1,119 research outputs found
The Evaluation Of Molecular Similarity And Molecular Diversity Methods Using Biological Activity Data
This paper reviews the techniques available for quantifying the effectiveness of methods for molecule similarity and molecular diversity, focusing in particular on similarity searching and on compound selection procedures. The evaluation criteria considered are based on biological activity data, both qualitative and quantitative, with rather different criteria needing to be used depending on the type of data available
Chemoinformatics Research at the University of Sheffield: A History and Citation Analysis
This paper reviews the work of the Chemoinformatics Research Group in the Department of Information Studies at the University of Sheffield, focusing particularly on the work carried out in the period 1985-2002. Four major research areas are discussed, these involving the development of methods for: substructure searching in databases of three-dimensional structures, including both rigid and flexible molecules; the representation and searching of the Markush structures that occur in chemical patents; similarity searching in databases of both two-dimensional and three-dimensional structures; and compound selection and the design of combinatorial libraries. An analysis of citations to 321 publications from the Group shows that it attracted a total of 3725 residual citations during the period 1980-2002. These citations appeared in 411 different journals, and involved 910 different citing organizations from 54 different countries, thus demonstrating the widespread impact of the Group's work
A Multiple Classifier System Identifies Novel Cannabinoid CB2 Receptor Ligands
open access articleDrugs have become an essential part of our lives due to their ability to improve people’s
health and quality of life. However, for many diseases, approved drugs are not yet available
or existing drugs have undesirable side effects, making the pharmaceutical industry strive to
discover new drugs and active compounds. The development of drugs is an expensive
process, which typically starts with the detection of candidate molecules (screening) for an
identified protein target. To this end, the use of high-performance screening techniques has
become a critical issue in order to palliate the high costs. Therefore, the popularity of
computer-based screening (often called virtual screening or in-silico screening) has rapidly
increased during the last decade. A wide variety of Machine Learning (ML) techniques has
been used in conjunction with chemical structure and physicochemical properties for
screening purposes including (i) simple classifiers, (ii) ensemble methods, and more recently
(iii) Multiple Classifier Systems (MCS). In this work, we apply an MCS for virtual screening
(D2-MCS) using circular fingerprints. We applied our technique to a dataset of cannabinoid
CB2 ligands obtained from the ChEMBL database. The HTS collection of Enamine
(1.834.362 compounds), was virtually screened to identify 48.432 potential active molecules
using D2-MCS. This list was subsequently clustered based on circular fingerprints and from
each cluster, the most active compound was maintained. From these, the top 60 were kept,
and 21 novel compounds were purchased. Experimental validation confirmed six highly
active hits (>50% displacement at 10 μM and subsequent Ki determination) and an
additional five medium active hits (>25% displacement at 10 μM). D2-MCS hence provided a
hit rate of 29% for highly active compounds and an overall hit rate of 52%
edge2vec: Representation learning using edge semantics for biomedical knowledge discovery
Representation learning provides new and powerful graph analytical approaches
and tools for the highly valued data science challenge of mining knowledge
graphs. Since previous graph analytical methods have mostly focused on
homogeneous graphs, an important current challenge is extending this
methodology for richly heterogeneous graphs and knowledge domains. The
biomedical sciences are such a domain, reflecting the complexity of biology,
with entities such as genes, proteins, drugs, diseases, and phenotypes, and
relationships such as gene co-expression, biochemical regulation, and
biomolecular inhibition or activation. Therefore, the semantics of edges and
nodes are critical for representation learning and knowledge discovery in real
world biomedical problems. In this paper, we propose the edge2vec model, which
represents graphs considering edge semantics. An edge-type transition matrix is
trained by an Expectation-Maximization approach, and a stochastic gradient
descent model is employed to learn node embedding on a heterogeneous graph via
the trained transition matrix. edge2vec is validated on three biomedical domain
tasks: biomedical entity classification, compound-gene bioactivity prediction,
and biomedical information retrieval. Results show that by considering
edge-types into node embedding learning in heterogeneous graphs,
\textbf{edge2vec}\ significantly outperforms state-of-the-art models on all
three tasks. We propose this method for its added value relative to existing
graph analytical methodology, and in the real world context of biomedical
knowledge discovery applicability.Comment: 10 page
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
Development of a Novel Virtual Screening Cascade Protocol to Identify Potential Trypanothione Reductase Inhibitors
The implementation of a novel sequential computational approach that can be used effectively for virtual screening and identification of prospective ligands that bind to trypanothione reductase (TryR) is reported. The multistep strategy combines a ligand-based virtual screening for building an enriched library of small molecules with a docking protocol (AutoDock, X-Score) for screening against the TryR target. Compounds were ranked by an exhaustive conformational consensus scoring approach that employs a rank-by-rank strategy by combining both scoring functions. Analysis of the predicted ligand-protein interactions highlights the role of bulky quaternary amine moieties for binding affinity. The scaffold hopping (SHOP) process derived from this computational approach allowed the identification of several chemotypes, not previously reported as antiprotozoal agents, which includes dibenzothiepine, dibenzooxathiepine, dibenzodithiepine, and polycyclic cationic structures like thiaazatetracyclo-nonadeca-hexaen-3-ium. Assays measuring the inhibiting effect of these compounds on T. cruzi and T. brucei TryR confirm their potential for further rational optimization
Evaluation of machine-learning methods for ligand-based virtual screening
Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed
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