CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates.

The interaction of small organic molecules such as drugs, agrochemicals, and cosmetics with cytochrome P450 enzymes (CYPs) can lead to substantial changes in the bioavailability of active substances and hence consequences with respect to pharmacological efficacy and toxicity. Therefore, efficient means of predicting the interactions of small organic molecules with CYPs are of high importance to a host of different industries. In this work, we present a new set of machine learning models for the classification of xenobiotics into substrates and non-substrates of nine human CYP isozymes: CYPs 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4.

Skin Doctor CP: Conformal Prediction of the Skin Sensitization Potential of Small Organic Molecules.

Skin sensitization potential or potency is an important end point in the safety assessment of new chemicals and new chemical mixtures. Formerly, animal experiments such as the local lymph node assay (LLNA) were the main form of assessment. Today, however, the focus lies on the development of nonanimal testing approaches (i.

GLORYx: Prediction of the Metabolites Resulting from Phase 1 and Phase 2 Biotransformations of Xenobiotics.

Predicting the structures of metabolites formed in humans can provide advantageous insights for the development of drugs and other compounds. Here we present GLORYx, which integrates machine learning-based site of metabolism (SoM) prediction with reaction rule sets to predict and rank the structures of metabolites that could potentially be formed by phase 1 and/or phase 2 metabolism. GLORYx extends the approach from our previously developed tool GLORY, which predicted metabolite structures for cytochrome P450-mediated metabolism only.

NERDD: a web portal providing access to in silico tools for drug discovery.

Summary: The New E-Resource for Drug Discovery (NERDD) is a quickly expanding web portal focused on the provision of peer-reviewed in silico tools for drug discovery. NERDD currently hosts tools for predicting the sites of metabolism (FAME) and metabolites (GLORY) of small organic molecules, for flagging compounds that are likely to interfere with biological assays (Hit Dexter), and for identifying natural products and natural product derivatives in large compound collections (NP-Scout). Several additional models and components are currently in development.

ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance.

Protein flexibility and solvation pose major challenges to docking algorithms and scoring functions. One established strategy for addressing these challenges is to use multiple protein conformations for docking (all-against-all ensemble docking). Recent studies have shown that the performance of ensemble docking can be improved by selecting the most relevant protein structures for docking.

Resources for Chemical, Biological, and Structural Data on Natural Products.

Natural products from plants, marine life, animals, fungi, bacteria, and other organisms remain the most productive source of inspiration for small-molecule drug discovery. Today, a wealth of information on natural products that is particularly valuable to applications in cheminformatics is at our disposal. In this contribution, we provide a timely overview of relevant resources for measured chemical, biological, and structural data on natural products.

Analysis of the FLVR motif of SHIP1 and its importance for the protein stability of SH2 containing signaling proteins.

Binding of proteins with SH2 domains to tyrosine-phosphorylated signaling proteins is a key mechanism for transmission of biological signals within the cell. Characterization of dysregulated proteins in cell signaling pathways is important for the development of therapeutic approaches. The AKT pathway is a frequently upregulated pathway in most cancer cells and the SH2-containing inositol 5-phosphatase SHIP1 is a negative regulator of the AKT pathway.

FAME 3: Predicting the Sites of Metabolism in Synthetic Compounds and Natural Products for Phase 1 and Phase 2 Metabolic Enzymes.

In this work we present the third generation of FAst MEtabolizer (FAME 3), a collection of extra trees classifiers for the prediction of sites of metabolism (SoMs) in small molecules such as drugs, druglike compounds, natural products, agrochemicals, and cosmetics. FAME 3 was derived from the MetaQSAR database ( Pedretti et al. 2018 , 61 , 1019 ), a recently published data resource on xenobiotic metabolism that contains more than 2100 substrates annotated with more than 6300 experimentally confirmed SoMs related to redox reactions, hydrolysis and other nonredox reactions, and conjugation reactions.

GLORY: Generator of the Structures of Likely Cytochrome P450 Metabolites Based on Predicted Sites of Metabolism.

Computational prediction of xenobiotic metabolism can provide valuable information to guide the development of drugs, cosmetics, agrochemicals, and other chemical entities. We have previously developed FAME 2, an effective tool for predicting sites of metabolism (SoMs). In this work, we focus on the prediction of the chemical structures of metabolites, in particular metabolites of xenobiotics.

Hit Dexter: A Machine-Learning Model for the Prediction of Frequent Hitters.

False-positive assay readouts caused by badly behaving compounds-frequent hitters, pan-assay interference compounds (PAINS), aggregators, and others-continue to pose a major challenge to experimental screening. There are only a few in silico methods that allow the prediction of such problematic compounds. We report the development of Hit Dexter, two extremely randomized trees classifiers for the prediction of compounds likely to trigger positive assay readouts either by true promiscuity or by assay interference.

Benchmarking Commercial Conformer Ensemble Generators.

Unlabelled: We assess and compare the performance of eight commercial conformer ensemble generators (ConfGen, ConfGenX, cxcalc, iCon, MOE LowModeMD, MOE Stochastic, MOE Conformation Import, and OMEGA) and one leading free algorithm, the distance geometry algorithm implemented in RDKit. The comparative study is based on a new version of the Platinum Diverse Dataset, a high-quality benchmarking dataset of 2859 protein-bound ligand conformations extracted from the PDB. Differences in the performance of commercial algorithms are much smaller than those observed for free algorithms in our previous study (J.

Data Resources for the Computer-Guided Discovery of Bioactive Natural Products.

Natural products from plants, animals, marine life, fungi, bacteria, and other organisms are an important resource for modern drug discovery. Their biological relevance and structural diversity make natural products good starting points for drug design. Natural product-based drug discovery can benefit greatly from computational approaches, which are a valuable precursor or supplementary method to in vitro testing.

FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity.

Unlabelled: We report on the further development of FAst MEtabolizer (FAME; J. Chem. Inf.

Alignment-Based Prediction of Sites of Metabolism.

Prediction of metabolically labile atom positions in a molecule (sites of metabolism) is a key component of the simulation of xenobiotic metabolism as a whole, providing crucial information for the development of safe and effective drugs. In 2008, an exploratory study was published in which sites of metabolism were derived based on molecular shape- and chemical feature-based alignment to a molecule whose site of metabolism (SoM) had been determined by experiments. We present a detailed analysis of the breadth of applicability of alignment-based SoM prediction, including transfer of the approach from a structure- to ligand-based method and extension of the applicability of the models from cytochrome P450 2C9 to all cytochrome P450 isozymes involved in drug metabolism.

High-Quality Dataset of Protein-Bound Ligand Conformations and Its Application to Benchmarking Conformer Ensemble Generators.

We developed a cheminformatics pipeline for the fully automated selection and extraction of high-quality protein-bound ligand conformations from X-ray structural data. The pipeline evaluates the validity and accuracy of the 3D structures of small molecules according to multiple criteria, including their fit to the electron density and their physicochemical and structural properties. Using this approach, we compiled two high-quality datasets from the Protein Data Bank (PDB): a comprehensive dataset and a diversified subset of 4626 and 2912 structures, respectively.