Chemical information matters: an e-Research perspective on information and data sharing in the chemical sciences

Recently, a number of organisations have called for open access to scientific information and especially to the data obtained from publicly funded research, among which the Royal Society report and the European Commission press release are particularly notable. It has long been accepted that building research on the foundations laid by other scientists is both effective and efficient. Regrettably, some disciplines, chemistry being one, have been slow to recognise the value of sharing and have thus been reluctant to curate their data and information in preparation for exchanging it. The very significant increases in both the volume and the complexity of the datasets produced has encouraged the expansion of e-Research, and stimulated the development of methodologies for managing, organising, and analysing "big data". We review the evolution of cheminformatics, the amalgam of chemistry, computer science, and information technology, and assess the wider e-Science and e-Research perspective. Chemical information does matter, as do matters of communicating data and collaborating with data. For chemistry, unique identifiers, structure representations, and property descriptors are essential to the activities of sharing and exchange. Open science entails the sharing of more than mere facts: for example, the publication of negative outcomes can facilitate better understanding of which synthetic routes to choose, an aspiration of the Dial-a-Molecule Grand Challenge. The protagonists of open notebook science go even further and exchange their thoughts and plans. We consider the concepts of preservation, curation, provenance, discovery, and access in the context of the research lifecycle, and then focus on the role of metadata, particularly the ontologies on which the emerging chemical Semantic Web will depend. Among our conclusions, we present our choice of the "grand challenges" for the preservation and sharing of chemical information

Evaluation of a Bayesian inference network for ligand-based virtual screening

Background Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query. Here, we modify the approach to enable a Bayesian inference network to be used for chemical similarity searching, where a database is ranked in order of decreasing probability of bioactivity. Results Bayesian inference networks were implemented using two different types of network and four different types of belief function. Experiments with the MDDR and WOMBAT databases show that a Bayesian inference network can be used to provide effective ligand-based screening, especially when the active molecules being sought have a high degree of structural homogeneity; in such cases, the network substantially out-performs a conventional, Tanimoto-based similarity searching system. However, the effectiveness of the network is much less when structurally heterogeneous sets of actives are being sought. Conclusion A Bayesian inference network provides an interesting alternative to existing tools for ligand-based virtual screening

TeachOpenCADD: a teaching platform for computer-aided drug design using open source packages and data

Owing to the increase in freely available software and data for cheminformatics and structural bioinformatics, research for computer-aided drug design (CADD) is more and more built on modular, reproducible, and easy-to-share pipelines. While documentation for such tools is available, there are only a few freely accessible examples that teach the underlying concepts focused on CADD, especially addressing users new to the field. Here, we present TeachOpenCADD, a teaching platform developed by students for students, using open source compound and protein data as well as basic and CADD-related Python packages. We provide interactive Jupyter notebooks for central CADD topics, integrating theoretical background and practical code. TeachOpenCADD is freely available on GitHub: https://github.com/volkamerlab/TeachOpenCAD

Towards automatic classification within the ChEBI ontology

*Background*
Appearing in a wide variety of contexts, biochemical 'small molecules' are a core element of biomedical data. Chemical ontologies, which provide stable identifiers and a shared vocabulary for use in referring to such biochemical small molecules, are crucial to enable the interoperation of such data. One such chemical ontology is ChEBI (Chemical Entities of Biological Interest), a candidate member ontology of the OBO Foundry. ChEBI is a publicly available, manually annotated database of chemical entities and contains around 18000 annotated entities as of the last release (May 2009). ChEBI provides stable unique identifiers for chemical entities; a controlled vocabulary in the form of recommended names (which are unique and unambiguous), common synonyms, and systematic chemical names; cross-references to other databases; and a structural and role-based classification within the ontology. ChEBI is widely used for annotation of chemicals within biological databases, text-mining, and data integration. ChEBI can be accessed online at "http://www.ebi.ac.uk/chebi/":http://www.ebi.ac.uk/chebi/ and the full dataset is available for download in various formats including SDF and OBO.

*Automated Classification*
The selection of chemical entities for inclusion in the ChEBI database is user-driven. As the use of ChEBI has grown, so too has the backlog of user-requested entries. Inevitably, the annotation backlog creates a bottleneck, and to speed up the annotation process, ChEBI has recently released a submission tool which allows community submissions of chemical entities, groups, and classes. However, classification of chemical entities within the ontology is a difficult and niche activity, and it is unlikely that the community as a whole will be able or willing to correctly and consistently classify each submitted entity, creating required classes where they are missing. As a result, it is likely that while the size of the database grows, the ontological classification will become less sophisticated, unless the classification of new entities is assisted computationally. In addition, the ChEBI database is expecting substantial size growth in the next year, so automatic classification, which has up till now not been possible, is urgently required. Automatic classification would also enable the ChEBI ontology classes to be applied to other compound databases such as PubChem. 

*Description Logic Reasoning*
Description logic based reasoning technology is a prime candidate for development of such an automatic classification system as it allows the rules of the classification system to be encoded within the knowledgebase. Already at 18000 entities, ChEBI is a fair size for a real-world application of description logic reasoning technology, and as the ontology is enhanced with a richer density of asserted relationships, the classification will become more complex and challenging. We have successfully tested a description logic-based classification of chemical entities based on specified structural properties using the hypertableaux-based HermiT reasoner, and found it to be sufficiently efficient to be feasible for use in a production environment on a database of the size that ChEBI is now. However, much work still remains to enrich the ChEBI knowledgebase itself with the properties needed to provide the formal class definitions for use in the automated classification, and to assess the efficiency of the available description logic reasoning technology on a database the size of ChEBI's forecast future growth.

*Acknowledgements*
ChEBI is funded by the European Commission under SLING, grant agreement number 226073 (Integrating Activity) within Research Infrastructures of the FP7 Capacities Specific Programme, and by the BBSRC, grant agreement number BB/G022747/1 within the “Bioinformatics and biological resources” fund

In silico generation of novel, drug-like chemical matter using the LSTM neural network

The exploration of novel chemical spaces is one of the most important tasks of cheminformatics when supporting the drug discovery process. Properly designed and trained deep neural networks can provide a viable alternative to brute-force de novo approaches or various other machine-learning techniques for generating novel drug-like molecules. In this article we present a method to generate molecules using a long short-term memory (LSTM) neural network and provide an analysis of the results, including a virtual screening test. Using the network one million drug-like molecules were generated in 2 hours. The molecules are novel, diverse (contain numerous novel chemotypes), have good physicochemical properties and have good synthetic accessibility, even though these qualities were not specific constraints. Although novel, their structural features and functional groups remain closely within the drug-like space defined by the bioactive molecules from ChEMBL. Virtual screening using the profile QSAR approach confirms that the potential of these novel molecules to show bioactivity is comparable to the ChEMBL set from which they were derived. The molecule generator written in Python used in this study is available on request.Comment: in this version fixed some reference number