MOTIVATION: Cytochrome P450s are a family of enzymes responsible for the metabolism of approximately 90% of FDA-approved drugs. Medicinal chemists often want to know which atoms of a molecule-its metabolized sites-are oxidized by Cytochrome P450s in order to modify their metabolism. Consequently, there are several methods that use literature-derived, atom-resolution data to train models that can predict a molecule's sites of metabolism. There is, however, much more data available at a lower resolution, where the exact site of metabolism is not known, but the region of the molecule that is oxidized is known. Until now, no site-of-metabolism models made use of region-resolution data. RESULTS: Here, we describe XenoSite-Region, the first reported method for training site-of-metabolism models with region-resolution data. Our approach uses the Expectation Maximization algorithm to train a site-of-metabolism model. Region-resolution metabolism data was simulated from a large site-of-metabolism dataset, containing 2000 molecules with 3400 metabolized and 30 000 un-metabolized sites and covering nine Cytochrome P450 isozymes. When training on the same molecules (but with only region-level information), we find that this approach yields models almost as accurate as models trained with atom-resolution data. Moreover, we find that atom-resolution trained models are more accurate when also trained with region-resolution data from additional molecules. Our approach, therefore, opens up a way to extend the applicable domain of site-of-metabolism models into larger regions of chemical space. This meets a critical need in drug development by tapping into underutilized data commonly available in most large drug companies. AVAILABILITY AND IMPLEMENTATION: The algorithm, data and a web server are available at http://swami.wustl.edu/xregion.

SUMMARY: Scaffold network generator (SNG) is an open-source command-line utility that computes the hierarchical network of scaffolds that define a large set of input molecules. Scaffold networks are useful for visualizing, analysing and understanding the chemical data that is increasingly available through large public repositories like PubChem. For example, some groups have used scaffold networks to identify missed-actives in high-throughput screens of small molecules with bioassays. Substantially improving on existing software, SNG is robust enough to work on millions of molecules at a time with a simple command-line interface. AVAILABILITY AND IMPLEMENTATION: SNG is accessible at http://swami.wustl.edu/sng

SUMMARY: Regioselectivity-WebPredictor (RS-WebPredictor) is a server that predicts isozyme-specific cytochrome P450 (CYP)-mediated sites of metabolism (SOMs) on drug-like molecules. Predictions may be made for the promiscuous 2C9, 2D6 and 3A4 CYP isozymes, as well as CYPs 1A2, 2A6, 2B6, 2C8, 2C19 and 2E1. RS-WebPredictor is the first freely accessible server that predicts the regioselectivity of the last six isozymes. Server execution time is fast, taking on average 2s to encode a submitted molecule and 1s to apply a given model, allowing for high-throughput use in lead optimization projects. AVAILABILITY: RS-WebPredictor is accessible for free use at http://reccr.chem.rpi.edu/Software/RS-WebPredictor/

We present a bundle algorithm for multiple-instance classification and ranking. These frameworks yield improved models on many problems possessing special structure. Multiple-instance loss functions are typically nonsmooth and nonconvex, and current algorithms convert these to smooth nonconvex optimization problems that are solved iteratively. Inspired by the latest linear-time subgradient-based methods for support vector machines, we optimize the objective directly using a nonconvex bundle method. Computational results show this method is linearly scalable, while not sacrificing generalization accuracy, permitting modeling on new and larger data sets in computational chemistry and other applications. This new implementation facilitates modeling with kernels.

This paper introduces a novel machine learning model called multiple instance ranking (MIRank) that enables ranking to be performed in a multiple instance learning setting. The motivation for MIRank stems from the hydrogen abstraction problem in computational chemistry, that of predicting the group of hydrogen atoms from which a hydrogen is abstracted (removed) during metabolism. The model predicts the preferred hydrogen group within a molecule by ranking the groups, with the ambiguity of not knowing which hydrogen atom within the preferred group is actually abstracted. This paper formulates MIRank in its general context and proposes an algorithm for solving MIRank problems using successive linear programming. The method outperforms multiple instance classification models on several real and synthetic datasets.