New benchmark for chemical nomenclature software.

We propose a new, robust benchmark, called Percentage Round Tripping of Canonical Isomeric SMILES (%RTCS), for assessing the ability of chemical nomenclature software to convert chemical structures to names and chemical names to structures. The benchmark is based on a string comparison between canonical isomeric SMILES generated from the original structure and the resultant structure from round tripping. Using the latest version of the OpenEye chemical nomenclature toolkit, Lexichem v2.1.0, we report %RTCS values of over 92% on average for a variety of challenging compound collections.

A novel hybrid ultrafast shape descriptor method for use in virtual screening.

BACKGROUND: We have introduced a new Hybrid descriptor composed of the MACCS key descriptor encoding topological information and Ballester and Richards' Ultrafast Shape Recognition (USR) descriptor. The latter one is calculated from the moments of the distribution of the interatomic distances, and in this work we also included higher moments than in the original implementation. RESULTS: The performance of this Hybrid descriptor is assessed using Random Forest and a dataset of 116,476 molecules. Our dataset includes 5,245 molecules in ten classes from the 2005 World Anti-Doping Agency (WADA) dataset and 111,231 molecules from the National Cancer Institute (NCI) database. In a 10-fold Monte Carlo cross-validation this dataset was partitioned into three distinct parts for training, optimisation of an internal threshold that we introduced, and validation of the resulting model. The standard errors obtained were used to assess statistical significance of observed improvements in performance of our new descriptor. CONCLUSION: The Hybrid descriptor was compared to the MACCS key descriptor, USR with the first three (USR), four (UF4) and five (UF5) moments, and a combination of MACCS with USR (three moments). The MACCS key descriptor was not combined with UF5, due to similar performance of UF5 and UF4. Superior performance in terms of all figures of merit was found for the MACCS/UF4 Hybrid descriptor with respect to all other descriptors examined. These figures of merit include recall in the top 1% and top 5% of the ranked validation sets, precision, F-measure, area under the Receiver Operating Characteristic curve and Matthews Correlation Coefficient.

Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds.

We investigate the classification performance of circular fingerprints in combination with the Naive Bayes Classifier (MP2D), Inductive Logic Programming (ILP) and Support Vector Inductive Logic Programming (SVILP) on a standard molecular benchmark dataset comprising 11 activity classes and about 102,000 structures. The Naive Bayes Classifier treats features independently while ILP combines structural fragments, and then creates new features with higher predictive power. SVILP is a very recently presented method which adds a support vector machine after common ILP procedures. The performance of the methods is evaluated via a number of statistical measures, namely recall, specificity, precision, F-measure, Matthews Correlation Coefficient, area under the Receiver Operating Characteristic (ROC) curve and enrichment factor (EF). According to the F-measure, which takes both recall and precision into account, SVILP is for seven out of the 11 classes the superior method. The results show that the Bayes Classifier gives the best recall performance for eight of the 11 targets, but has a much lower precision, specificity and F-measure. The SVILP model on the other hand has the highest recall for only three of the 11 classes, but generally far superior specificity and precision. To evaluate the statistical significance of the SVILP superiority, we employ McNemar's test which shows that SVILP performs significantly (p < 5%) better than both other methods for six out of 11 activity classes, while being superior with less significance for three of the remaining classes. While previously the Bayes Classifier was shown to perform very well in molecular classification studies, these results suggest that SVILP is able to extract additional knowledge from the data, thus improving classification results further.

Chemoinformatics-based classification of prohibited substances employed for doping in sport.

Representative molecules from 10 classes of prohibited substances were taken from the World Anti-Doping Agency (WADA) list, augmented by molecules from corresponding activity classes found in the MDDR database. Together with some explicitly allowed compounds, these formed a set of 5245 molecules. Five types of fingerprints were calculated for these substances. The random forest classification method was used to predict membership of each prohibited class on the basis of each type of fingerprint, using 5-fold cross-validation. We also used a k-nearest neighbors (kNN) approach, which worked well for the smallest values of k. The most successful classifiers are based on Unity 2D fingerprints and give very similar Matthews correlation coefficients of 0.836 (kNN) and 0.829 (random forest). The kNN classifiers tend to give a higher recall of positives at the expense of lower precision. A naïve Bayesian classifier, however, lies much further toward the extreme of high recall and low precision. Our results suggest that it will be possible to produce a reliable and quantitative assignment of membership or otherwise of each class of prohibited substances. This should aid the fight against the use of bioactive novel compounds as doping agents, while also protecting athletes against unjust disqualification.

Chapter 9 Molecular Similarity: Advances in Methods, Applications and Validations in Virtual Screening and QSAR.

This chapter discusses recent developments in some of the areas that exploit the molecular similarity principle, novel approaches to capture molecular properties by the use of novel descriptors, focuses on a crucial aspect of computational models-their validity, and discusses additional ways to examine data available, such as those from high-throughput screening (HTS) campaigns and to gain more knowledge from this data. The chapter also presents some of the recent applications of methods discussed focusing on the successes of virtual screening applications, database clustering and comparisons (such as drug- and in-house-likeness), and the recent large-scale validations of docking and scoring programs. While a great number of descriptors and modeling methods has been proposed until today, the recent trend toward proper model validation is very much appreciated. Although some of their limitations are surely because of underlying principles and limitations of fundamental concepts, others will certainly be eliminated in the future.