Accurate and efficient target prediction using a potency-sensitive influence-relevance voter

BACKGROUND: A number of algorithms have been proposed to predict the biological targets of diverse molecules. Some are structure-based, but the most common are ligand-based and use chemical fingerprints and the notion of chemical similarity. These methods tend to be computationally faster than others, making them particularly attractive tools as the amount of available data grows. RESULTS: Using a ChEMBL-derived database covering 490,760 molecule-protein interactions and 3236 protein targets, we conduct a large-scale assessment of the performance of several target-prediction algorithms at predicting drug-target activity. We assess algorithm performance using three validation procedures: standard tenfold cross-validation, tenfold cross-validation in a simulated screen that includes random inactive molecules, and validation on an external test set composed of molecules not present in our database. CONCLUSIONS: We present two improvements over current practice. First, using a modified version of the influence-relevance voter (IRV), we show that using molecule potency data can improve target prediction. Second, we demonstrate that random inactive molecules added during training can boost the accuracy of several algorithms in realistic target-prediction experiments. Our potency-sensitive version of the IRV (PS-IRV) obtains the best results on large test sets in most of the experiments. Models and software are publicly accessible through the chemoinformatics portal at http://chemdb.ics.uci.edu/ ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13321-015-0110-6) contains supplementary material, which is available to authorized users

Computational Tools for Chemical Reactions: Simulation & Prediction

Achieving human-level performance at predicting chemical reactions remains an open prob- lem with broad potential applications. Here we describe a deep learning-based tool for chemical reaction prediction and product identification. Significant efforts were made to curate and refine a new, high-quality data set of hand-selected chemical reactions written at the level of elementary electron movements. Using deep artificial neural networks trained on this data, we demonstrate a high degree of accuracy at predicting real-world reactions. Because predictions are made at the elementary step level, they can be chained together to form multi-step reaction pathway searches, to help identify unknown side products.We also present a computational brewing application, COBRA, capable of simulating com- plex chemical mixtures. We demonstrate its efficacy at modeling both the photooxidation of isoprene, and the oxidation of squalene in the presence of ozone, by comparing predicted results with results obtained from high-resolution mass spectrometry.In addition, we address the problem of atom-mapping for chemical reactions, by designing a new atom-mapping algorithm that can be used to annotate unmapped reactions

Topological Data Analysis Reveals a Subgroup of Luminal B Breast Cancer

Objective: High-throughput biological data, with its vast complexity and higher dimensions, continues to require innovative analytic methodologies for meaningful exploration. Most methods for reducing data dimensions overlook the shape and topology of data, even though these are vital components of the data structure and complexity. This study leverages topological data analysis (TDA) and shows, using breast cancer (BC) gene expression data as an illustrative example, the power of including the shape of data. Results: In addition to delineating the known subtypes of BC, TDA identifies a new subtype within luminal B cancer along with the features that define the subtype. The final outcome is shown via three-dimensional (3D) scatter plots which demonstrate how the underlying patterns that we identified through TDA map to 3D space. Conclusions: The new subtype, obtained unsupervised and validated by prior knowledge, demonstrates the power of embedding the topology and shape of data in the analyses

MOESM1 of Accurate and efficient target prediction using a potency-sensitive influence-relevance voter

Additional file 1: Protein targets list

MOESM2 of Accurate and efficient target prediction using a potency-sensitive influence-relevance voter

Additional file 2: Additional tables and figures

COBRA: A Computational Brewing Application for Predicting the Molecular Composition of Organic Aerosols

Atmospheric organic aerosols (OA) represent a significant fraction of airborne particulate matter and can impact climate, visibility, and human health. These mixtures are difficult to characterize experimentally due to their complex and dynamic chemical composition. We introduce a novel Computational Brewing Application (COBRA) and apply it to modeling oligomerization chemistry stemming from condensation and addition reactions in OA formed by photooxidation of isoprene. COBRA uses two lists as input: a list of chemical structures comprising the molecular starting pool and a list of rules defining potential reactions between molecules. Reactions are performed iteratively, with products of all previous iterations serving as reactants for the next. The simulation generated thousands of structures in the mass range of 120–500 Da and correctly predicted ∼70% of the individual OA constituents observed by high-resolution mass spectrometry. Select predicted structures were confirmed with tandem mass spectrometry. Esterification was shown to play the most significant role in oligomer formation, with hemiacetal formation less important, and aldol condensation insignificant. COBRA is not limited to atmospheric aerosol chemistry; it should be applicable to the prediction of reaction products in other complex mixtures for which reasonable reaction mechanisms and seed molecules can be supplied by experimental or theoretical methods

Atmospheric Oxidation of Squalene: Molecular Study Using COBRA Modeling and High-Resolution Mass Spectrometry

Squalene is a major component of skin and plant surface lipids and is known to be present at high concentrations in indoor dust. Its high reactivity toward ozone makes it an important ozone sink and a natural protectant against atmospheric oxidizing agents. While the volatile products of squalene ozonolysis are known, the condensed-phase products have not been characterized. We present an analysis of condensed-phase products resulting from an extensive oxidation of squalene by ozone probed by electrospray ionization (ESI) high-resolution mass spectrometry (HR–MS). A complex distribution of nearly 1300 peaks assignable to molecular formulas is observed in direct infusion positive ion mode ESI mass spectra. The distribution of peaks in the mass spectra suggests that there are extensive cross-coupling reactions between hydroxy-carbonyl products of squalene ozonolysis. To get additional insights into the mechanism, we apply a Computational Brewing Application (COBRA) to simulate the oxidation of squalene in the presence of ozone, and compare predicted results with those observed by the HR–MS experiments. The system predicts over one billion molecular structures between 0 and 1450 Da, which correspond to about 27 000 distinct elemental formulas. Over 83% of the squalene oxidation products inferred from the mass spectrometry data are matched by the simulation. The simulation indicates a prevalence of peroxy groups, with hydroxyl and ether groups being the second-most important O-containing functional groups formed during squalene oxidation. These highly oxidized products of squalene ozonolysis may accumulate on indoor dust and surfaces and contribute to their redox capacity

Targeted molecular therapy of head and neck squamous cell carcinoma with the tyrosine kinase inhibitor vandetanib in a mouse model

BACKGROUND: We investigated the effects of vandetanib, an inhibitor of vascular endothelial growth factor receptor 2 (VEGFR-2) and epidermal growth factor receptor (EGFR), alone and in combination with paclitaxel in an orthotopic mouse model of human head and neck squamous cell carcinoma (HNSCC). METHODS: The in vitro effects of vandetanib (ZACTIMA(™)) were assessed in two HNSCC cell lines on cell growth, apoptosis, and receptor and downstream signaling morecule expression and phosphorylation levels. We assessed in vivo effects of vandetanib and/or paclitaxel by measuring tumor cell apoptosis, endothelial cell apoptosis, microvessel density, tumor size, and animal survival. RESULTS: In vitro, vandetanib inhibited the phosphorylation of EGFR and its downstream targets in HNSCC cells and inhibited proliferation and induced apoptosis of HNSCC cells and extended survival and inhibited tumor growth in nude mice orthotopically injected with human HNSCC. CONCLUSION: Vandetanib has the potential to be a novel molecular targeted therapy for HNSCC