3,638 research outputs found
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
Similarity-based virtual screening using 2D fingerprints
This paper summarises recent work at the University of Sheffield on virtual screening methods that use 2D fingerprint measures of structural similarity. A detailed comparison of a large number of similarity coefficients demonstrates that the well-known Tanimoto coefficient remains the method of choice for the computation of fingerprint-based similarity, despite possessing some inherent biases related to the sizes of the molecules that are being sought. Group fusion involves combining the results of similarity searches based on multiple reference structures and a single similarity measure. We demonstrate the effectiveness of this approach to screening, and also describe an approximate form of group fusion, turbo similarity searching, that can be used when just a single reference structure is available
Analysis of Multitarget Activities and Assay Interference Characteristics of Pharmaceutically Relevant Compounds
The availability of large amounts of data in public repositories provide a useful source of knowledge in the field of drug discovery. Given the increasing sizes of compound databases and volumes of activity data, computational data mining can be used to study different characteristics and properties of compounds on a large scale. One of the major source of identification of new compounds in early phase of drug discovery is high-throughput screening where millions of compounds are tested against many targets. The screening data provides opportunities to assess activity profiles of compounds. This thesis aims at systematically mining activity data from publicly available sources in order to study the nature of growth of bioactive compounds, analyze multitarget activities and assay interference characteristics of pharmaceutically relevant compounds in context of polypharmacology. In the first study, growth of bioactive compounds against five major target families is monitored over time and compound-scaffold-CSK (cyclic skeleton) hierarchy is applied to investigate structural diversity of active compounds and topological diversity of their scaffolds. The next part of the thesis is based on the analysis of screening data. Initially, extensively assayed compounds are mined from the PubChem database and promiscuity of these compounds is assessed by taking assay frequencies into account. Next, DCM (dark chemical matter) or consistently inactive compounds that have been extensively tested are systematically extracted and their analog relationships with bioactive compounds are determined in order to derive target hypotheses for DCM. Further, PAINS (pan-assay interference compounds) are identified in the extensively tested set of compounds using substructure filters and their assay interference characteristics are studied. Finally, the limitations of PAINS filters are addressed using machine learning models that can distinguish between promiscuous and DCM PAINS. Structural context dependence of PAINS activities is studied by assessing predictions through feature weighting and mapping
Application and Development of Computational Methods for Ligand-Based Virtual Screening
The detection of novel active compounds that are able to modulate the biological function of a target is the primary goal of drug discovery. Different screening methods are available to identify hit compounds having the desired bioactivity in a large collection of molecules. As a computational method, virtual screening (VS) is used to search compound libraries in silico and identify those compounds that are likely to exhibit a specific activity. Ligand-based virtual screening (LBVS) is a subdiscipline that uses the information of one or more known active compounds in order to identify new hit compounds. Different LBVS methods exist, e.g. similarity searching and support vector machines (SVMs). In order to enable the application of these computational approaches, compounds have to be described numerically. Fingerprints derived from the two-dimensional compound structure, called 2D fingerprints, are among the most popular molecular descriptors available. This thesis covers the usage of 2D fingerprints in the context of LBVS. The first part focuses on a detailed analysis of 2D fingerprints. Their performance range against a wide range of pharmaceutical targets is globally estimated through fingerprint-based similarity searching. Additionally, mechanisms by which fingerprints are capable of detecting structurally diverse active compounds are identified. For this purpose, two different feature selection methods are applied to find those fingerprint features that are most relevant for the active compounds and distinguish them from other compounds. Then, 2D fingerprints are used in SVM calculations. The SVM methodology provides several opportunities to include additional information about the compounds in order to direct LBVS search calculations. In a first step, a variant of the SVM approach is applied to the multi-class prediction problem involving compounds that are active against several related targets. SVM linear combination is used to recover compounds with desired activity profiles and deprioritize compounds with other activities. Then, the SVM methodology is adopted for potency-directed VS. Compound potency is incorporated into the SVM approach through potencyoriented SVM linear combination and kernel function design to direct search calculations to the preferential detection of potent hit compounds. Next, SVM calculations are applied to address an intrinsic limitation of similarity-based methods, i.e., the presence of similar compounds having large differences in their potency. An especially designed SVM approach is introduced to predict compound pairs forming such activity cliffs. Finally, the impact of different training sets on the recall performance of SVM-based VS is analyzed and caveats are identified
Fingerprint-Based Machine Learning Approach to Identify Potent and Selective 5-HT2BR Ligands
The identification of subtype-selective GPCR (G-protein coupled receptor) ligands is a challenging task. In this study, we developed a computational protocol to find compounds with 5-HT2BR versus 5-HT1BR selectivity. Our approach employs the hierarchical combination of machine learning methods, docking, and multiple scoring methods. First, we applied machine learning tools to filter a large database of druglike compounds by the new Neighbouring Substructures Fingerprint (NSFP). This two-dimensional fingerprint contains information on the connectivity of the substructural features of a compound. Preselected subsets of the database were then subjected to docking calculations. The main indicators of compounds’ selectivity were their different interactions with the secondary binding pockets of both target proteins, while binding modes within the orthosteric binding pocket were preserved. The combined methodology of ligand-based and structure-based methods was validated prospectively, resulting in the identification of hits with nanomolar affinity and ten-fold to ten thousand-fold selectivitiesÁ.A.K. and G.M.K. were supported by the National Brain Research Program (2017-1.2.1-NKP-2017-00002). K.R. is grateful for the ETIUDA scholarship of the National Science Center, Poland. J.B. and M.I.L. are grateful for the support from the Spanish Ministerio de Economía y Comptetitividad (SAF2017-85225-C3-1-R)S
Identification of Novel Antimalarial Chemotypes via Chemoinformatic Compound Selection Methods for a High-Throughput Screening Program against the Novel Malarial Target, PfNDH2: Increasing Hit Rate via Virtual Screening Methods
Malaria is responsible for approximately 1 million deaths annually; thus, continued efforts to discover new antimalarials are required. A HTS screen was established to identify novel inhibitors of the parasite's mitochondrial enzyme NADH:quinone oxidoreductase (PfNDH2). On the basis of only one known inhibitor of this enzyme, the challenge was to discover novel inhibitors of PfNDH2 with diverse chemical scaffolds. To this end, using a range of ligand-based chemoinformatics methods, ~17000 compounds were selected from a commercial library of ~750000 compounds. Forty-eight compounds were identified with PfNDH2 enzyme inhibition IC(50) values ranging from 100 nM to 40 μM and also displayed exciting whole cell antimalarial activity. These novel inhibitors were identified through sampling 16% of the available chemical space, while only screening 2% of the library. This study confirms the added value of using multiple ligand-based chemoinformatic approaches and has successfully identified novel distinct chemotypes primed for development as new agents against malaria
Computational approaches to virtual screening in human central nervous system therapeutic targets
In the past several years of drug design, advanced high-throughput synthetic and analytical chemical technologies are continuously producing a large number of compounds. These large collections of chemical structures have resulted in many public and commercial molecular databases. Thus, the availability of larger data sets provided the opportunity for developing new knowledge mining or virtual screening (VS) methods. Therefore, this research work is motivated by the fact that one of the main interests in the modern drug discovery process is the development of new methods to predict compounds with large therapeutic profiles (multi-targeting activity), which is essential for the discovery of novel drug candidates against complex multifactorial diseases like central nervous system (CNS) disorders. This work aims to advance VS approaches by providing a deeper understanding of the relationship between chemical structure and pharmacological properties and design new fast and robust tools for drug designing against different targets/pathways.
To accomplish the defined goals, the first challenge is dealing with big data set of diverse molecular structures to derive a correlation between structures and activity. However, an extendable and a customizable fully automated in-silico Quantitative-Structure Activity Relationship (QSAR) modeling framework was developed in the first phase of this work. QSAR models are computationally fast and powerful tool to screen huge databases of compounds to determine the biological properties of chemical molecules based on their chemical structure. The generated framework reliably implemented a full QSAR modeling pipeline from data preparation to model building and validation. The main distinctive features of the designed framework include a)efficient data curation b) prior estimation of data modelability and, c)an-optimized variable selection methodology that was able to identify the most biologically relevant features responsible for compound activity. Since the underlying principle in QSAR modeling is the assumption that the structures of molecules are mainly responsible for their pharmacological activity, the accuracy of different structural representation approaches to decode molecular structural information largely influence model predictability. However, to find the best approach in QSAR modeling, a comparative analysis of two main categories of molecular representations that included descriptor-based (vector space) and distance-based (metric space) methods was carried out. Results obtained from five QSAR data sets showed that distance-based method was superior to capture the more relevant structural elements for the accurate characterization of molecular properties in highly diverse data sets (remote chemical space regions). This finding further assisted to the development of a novel tool for molecular space visualization to increase the understanding of structure-activity relationships (SAR) in drug discovery projects by exploring the diversity of large heterogeneous chemical data. In the proposed visual approach, four nonlinear DR methods were tested to represent molecules lower dimensionality (2D projected space) on which a non-parametric 2D kernel density estimation (KDE) was applied to map the most likely activity regions (activity surfaces). The analysis of the produced probabilistic surface of molecular activities (PSMAs) from the four datasets showed that these maps have both descriptive and predictive power, thus can be used as a spatial classification model, a tool to perform VS using only structural similarity of molecules.
The above QSAR modeling approach was complemented with molecular docking, an approach that predicts the best mode of drug-target interaction. Both approaches were integrated to develop a rational and re-usable polypharmacology-based VS pipeline with improved hits identification rate. For the validation of the developed pipeline, a dual-targeting drug designing model against Parkinson’s disease (PD) was derived to identify novel inhibitors for improving the motor functions of PD patients by enhancing the bioavailability of dopamine and avoiding neurotoxicity. The proposed approach can easily be extended to more complex multi-targeting disease models containing several targets and anti/offtargets to achieve increased efficacy and reduced toxicity in multifactorial diseases like CNS disorders and cancer.
This thesis addresses several issues of cheminformatics methods (e.g., molecular structures representation, machine learning, and molecular similarity analysis) to improve and design new computational approaches used in chemical data mining. Moreover, an integrative drug-designing pipeline is designed to improve polypharmacology-based VS approach. This presented methodology can identify the most promising multi-targeting candidates for experimental validation of drug-targets network at the systems biology level in the drug discovery process
Computational Ligand-Based CNS Therapeutic Design: The Search for Novel-Scaffold Norepinephrine Transporter Inhibitors
Monoamine transporter (MAT) proteins are responsible for regulating cellular signal transduction through control of neurotransmitter reuptake in the synapse, and are therefore relevant to diseases including addiction, psychosis, anxiety and depression. MATs, specifically the serotonin transporter (SERT or 5-HTT), norepinephrine transporter (NET), and dopamine transporter (DAT), serve as the principal targets for antidepressant drugs, such as SSRIs (selective serotonin reuptake inhibitors), NRIs (norepinephrine reuptake inhibitors) and TCAs (tricyclic antidepressants), as well as psychostimulant drugs of abuse such as cocaine and the amphetamines. Due to a lack of crystallographic MAT data, it is unclear as to which of two MAT protein ligand binding sites these drugs bind, hindering knowledge of the specific binding modes of MAT ligands. In this study an in silico pharmacophore model was created using a ligand-based method aimed at drug screening for the ability to specifically inhibit NET, using Molecular Operating Environment software. A group of four structurally-diverse compounds with high NET binding affinities comprised the training set used to generate the model. A test set, which included ten compounds with a range of known NET affinities, served in the validation of the model. The constructed pharmacophore model selected all high affinity NET inhibitors and one relatively inactive compound from the test set. Following model validation, the ZINC small molecule structural database was virtually screened to identify novel MAT inhibitor candidates. Hit compounds were ranked by an overlay score, which calculated how well novel compounds aligned to the original training set alignment. Six top-ranking compounds were purchased and evaluated via in vitro pharmacology to determine the binding affinity at the MATs. Although no significant inhibition was observed at the MATs, compound AC-1 showed a 15% inhibition at the DAT in radioligand binding assays. This result suggests that with further refinement of key pharmacophore features or alteration of the AC-1 structure, more potent MAT inhibitors could be discovered. Pharmacophore-based drug design has become one of the most important tools in drug discovery. Using the molecular modeling approaches described in this study, it is possible to rationally design novel and more selective central nervous system drugs
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