Noel M O'Boyle’s research while affiliated with University College Cork and other places

Aim: The assumption in scaffold hopping is that changing the scaffold does not change the binding mode and the same structure-activity relationships (SARs) are seen for substituents decorating each scaffold. Results/methodology: We present the use of matched series analysis, an extension of matched molecular pair analysis, to automate the analysis of a project's data and detect the presence or absence of comparable SAR between chemical series. Conclusion: The presence of SAR transfer can confirm the perceived binding mode overlay of different chemotypes or suggest new arrangements between scaffolds that may have gone unnoticed. The absence of series correlation can highlight the presence of inconsistent data points where assay values should be reconfirmed, or provide challenge to any project dogma.

Background The concept of molecular similarity is one of the central ideas in cheminformatics, despite the fact that it is ill-defined and rather difficult to assess objectively. Here we propose a practical definition of molecular similarity in the context of drug discovery: molecules A and B are similar if a medicinal chemist would be likely to synthesise and test them around the same time as part of the same medicinal chemistry program. The attraction of such a definition is that it matches one of the key uses of similarity measures in early-stage drug discovery. If we make the assumption that molecules in the same compound activity table in a medicinal chemistry paper were considered similar by the authors of the paper, we can create a dataset of similar molecules from the medicinal chemistry literature. Furthermore, molecules with decreasing levels of similarity to a reference can be found by either ordering molecules in an activity table by their activity, or by considering activity tables in different papers which have at least one molecule in common. Results Using this procedure with activity data from ChEMBL, we have created two benchmark datasets for structural similarity that can be used to guide the development of improved measures. Compared to similar results from a virtual screen, these benchmarks are an order of magnitude more sensitive to differences between fingerprints both because of their size and because they avoid loss of statistical power due to the use of mean scores or ranks. We measure the performance of 28 different fingerprints on the benchmark sets and compare the results to those from the Riniker and Landrum (J Cheminf 5:26, 2013. doi:10.1186/1758-2946-5-26) ligand-based virtual screening benchmark. Conclusions Extended-connectivity fingerprints of diameter 4 and 6 are among the best performing fingerprints when ranking diverse structures by similarity, as is the topological torsion fingerprint. However, when ranking very close analogues, the atom pair fingerprint outperforms the others tested. When ranking diverse structures or carrying out a virtual screen, we find that the performance of the ECFP fingerprints significantly improves if the bit-vector length is increased from 1024 to 16,384.Graphical abstractAn example series from one of the benchmark datasets. Each fingerprint is assessed on its ability to reproduce a specific series order. Electronic supplementary material The online version of this article (doi:10.1186/s13321-016-0148-0) contains supplementary material, which is available to authorized users.

Awareness of the adverse effects of chemicals is important in biomedical research and healthcare. Text mining can allow timely and low-cost extraction of this knowledge from the biomedical literature. We extended our text mining solution, LeadMine, to identify diseases and chemical-induced disease relationships (CIDs). LeadMine is a dictionary/grammar-based entity recognizer and was used to recognize and normalize both chemicals and diseases to Medical Subject Headings (MeSH) IDs. The disease lexicon was obtained from three sources: MeSH, the Disease Ontology and Wikipedia. The Wikipedia dictionary was derived from pages with a disease/symptom box, or those where the page title appeared in the lexicon. Composite entities (e.g. heart and lung disease) were detected and mapped to their composite MeSH IDs. For CIDs, we developed a simple pattern-based system to find relationships within the same sentence. Our system was evaluated in the BioCreative V Chemical–Disease Relation task and achieved very good results for both disease concept ID recognition (F1-score: 86.12%) and CIDs (F1-score: 52.20%) on the test set. As our system was over an order of magnitude faster than other solutions evaluated on the task, we were able to apply the same system to the entirety of MEDLINE allowing us to extract a collection of over 250 000 distinct CIDs.

This is an archive of the source code for cclib version 1.4, orginally released on github (https://github.com/cclib/cclib/releases/tag/v1.4).

This is an archive of the source code for cclib version 1.5, orginally released on github (https://github.com/cclib/cclib/releases/tag/v1.5).

Lead optimisation projects progress by making successive enhancements to one or more starting structures. This is a classic multi-objective optimisation procedure where the goal is not only to improve potency but also to improve physicochemical and absorption, distribution, metabolism and elimination (ADME) properties. For physicochemical and ADME properties, the popular matched molecular pair analysis method has been a successful strategy; however, it notably fails in the goal of improving potency. Here we discuss a lead optimisation approach involving matched series, the extension of matched pairs to more than two R-groups, which can successfully be used to guide molecular design towards improved potency. Furthermore, this approach retains the attractive features of matched pair analysis in that it is entirely driven by experimental data and is a natural fit to the medicinal chemistry approach of designing analogues by successive small changes to an existing molecule.

A Matched Molecular Series is the general form of a Matched Molecular Pair, and refers to a set of two or more molecules with the same scaffold but different R groups at the same position. We describe Matsy, a knowledge-based method that uses matched series to predict R groups likely to improve activity given an observed activity order for some R groups. We compare the Matsy predictions based on activity data from ChEMBLdb to the recommendations of the Topliss Tree and carry out a large scale retrospective test to measure performance. We show that the basis for predictive success is preferred orders in matched series and that this preference is stronger for longer series. The Matsy algorithm allows medicinal chemists to integrate activity trends from diverse medicinal chemistry programmes and apply them to problems of interest as a Topliss-like recommendation or as a hypothesis generator to aid compound design.

A matched molecular series is the general form of a matched molecular pair and refers to a set of two or more molecules with the same scaffold but different R groups at the same position. We describe Matsy, a knowledge-based method that uses matched series to predict R groups likely to improve activity given an observed activity order for some R groups. We compare the Matsy predictions based on activity data from ChEMBLdb to the recommendations of the Topliss tree and carry out a large scale retrospective test to measure performance. We show that the basis for predictive success is preferred orders in matched series and that this preference is stronger for longer series. The Matsy algorithm allows medicinal chemists to integrate activity trends from diverse medicinal chemistry programs and apply them to problems of interest as a Topliss-like recommendation or as a hypothesis generator to aid compound design.

NOVEL TECHNOLOGIES IN the life sciences produce information at an accelerating rate, with public data stores (such as the one managed by the European Bioinformatics Institute http://www.ebi. ac.uk) containing on the order of 10PB of biological information. For nearly 40 years, the same was not so for chemical information, but in 2004 a large public small-molecule structure repository (PubChem http://pubchem. ncbi.nlm.nih.gov) was made freely available by the National Library of Medicine (part of the U.S. National Institutes of Health) and soon followed by other databases. Likewise, while many of the foundational algorithms of cheminformatics have been described since the 1950s, open-source software implementing many of them have become accessible only since the mid-1990s.

Python script that shows how to parse the canonical labels from an InChI with auxiliary information.