensional subspace of a (typically six-dimensional) BCUT chemistry-space and, thus, enables computer graphic visualization of the actual coordinat...

Metric Validation and the Receptor-Relevant Subspace Concept

Agonist affinity changes dramatically as a result of serine to alanine mutations (S193A, S194A, and S197A) within the fifth transmembrane region of D2 dopamine receptors and other receptors for monoamine neurotransmitters. However, agonist 2D-structure does not predict which drugs will be sensitive to which point mutations. Modeling drug-receptor interactions at the 3D level offers considerably more promise in this regard. In particular, a comparison of the same test set of agonists across receptors differing minimally (point mutations) offers promise to enhance the understanding of the structural bases for drug-receptor interactions. We have previously shown that comparative molecular field analysis (CoMFA) can be applied to comparisons of affinity at recombinant D1 and D2 dopamine receptors for the same set of agonists, a differential QSAR. Here, we predicted agonist K(L) for the same set of agonists at wild type D2 vs S193A, S194A, and S197A receptors using CoMFA. Each model used bromocriptine as the template. ln(1/K(L)) values for the low-affinity agonist binding conformation at recombinant wild type and mutant D2 dopamine receptors stably expressed in C6 glioma cells were used as the target property for the CoMFA of the 16 aligned agonist structures. The resulting CoMFA models yielded cross-validated R(2) (q(2)) values ranging from 0.835 to 0.864 and simple R(2) values ranging from 0.999 to 1.000. Predictions of test compound affinities at WT and each mutant receptor were close to measured affinity values. This finding confirmed the predictive ability of the models and their differences from one another. The results strongly support the idea that CoMFA models of the same training set of compounds applied to WT vs mutant receptors can accurately predict differences in drug affinity at each. Furthermore, in a "proof of principle", two different templates were used to derive the CoMFA model for the WT and S193A mutant receptors. Pergolide was chosen as an alternate template because it showed a significant increase in affinity as a result of the S193A mutation. In this instance both the bromocriptine- and pergolide-based CoMFA models were similar to one another but different from those for the WT receptor using bromocriptine- or pergolide- as templates. The pergolide-based S193A model was more strikingly different from that of the WT receptor than was the bromocriptine-based S193A model. This suggests that a "dual-template" approach to differential CoMFA may have special value in elucidating key differences across related receptor types and in determining important elements of the drug-receptor interaction.

Robert S. Pearlman

Papers

Water Solubilities of Polynuclear Aromatic and Heteroaromatic Compounds

Metric Validation and the Receptor-Relevant Subspace Concept

CoMFA-based prediction of agonist affinities at recombinant wild type versus serine to alanine point mutated D2 dopamine receptors.

Efficient exact solution of the ring perception problem

Use of flexible queries for searching conformationally flexible molecules in databases of three-dimensional structures