BRADSHAW: a system for automated molecular design.

This paper introduces BRADSHAW (Biological Response Analysis and Design System using an Heterogenous, Automated Workflow), a system for automated molecular design which integrates methods for chemical structure generation, experimental design, active learning and cheminformatics tools. The simple user interface is designed to facilitate access to large scale automated design whilst minimising software development required to introduce new algorithms, a critical requirement in what is a very fast moving field. The system embodies a philosophy of automation, best practice, experimental design and the use of both traditional cheminformatics and modern machine learning algorithms.

Correction to: BRADSHAW: a system for automated molecular design.

The original version of this article unfortunately contained some mistakes in the references.

Compression of molecular interaction fields using wavelet thumbnails: application to molecular alignment.

Molecular interaction fields provide a useful description of ligand binding propensity and have found widespread use in computer-aided drug design, for example, to characterize protein binding sites and in small molecular applications, such as three-dimensional quantitative structure-activity relationships, physicochemical property prediction, and virtual screening. However, the grids on which the field data are stored are typically very large, consisting of thousands of data points, which make them cumbersome to store and manipulate. The wavelet transform is a commonly used data compression technique, for example, in signal processing and image compression. Here we use the wavelet transform to encode molecular interaction fields as wavelet thumbnails, which represent the original grid data in significantly reduced volumes. We describe a method for aligning wavelet thumbnails based on extracting extrema from the thumbnails and subsequently use them for virtual screening. We demonstrate that wavelet thumbnails provide an effective method of capturing the three-dimensional information encoded in a molecular interaction field.

Structure and property based design of factor Xa inhibitors: pyrrolidin-2-ones with aminoindane and phenylpyrrolidine P4 motifs.

The rational design, syntheses and evaluation of potent sulfonamidopyrrolidin-2-one-based factor Xa inhibitors incorporating aminoindane and phenylpyrrolidine P4 motifs are described. These series delivered highly potent anticoagulant compounds with excellent oral pharmacokinetic profiles; however, significant time dependant P450 inhibition was an issue for the aminoindane series, but this was not observed with the phenylpyrrolidine motif, which produced candidate quality molecules with potential for once-daily oral dosing in humans.

The discovery of potent and long-acting oral factor Xa inhibitors with tetrahydroisoquinoline and benzazepine P4 motifs.

The discovery and evaluation of potent and long-acting oral sulfonamidopyrrolidin-2-one factor Xa inhibitors with tetrahydroisoquinoline and benzazepine P4 motifs are described. Unexpected selectivity issues versus tissue plasminogen activator in the former series were addressed in the later, delivering a robust candidate for progression towards clinical studies.

Structure-activity relationships and in vivo activity of (1H-pyrazol-4-yl)acetamide antagonists of the P2X(7) receptor.

Structure-activity relationships (SAR) of analogues of lead compound 1 were investigated and compound 16 was selected for further study in animal models of pain. Compound 16 was shown to be a potent antihyperalgesic agent in both the rat acute complete Freund's adjuvant (CFA) model of inflammatory pain [Iadarola, M. J.; Douglass, J.; Civelli, O.; Naranjo, J. R. rain Res.1988, 455, 205] and the knee joint model of chronic inflammatory pain [Wilson, A. W.; Medhurst, S. J.; Dixon, C. I.; Bontoft, N. C.; Winyard, L. A.; Brackenborough, K. T.; De Alba, J.; Clarke, C. J.; Gunthorpe, M. J.; Hicks, G. A.; Bountra, C.; McQueen, D. S.; Chessell, I. P. Eur. J. Pain2006, 10, 537].

Synthesis and structure-activity relationships of a series of (1H-pyrazol-4-yl)acetamide antagonists of the P2X7 receptor.

High-throughput screening identified compound 1 as a potent P2X(7) receptor antagonist suitable for lead optimisation. Structure-activity relationships (SAR) of a series of (1H-pyrazol-4-yl)acetamides were investigated and compound 32 was identified as a potent P2X(7) antagonist with enhanced potency and favourable physicochemical and pharmacokinetic properties.

Discovery and structure-activity relationships of a series of pyroglutamic acid amide antagonists of the P2X7 receptor.

A computational lead-hopping exercise identified compound 4 as a structurally distinct P2X(7) receptor antagonist. Structure-activity relationships (SAR) of a series of pyroglutamic acid amide analogues of 4 were investigated and compound 31 was identified as a potent P2X(7) antagonist with excellent in vivo activity in animal models of pain, and a profile suitable for progression to clinical studies.

Structure and property based design of factor Xa inhibitors: pyrrolidin-2-ones with monoaryl P4 motifs.

Structure and property based drug design was exploited in the synthesis of sulfonamidopyrrolidin-2-one-based factor Xa inhibitors, incorporating neutral and basic monoaryl P4 groups, ultimately producing potent inhibitors with effective levels of anticoagulant activity and extended oral pharmacokinetic profiles. However, time dependant inhibition of Cytochrome P450 3A4 was a particular issue with this series.

Structure and property based design of factor Xa inhibitors: biaryl pyrrolidin-2-ones incorporating basic heterocyclic motifs.

Structure and property based drug design was exploited in the synthesis of sulfonamidopyrrolidin-2-one-based factor Xa (fXa) inhibitors, incorporating basic biaryl P4 groups, producing highly potent inhibitors with significant anticoagulant activities and encouraging oral pharmacokinetic profiles.

Structure and property based design of factor Xa inhibitors: pyrrolidin-2-ones with biaryl P4 motifs.

Structure and property based drug design was exploited in the synthesis of sulfonamidopyrrolidin-2-one-based factor Xa (fXa) inhibitors, incorporating biaryl P4 groups, producing highly potent inhibitors with encouraging oral pharmacokinetic profiles and significant but sub-optimal anticoagulant activities.

Explicit interaction information from WikiPathways in RDF facilitates drug discovery in the Open PHACTS Discovery Platform.

Open PHACTS is a pre-competitive project to answer scientific questions developed recently by the pharmaceutical industry. Having high quality biological interaction information in the Open PHACTS Discovery Platform is needed to answer multiple pathway related questions. To address this, updated WikiPathways data has been added to the platform. This data includes information about biological interactions, such as stimulation and inhibition. The platform's Application Programming Interface (API) was extended with appropriate calls to reference these interactions. These new methods of the Open PHACTS API are available now.

Factor Xa inhibitors: S1 binding interactions of a series of N-{(3S)-1-[(1S)-1-methyl-2-morpholin-4-yl-2-oxoethyl]-2-oxopyrrolidin-3-yl}sulfonamides.

Factor Xa inhibitory activities for a series of N-{(3S)-1-[(1S)-1-methyl-2-morpholin-4-yl-2-oxoethyl]-2-oxopyrrolidin-3-yl}sulfonamides with different P1 groups are described. These data provide insight into binding interactions within the S1 primary specificity pocket; rationales are presented for the derived SAR on the basis of electronic interactions through crystal structures of fXa-ligand complexes and molecular modeling studies. A good correlation between in vitro anticoagulant activities with lipophilicity and the extent of human serum albumin binding is observed within this series of potent fXa inhibitors. Pharmacokinetic profiles in rat and dog, together with selectivity over other trypsin-like serine proteases, identified 1f as a candidate for further evaluation.

Assessment of the significance of patent-derived information for the early identification of compound-target interaction hypotheses.

BACKGROUND: Patents are an important source of information for effective decision making in drug discovery. Encouragingly, freely accessible patent-chemistry databases are now in the public domain. However, at present there is still a wide gap between relatively low coverage-high quality manually-curated data sources and high coverage data sources that use text mining and automated extraction of chemical structures. To secure much needed funding for further research and an improved infrastructure, hard evidence is required to demonstrate the significance of patent-derived information in drug discovery. Surprisingly little such evidence has been reported so far. To address this, the present study attempts to quantify the relevance of patents for formulating and substantiating hypotheses for compound-target interactions. RESULTS: A manually-curated set of 130 compound-target interaction pairs annotated with what are considered to be the earliest patent and publication has been produced. The analysis of this set revealed that in stark contrast to what has been reported for novel chemical structures, only about 10% of the compound-target interaction pairs could be found in publications in the scientific literature within one year of being reported in patents. The average delay across all interaction pairs is close to 4 years. In an attempt to benchmark current capabilities, it was also examined how much of the benefit of using patent-derived information can be retained when a bioannotated version of SureChEMBL is used as secondary source for the patent literature. Encouragingly, this approach found the patents in the annotated set for 72% of the compound-target interaction pairs. Similarly, the effect of using the bioactivity database ChEMBL as secondary source for the scientific literature was studied. Here, the publications from the annotated set were only found for 46% of the compound-target interaction pairs. CONCLUSION: Patent-derived information is a significant enabler for formulating compound-target interaction hypotheses even in cases where the respective interaction is later reported in the scientific literature. The findings of this study clearly highlight the significance of future investments in the development and provision of databases and tools that will allow scientists to search patent information in a comprehensive, reliable, and efficient manner.

A critical assessment of docking programs and scoring functions.

Docking is a computational technique that samples conformations of small molecules in protein binding sites; scoring functions are used to assess which of these conformations best complements the protein binding site. An evaluation of 10 docking programs and 37 scoring functions was conducted against eight proteins of seven protein types for three tasks: binding mode prediction, virtual screening for lead identification, and rank-ordering by affinity for lead optimization. All of the docking programs were able to generate ligand conformations similar to crystallographically determined protein/ligand complex structures for at least one of the targets. However, scoring functions were less successful at distinguishing the crystallographic conformation from the set of docked poses. Docking programs identified active compounds from a pharmaceutically relevant pool of decoy compounds; however, no single program performed well for all of the targets. For prediction of compound affinity, none of the docking programs or scoring functions made a useful prediction of ligand binding affinity.

Managing expectations: assessment of chemistry databases generated by automated extraction of chemical structures from patents.

BACKGROUND: First public disclosure of new chemical entities often takes place in patents, which makes them an important source of information. However, with an ever increasing number of patent applications, manual processing and curation on such a large scale becomes even more challenging. An alternative approach better suited for this large corpus of documents is the automated extraction of chemical structures. A number of patent chemistry databases generated by using the latter approach are now available but little is known that can help to manage expectations when using them. This study aims to address this by comparing two such freely available sources, SureChEMBL and IBM SIIP (IBM Strategic Intellectual Property Insight Platform), with manually curated commercial databases. RESULTS: When looking at the percentage of chemical structures successfully extracted from a set of patents, using SciFinder as our reference, 59 and 51 % were also found in our comparison in SureChEMBL and IBM SIIP, respectively. When performing this comparison with compounds as starting point, i.e. establishing if for a list of compounds the databases provide the links between chemical structures and patents they appear in, we obtained similar results. SureChEMBL and IBM SIIP found 62 and 59 %, respectively, of the compound-patent pairs obtained from Reaxys. CONCLUSIONS: In our comparison of automatically generated vs. manually curated patent chemistry databases, the former successfully provided approximately 60 % of links between chemical structure and patents. It needs to be stressed that only a very limited number of patents and compound-patent pairs were used for our comparison. Nevertheless, our results will hopefully help to manage expectations of users of patent chemistry databases of this type and provide a useful framework for more studies like ours as well as guide future developments of the workflows used for the automated extraction of chemical structures from patents. The challenges we have encountered whilst performing this study highlight that more needs to be done to make such assessments easier. Above all, more adequate, preferably open access to relevant 'gold standards' is required.

The application of the open pharmacological concepts triple store (open PHACTS) to support drug discovery research.

Integration of open access, curated, high-quality information from multiple disciplines in the Life and Biomedical Sciences provides a holistic understanding of the domain. Additionally, the effective linking of diverse data sources can unearth hidden relationships and guide potential research strategies. However, given the lack of consistency between descriptors and identifiers used in different resources and the absence of a simple mechanism to link them, gathering and combining relevant, comprehensive information from diverse databases remains a challenge. The Open Pharmacological Concepts Triple Store (Open PHACTS) is an Innovative Medicines Initiative project that uses semantic web technology approaches to enable scientists to easily access and process data from multiple sources to solve real-world drug discovery problems. The project draws together sources of publicly-available pharmacological, physicochemical and biomolecular data, represents it in a stable infrastructure and provides well-defined information exploration and retrieval methods. Here, we highlight the utility of this platform in conjunction with workflow tools to solve pharmacological research questions that require interoperability between target, compound, and pathway data. Use cases presented herein cover 1) the comprehensive identification of chemical matter for a dopamine receptor drug discovery program 2) the identification of compounds active against all targets in the Epidermal growth factor receptor (ErbB) signaling pathway that have a relevance to disease and 3) the evaluation of established targets in the Vitamin D metabolism pathway to aid novel Vitamin D analogue design. The example workflows presented illustrate how the Open PHACTS Discovery Platform can be used to exploit existing knowledge and generate new hypotheses in the process of drug discovery.

Scientific competency questions as the basis for semantically enriched open pharmacological space development.

Molecular information systems play an important part in modern data-driven drug discovery. They do not only support decision making but also enable new discoveries via association and inference. In this review, we outline the scientific requirements identified by the Innovative Medicines Initiative (IMI) Open PHACTS consortium for the design of an open pharmacological space (OPS) information system. The focus of this work is the integration of compound-target-pathway-disease/phenotype data for public and industrial drug discovery research. Typical scientific competency questions provided by the consortium members will be analyzed based on the underlying data concepts and associations needed to answer the questions. Publicly available data sources used to target these questions as well as the need for and potential of semantic web-based technology will be presented.

Wavelet Approximation of GRID Fields: Application to Quantitative Structure-Activity Relationships.

Molecular interaction fields such as those computed by the GRID program are widely used in applications such as virtual screening, molecular docking and 3D-QSAR modelling. They characterise molecules according to their favourable interaction sites and therefore enable predictions to be made on how molecules might interact. The fields are, however, comprised of a very large number of data points which presents difficulties for many applications. For example, there are likely to be high degrees of correlation between the variables which can lead to misleading results in 3D-QSAR. We describe the use of wavelet methods for approximating such data into a much smaller number of variables. We present a number of validation experiments, including use of the approximated GRIDs in 3D-QSAR, and demonstrate that wavelet approximation at high levels of data compression preserves the information content in GRID fields while significantly reducing computational requirements.

Using Tversky similarity searches for core hopping: finding the needles in the haystack.

The combination of Daylight fingerprints and the Tversky coefficient is a powerful method for performing core hopping, that is, scaffold (or lead) hopping where the main structural difference between the query and bioactive target molecule is located in the central core of the molecular structure. However, a major disadvantage of this approach is the fact that a large number of false positives (in the context of core hopping) are retrieved. The tool we have developed and which is described here can be used to postprocess the hits from Daylight Tversky similarity searches by fragmenting the molecules and subsequently annotating them in a way that assists the users in removing false positives and enables them to better focus on molecules of interest. To validate our approach, we have selected four biological targets for which scaffold hopping examples have been reported. We present results from searches in databases containing published activity data and the subsequent analysis of the hits aimed at establishing the potential of our approach.