A rule-based ontological framework for the classification of molecules

BACKGROUND: A variety of key activities within life sciences research involves integrating and intelligently managing large amounts of biochemical information. Semantic technologies provide an intuitive way to organise and sift through these rapidly growing datasets via the design and maintenance of ontology-supported knowledge bases. To this end, OWL-a W3C standard declarative language- has been extensively used in the deployment of biochemical ontologies that can be conveniently organised using the classification facilities of OWL-based tools. One of the most established ontologies for the chemical domain is ChEBI, an open-access dictionary of molecular entities that supplies high quality annotation and taxonomical information for biologically relevant compounds. However, ChEBI is being manually expanded which hinders its potential to grow due to the limited availability of human resources. RESULTS: In this work, we describe a prototype that performs automatic classification of chemical compounds. The software we present implements a sound and complete reasoning procedure of a formalism that extends datalog and builds upon an off-the-shelf deductive database system. We capture a wide range of chemical classes that are not expressible with OWL-based formalisms such as cyclic molecules, saturated molecules and alkanes. Furthermore, we describe a surface 'less-logician-like' syntax that allows application experts to create ontological descriptions of complex biochemical objects without prior knowledge of logic. In terms of performance, a noticeable improvement is observed in comparison with previous approaches. Our evaluation has discovered subsumptions that are missing from the manually curated ChEBI ontology as well as discrepancies with respect to existing subclass relations. We illustrate thus the potential of an ontology language suitable for the life sciences domain that exhibits a favourable balance between expressive power and practical feasibility. CONCLUSIONS: Our proposed methodology can form the basis of an ontology-mediated application to assist biocurators in the production of complete and error-free taxonomies. Moreover, such a tool could contribute to a more rapid development of the ChEBI ontology and to the efforts of the ChEBI team to make annotated chemical datasets available to the public. From a modelling point of view, our approach could stimulate the adoption of a different and expressive reasoning paradigm based on rules for which state-of-the-art and highly optimised reasoners are available; it could thus pave the way for the representation of a broader spectrum of life sciences and biomedical knowledge.</p

ClassyFire: automated chemical classification with a comprehensive, computable taxonomy

Additional file 5. Use cases. Text-based search on the ClassyFire web server. (A) Building the query. (B) Sparteine, one of the returned compounds

Fusion of molecular representations and prediction of biological activity using convolutional neural network and transfer learning

Basic structural features and physicochemical properties of chemical molecules determine their behaviour during chemical, physical, biological and environmental processes and hence need to be investigated for determining and modelling the actions of the molecule. Computational approaches such as machine learning methods are alternatives to predict physiochemical properties of molecules based on their structures. However, limited accuracy and error rates of these predictions restrict their use. This study developed three classes of new methods based on deep learning convolutional neural network for bioactivity prediction of chemical compounds. The molecules are represented as a convolutional neural network (CNN) with new matrix format to represent the molecular structures. The first class of methods involved the introduction of three new molecular descriptors, namely Mol2toxicophore based on molecular interaction with toxicophores features, Mol2Fgs based on distributed representation for constructing abstract features maps of a selected set of small molecules, and Mol2mat, which is a molecular matrix representation adapted from the well-known 2D-fingerprint descriptors. The second class of methods was based on merging multi-CNN models that combined all the molecular representations. The third class of methods was based on automatic learning of features using values within the neurons of the last layer in the proposed CNN architecture. To evaluate the performance of the methods, a series of experiments were conducted using two standard datasets, namely MDL Drug Data Report (MDDR) and Sutherland datasets. The MDDR datasets comprised 10 homogeneous and 10 heterogeneous activity classes, whilst Sutherland datasets comprised four homogeneous activity classes. Based on the experiments, the Mol2toxicophore showed satisfactory prediction rates of 92% and 80% for homogeneous and heterogeneous activity classes, respectively. The Mol2Fgs was better than Mol2toxicophore with prediction accuracy result of 95% for homogeneous and 90% for heterogeneous activity classes. The Mol2mat molecular representation had the highest prediction accuracy with 97% and 94% for homogeneous and heterogeneous datasets, respectively. The combined multi-CNN model leveraging on the knowledge acquired from the three molecular presentations produced better accuracy rate of 99% for the homogeneous and 98% for heterogeneous datasets. In terms of molecular similarity measure, use of the values in the neurons of the last hidden layer as the automatically learned feature in the multi-CNN model as a novel molecular learning representation was found to perform well with 88.6% in terms of average recall value in 5% structures most similar to the target search. The results have demonstrated that the newly developed methods can be effectively used for bioactivity prediction and molecular similarity searching

Structural bioinformatics studies and tool development related to drug discovery

This thesis is divided into two distinct sections which can be combined under the broad umbrella of structural bioinformatics studies related to drug discovery. The first section involves the establishment of an online South African natural products database. Natural products (NPs) are chemical entities synthesised in nature and are unrivalled in their structural complexity, chemical diversity, and biological specificity, which has long made them crucial to the drug discovery process. South Africa is rich in both plant and marine biodiversity and a great deal of research has gone into isolating compounds from organisms found in this country. However, there is no official database containing this information, making it difficult to access for research purposes. This information was extracted manually from literature to create a database of South African natural products. In order to make the information accessible to the general research community, a website, named “SANCDB”, was built to enable compounds to be quickly and easily searched for and downloaded in a number of different chemical formats. The content of the database was assessed and compared to other established natural product databases. Currently, SANCDB is the only database of natural products in Africa with an online interface. The second section of the thesis was aimed at performing structural characterisation of proteins with the potential to be targeted for antimalarial drug therapy. This looked specifically at 1) The interactions between an exported heat shock protein (Hsp) from Plasmodium falciparum (P. falciparum), PfHsp70-x and various host and exported parasite J proteins, as well as 2) The interface between PfHsp90 and the heat shock organising protein (PfHop). The PfHsp70-x:J protein study provided additional insight into how these two proteins potentially interact. Analysis of the PfHsp90:PfHop also provided a structural insight into the interaction interface between these two proteins and identified residues that could be targeted due to their contribution to the stability of the Hsp90:Hop binding complex and differences between parasite and human proteins. These studies inspired the development of a homology modelling tool, which can be used to assist researchers with homology modelling, while providing them with step-by-step control over the entire process. This thesis presents the establishment of a South African NP database and the development of a homology modelling tool, inspired by protein structural studies. When combined, these two applications have the potential to contribute greatly towards in silico drug discovery research