A Cryptosporidium PI(4)K inhibitor is a drug candidate for cryptosporidiosis.

Diarrhoeal disease is responsible for 8.6% of global child mortality. Recent epidemiological studies found the protozoan parasite Cryptosporidium to be a leading cause of paediatric diarrhoea, with particularly grave impact on infants and immunocompromised individuals. There is neither a vaccine nor an effective treatment. Here we establish a drug discovery process built on scalable phenotypic assays and mouse models that take advantage of transgenic parasites. Screening a library of compounds with anti-parasitic activity, we identify pyrazolopyridines as inhibitors of Cryptosporidium parvum and Cryptosporidium hominis. Oral treatment with the pyrazolopyridine KDU731 results in a potent reduction in intestinal infection of immunocompromised mice. Treatment also leads to rapid resolution of diarrhoea and dehydration in neonatal calves, a clinical model of cryptosporidiosis that closely resembles human infection. Our results suggest that the Cryptosporidium lipid kinase PI(4)K (phosphatidylinositol-4-OH kinase) is a target for pyrazolopyridines and that KDU731 warrants further preclinical evaluation as a drug candidate for the treatment of cryptosporidiosis.

Prediction of pKa Using Machine Learning Methods with Rooted Topological Torsion Fingerprints: Application to Aliphatic Amines.

The acid-base dissociation constant, pKa, is a key parameter to define the ionization state of a compound and directly affects its biopharmaceutical profile. In this study, we developed a novel approach for pKa prediction using rooted topological torsion fingerprints in combination with five machine learning (ML) methods: random forest, partial least squares, extreme gradient boosting, lasso regression, and support vector regression. With a large and diverse set of 14 499 experimental pKa values, pKa models were developed for aliphatic amines. The models demonstrated consistently good prediction statistics and were able to generate accurate prospective predictions as validated with an external test set of 726 pKa values (RMSE 0.45, MAE 0.33, and R2 0.84 by the top model). The factors that may affect prediction accuracy and model applicability were carefully assessed. The results demonstrated that rooted topological torsion fingerprints coupled with ML methods provide a promising approach for developing accurate pKa prediction models.

Developing Collaborative QSAR Models Without Sharing Structures.

It is widely understood that QSAR models greatly improve if more data are used. However, irrespective of model quality, once chemical structures diverge too far from the initial data set, the predictive performance of a model degrades quickly. To increase the applicability domain we need to increase the diversity of the training set. This can be achieved by combining data from diverse sources. Public data can be easily included; however, proprietary data may be more difficult to add due to intellectual property concerns. In this contribution, we will present a method for the collaborative development of linear regression models that addresses this problem. The method differs from other past approaches, because data are only shared in an aggregated form. This prohibits access to individual data points and therefore avoids the disclosure of confidential structural information. The final models are equivalent to models that were built with combined data sets.

Shared Consensus Machine Learning Models for Predicting Blood Stage Malaria Inhibition.

The development of new antimalarial therapies is essential, and lowering the barrier of entry for the screening and discovery of new lead compound classes can spur drug development at organizations that may not have large compound screening libraries or resources to conduct high-throughput screens. Machine learning models have been long established to be more robust and have a larger domain of applicability with larger training sets. Screens over multiple data sets to find compounds with potential malaria blood stage inhibitory activity have been used to generate multiple Bayesian models. Here we describe a method by which Bayesian quantitative structure-activity relationship models, which contain information on thousands to millions of proprietary compounds, can be shared between collaborators at both for-profit and not-for-profit institutions. This model-sharing paradigm allows for the development of consensus models that have increased predictive power over any single model and yet does not reveal the identity of any compounds in the training sets.

Benefit of Retraining pKa Models Studied Using Internally Measured Data.

The ionization state of drugs influences many pharmaceutical properties such as their solubility, permeability, and biological activity. It is therefore important to understand the structure property relationship for the acid-base dissociation constant pKa during the lead optimization process to make better-informed design decisions. Computational approaches, such as implemented in MoKa, can help with this; however, they often predict with too large error especially for proprietary compounds. In this contribution, we look at how retraining helps to greatly improve prediction error. Using a longitudinal study with data measured over 15 years in a drug discovery environment, we assess the impact of model training on prediction accuracy and look at model degradation over time. Using the MoKa software, we will demonstrate that regular retraining is required to address changes in chemical space leading to model degradation over six to nine months.

FOCUS--Development of a Global Communication and Modeling Platform for Applied and Computational Medicinal Chemists.

Communication of data and ideas within a medicinal chemistry project on a global as well as local level is a crucial aspect in the drug design cycle. Over a time frame of eight years, we built and optimized FOCUS, a platform to produce, visualize, and share information on various aspects of a drug discovery project such as cheminformatics, data analysis, structural information, and design. FOCUS is tightly integrated with internal services that involve-among others-data retrieval systems and in-silico models and provides easy access to automated modeling procedures such as pharmacophore searches, R-group analysis, and similarity searches. In addition, an interactive 3D editor was developed to assist users in the generation and docking of close analogues of a known lead. In this paper, we will specifically concentrate on issues we faced during development, deployment, and maintenance of the software and how we continually adapted the software in order to improve usability. We will provide usage examples to highlight the functionality as well as limitations of FOCUS at the various stages of the development process. We aim to make the discussion as independent of the software platform as possible, so that our experiences can be of more general value to the drug discovery community.

Deriving static atomic multipoles from the electrostatic potential.

The description of molecular systems using multipolar electrostatics calls for automated methods to fit the necessary parameters. In this paper, we describe an open-source software package that allows fitting atomic multipoles (MTPs) from the ab initio electrostatic potential by adequate atom typing and judicious assignment of the local axis system. By enabling the simultaneous fit of several molecules and/or conformations, the package addresses issues of parameter transferability and lack of sampling for buried atoms. We illustrate the method by studying a series of small alcohol molecules, as well as various conformations of protonated butylamine.

Atomic multipoles: electrostatic potential fit, local reference axis systems, and conformational dependence.

Currently, all standard force fields for biomolecular simulations use point charges to model intermolecular electrostatic interactions. This is a fast and simple approach but has deficiencies when the electrostatic potential (ESP) is compared to that from ab initio methods. Here, we show how atomic multipoles can be rigorously implemented into common biomolecular force fields. For this, a comprehensive set of local reference axis systems is introduced, which represents a universal solution for treating atom-centered multipoles for all small organic molecules and proteins. Furthermore, we introduce a new method for fitting atomic multipole moments to the quantum mechanically derived ESP. This methods yields a 50-90% error reduction compared to both point charges fit to the ESP and multipoles directly calculated from the ab initio electron density. It is shown that it is necessary to directly fit the multipole moments of conformational ensembles to the ESP. Ignoring the conformational dependence or averaging over parameters from different conformations dramatically deteriorates the results obtained with atomic multipole moments, rendering multipoles worse than partial charges.

Development of isoform selective PI3-kinase inhibitors as pharmacological tools for elucidating the PI3K pathway.

Using a parallel synthesis approach to target a non-conserved region of the PI3K catalytic domain a pan-PI3K inhibitor 1 was elaborated to provide alpha, delta and gamma isoform selective Class I PI3K inhibitors 21, 24, 26 and 27. The compounds had good cellular activity and were selective against protein kinases and other members of the PI3K superfamily including mTOR and DNA-PK.

Design and synthesis of a library of chemokine antagonists.

A library of chemokine antagonists has been synthesized using a combination of solid and solution-phase chemistry. Structures of known chemokine antagonists were used to produce a pharmacophore which served to guide monomer selection. Several combinations of monomers have resulted in providing novel chemokine antagonists which in some cases display dual chemokine receptor antagonism.

Three descriptor model sets a high standard for the CSAR-NRC HiQ benchmark.

Here we report the results we obtained with a proteochemometric approach for predicting ligand binding free energies of the CSAR-NRC HiQ benchmark data set. Using distance-dependent atom-type pair descriptors in a bagged stepwise multiple-linear regression (MLR) model with subsequent complexity reduction we were able to identify three descriptors that can be used to build a very robust regression model for the CSAR-NRC HiQ data set. The model has an R(2)(cv) of 0.55, a MUE(cv) of 1.19, and an RMSE(cv) of 1.49 on the out-of-bag test set. The descriptors selected are the count of protein atoms in a shell between 4.5 Å and 6 Å around each heavy ligand atom excluding oxygen and phosphorus, the count of sulfur atoms in the vicinity of tryptophan, and the count of aliphatic ligand hydroxy hydrogens. The first two descriptors have a positive sign indicating that they contribute favorably to the binding energy, whereas the count of hydroxy hydrogens contributes unfavorably to the binding free energy observed. The fact that such a simple model can be so effective raises a couple of questions that are addressed in the article.

Global free energy scoring functions based on distance-dependent atom-type pair descriptors.

Scoring functions for protein-ligand docking have received much attention in the past two decades. In many cases, remarkable success has been demonstrated in predicting the correct geometry of interaction. On independent test sets, however, the predicted binding energies or scores correlate only slightly with the observed free energies of binding. In this study, we analyze how well free energies of binding can be predicted on the basis of crystal structures using traditional QSAR techniques in a proteochemometric approach. We introduce a new set of protein-ligand interaction descriptors on the basis of distance-binned Crippen-like atom type pairs. A subset of the publicly available PDBbind09-CN refined set (MW < 900 g/mol, #P < 2, ndon + nacc < 20; N = 1387) is being used as data set. It is demonstrated how simple, yet surprisingly good, scoring functions can be generated for the whole diverse database (R(2)(out-of-bag) = 0.48, R(p) = 0.69, RMSE = 1.44, MUE = 1.14) and individual protein family subsets. This performance is significantly better than the performance of almost all other scoring functions published that have been validated on a test set as large and diverse as the PDBbind refined set. We also find that on some protein families surprisingly good scoring functions can be obtained using simple ligand-only descriptors like logS, logP, and molecular weight. The ligand-descriptor based scoring function equals or even outperforms commonly used scoring functions, highlighting the need for better scoring functions. We demonstrate how the observed performance depends on the validation strategy, and we outline a general validation protocol for future free energy scoring functions.

Avoidance of the Ames test liability for aryl-amines via computation.

Aryl-amines are commonly used synthons in modern drug discovery, however a minority of these chemical templates have the potential to cause toxicity through mutagenicity. The toxicity mostly arises through a series of metabolic steps leading to a reactive electrophilic nitrenium cation intermediate that reacts with DNA nucleotides causing mutation. Highly detailed in silico calculations of the energetics of chemical reactions involved in the metabolic formation of nitrenium cations have been performed. This allowed a critical assessment of the accuracy and reliability of using a theoretical formation energy of the DNA-reactive nitrenium intermediate to correlate with the Ames test response. This study contains the largest data set reported to date, and presents the in silico calculations versus the in vitro Ames response data in the form of beanplots commonly used in statistical analysis. A comparison of this quantum mechanical approach to QSAR and knowledge-based methods is also reported, as well as the calculated formation energies of nitrenium ions for thousands of commercially available aryl-amines generated as a watch-list for medicinal chemists in their synthetic optimization strategies.

Using Machine Learning to Parse Chemical Mixture Descriptions.

Chemical mixtures have recently come to the attention of open standards and data structures for capturing machine-readable descriptions for informatics uses. At the present time, essentially all transmission of information about mixtures is done using short text descriptions that are readable only by trained scientists, and there are no accessible repositories of marked-up mixture data. We have designed a machine learning tool that can interpret mixture descriptions and upgrade them to the high-level Mixfile format, which can in turn be used to generate Mixtures InChI notation. The interpretation achieves a high success rate and can be used at scale to markup large catalogs and inventories, with some expert checking to catch edge cases. The training data that was accumulated during the project is made openly available, along with previously released mixture editing tools and utilities.

A physical properties based approach for the exploration of a 4-hydroxybenzothiazolone series of beta2-adrenoceptor agonists as inhaled long-acting bronchodilators.

The chiral synthesis of a 4-hydroxybenzothiazolone based series of beta(2)-adrenoceptor agonists is described. Using this methodology a library of N-substituted analogues were prepared for the rapid identification of leads with the potential to be fast onset and long-acting inhaled bronchodilators with improved therapeutic margins. The design of the library to achieve the targeted profile was based upon lipophilicity and metabolism based hypotheses. This approach identified beta-phenethyl, alpha-substituted cyclopentyl and monoterpene N-substituents to be of particular interest for further evaluation, as exemplified by structures 19, 29 and 33, respectively.

Synthesis and evaluation of two series of 4'-aza-carbocyclic nucleosides as adenosine A2A receptor agonists.

The synthesis of two series of 4'-aza-carbocyclic nucleosides are described in which the 4'-substituent is either a reversed amide, relative to the carboxamide of NECA, or an N-bonded heterocycle. Using established purine substitution patterns, potent and selective examples of agonists of the human adenosine A(2A) receptor have been identified from both series. The propionamides 14-18 and the 4-hydroxymethylpyrazole 32 were determined to be the most potent and selective examples from the 4'-reversed amide and 4'-N-bonded heterocyclic series, respectively.

Leave-cluster-out cross-validation is appropriate for scoring functions derived from diverse protein data sets.

With the emergence of large collections of protein-ligand complexes complemented by binding data, as found in PDBbind or BindingMOAD, new opportunities for parametrizing and evaluating scoring functions have arisen. With huge data collections available, it becomes feasible to fit scoring functions in a QSAR style, i.e., by defining protein-ligand interaction descriptors and analyzing them with modern machine-learning methods. As in each data modeling ansatz, care has to be taken to validate the model carefully. Here, we show that there are large differences measured in R (0.77 vs 0.46) or R² (0.59 vs 0.21) for a relatively simple scoring function depending on whether it is validated against the PDBbind core set or validated in a leave-cluster-out cross-validation. If proteins from the same family are present in both the training and validation set, the estimated prediction quality from standard validation techniques looks too optimistic.

Capturing mixture composition: an open machine-readable format for representing mixed substances.

We describe a file format that is designed to represent mixtures of compounds in a way that is fully machine readable. This Mixfile format is intended to fill the same role for substances that are composed of multiple components as the venerable Molfile does for specifying individual structures. This much needed datastructure is intended to replace current practices for communicating information about mixtures, which usually relies on human-readable text descriptions, drawing several species within a single molecular diagram, or mutually incompatible ad hoc solutions. We describe an open source software application for editing mixture files, which can also be used as web-ready tools for manipulating the file format. We also present a corpus of mixture examples, which we have extracted from collections of text-based descriptions. Furthermore, we present an early look at the proposed IUPAC Mixtures InChI specification, instances of which can be automatically generated using the Mixfile format as a precursor.

Insight into the mechanism of inactivation and pH sensitivity in potassium channels from molecular dynamics simulations.

Potassium (K (+)) channels can regulate ionic conduction through their pore by a mechanism, involving the selectivity filter, known as C-type inactivation. This process is rapid in the hERG K (+) channel and is fundamental to its physiological role. Although mutations within hERG are known to remove this process, a structural basis for the inactivation mechanism has yet to be characterized. Using MD simulations based on homology modeling, we observe that the carbonyl of the filter aromatic, Phe627, forming the S 0 K (+) binding site, swiftly rotates away from the conduction axis in the wild-type channel. In contrast, in well-characterized non-inactivating mutant channels, this conformational change occurs less frequently. In the non-inactivating channels, interactions with a water molecule located behind the selectivity filter are critical to the enhanced stability of the conducting state. We observe comparable conformational changes in the acid sensitive TASK-1 channel and propose a common mechanism in these channels for regulating efflux of K (+) ions through the selectivity filter.

Exploiting QSAR models in lead optimization.

QSAR models can play a vital role in both the opening phase and the endgame of lead optimization. In the opening phase, there is often a large quantity of data from high-throughput screening (HTS), and potential leads need to be selected from several distinct chemotypes. In the endgame, the throughput of the final, critical ADMET and pharmacokinetic assays is often not sufficient to allow full experimental characterization of all the structures in the available time. A considerable amount of the current research toward new QSAR models is based on the modeling of the general ADMET phenomena, with the aim of constructing globally applicable models. The process to construct QSAR models is relatively straightforward; however, it is also simple to build misleading, or even incorrect, models. This review considers the key developments in the field of QSAR modeling: how QSAR models are constructed, how they can be validated, their reliability and their applicability. If applied carefully and appropriately, the QSAR technique has a valuable role to play during lead optimization.