Database development and machine learning classification of medicinal chemicals and biomolecules

Ph.DDOCTOR OF PHILOSOPH

NOVEL ALGORITHMS AND TOOLS FOR LIGAND-BASED DRUG DESIGN

Computer-aided drug design (CADD) has become an indispensible component in modern drug discovery projects. The prediction of physicochemical properties and pharmacological properties of candidate compounds effectively increases the probability for drug candidates to pass latter phases of clinic trials. Ligand-based virtual screening exhibits advantages over structure-based drug design, in terms of its wide applicability and high computational efficiency. The established chemical repositories and reported bioassays form a gigantic knowledgebase to derive quantitative structure-activity relationship (QSAR) and structure-property relationship (QSPR). In addition, the rapid advance of machine learning techniques suggests new solutions for data-mining huge compound databases. In this thesis, a novel ligand classification algorithm, Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS), was reported for the prediction of diverse categorical pharmacological properties. LiCABEDS was successfully applied to model 5-HT1A ligand functionality, ligand selectivity of cannabinoid receptor subtypes, and blood-brain-barrier (BBB) passage. LiCABEDS was implemented and integrated with graphical user interface, data import/export, automated model training/ prediction, and project management. Besides, a non-linear ligand classifier was proposed, using a novel Topomer kernel function in support vector machine. With the emphasis on green high-performance computing, graphics processing units are alternative platforms for computationally expensive tasks. A novel GPU algorithm was designed and implemented in order to accelerate the calculation of chemical similarities with dense-format molecular fingerprints. Finally, a compound acquisition algorithm was reported to construct structurally diverse screening library in order to enhance hit rates in high-throughput screening

The Joint European Compound Library:boosting precompetitive research

The Joint European Compound Library (JECL) is a new high-throughput screening collection aimed at driving precompetitive drug discovery and target validation. The JECL has been established with a core of over 321000 compounds from the proprietary collections of seven pharmaceutical companies and will expand to around 500000 compounds. Here, we analyse the physicochemical profile and chemical diversity of the core collection, showing that the collection is diverse and has a broad spectrum of predicted biological activity. We also describe a model for sharing compound information from multiple proprietary collections, enabling diversity and quality analysis without disclosing structures. The JECL is available for screening at no cost to European academic laboratories and SMEs through the IMI European Lead Factory (http://www.europeanleadfactory.eu/)

Applications of Support Vector Machines as a Robust tool in High Throughput Virtual Screening

Chemical space is enormously huge but not all of it is pertinent for the drug designing. Virtual screening methods act as knowledge-based filters to discover the coveted novel lead molecules possessing desired pharmacological properties. Support Vector Machines (SVM) is a reliable virtual screening tool for prioritizing molecules with the required biological activity and minimum toxicity. It has to its credit inherent advantages such as support for noisy data mainly coming from varied high-throughput biological assays, high sensitivity, specificity, prediction accuracy and reduction in false positives. SVM-based classification methods can efficiently discriminate inhibitors from non-inhibitors, actives from inactives, toxic from non-toxic and promiscuous from non-promiscuous molecules. As the principles of drug design are also applicable for agrochemicals, SVM methods are being applied for virtual screening for pesticides too. The current review discusses the basic kernels and models used for binary discrimination and also features used for developing SVM-based scoring functions, which will enhance our understanding of molecular interactions. SVM modeling has also been compared by many researchers with other statistical methods such as Artificial Neural Networks, k-nearest neighbour (kNN), decision trees, partial least squares, etc. Such studies have also been discussed in this review. Moreover, a case study involving the use of SVM method for screening molecules for cancer therapy has been carried out and the preliminary results presented here indicate that the SVM is an excellent classifier for screening the molecules

QSAR METHODS DEVELOPMENT, VIRTUAL AND EXPERIMENTAL SCREENING FOR CANNABINOID LIGAND DISCOVERY

G protein coupled receptors (GPCRs) are the largest receptor family in mammalian genomes and are known to regulate wide variety of signals such as ions, hormones and neurotransmitters. It has been estimated that GPCRs represent more than 30% of current drug targets and have attracted many pharmaceutical industries as well as academic groups for potential drug discovery. Cannabinoid (CB) receptors, members of GPCR superfamily, are also involved in the activation of multiple intracellular signal transductions and their endogenous ligands or cannabinoids have attracted pharmacological research because of their potential therapeutic effects. In particular, the cannabinoid subtype-2 (CB2) receptor is known to be involved in immune system signal transductions and its ligands have the potential to be developed as drugs to treat many immune system disorders without potential psychotic side-effects. Therefore, this work was focused on discovering novel CB2 ligands by developing novel quantitative structure-activity relationship (QSAR) methods and performing virtual and experimental screenings. Three novel QSAR methods were developed to predict biological activities and binding affinities of ligands. In the first method, a traditional fragment-based approach was improved by introducing a fragment similarity concept that enhanced the prediction accuracy remarkably. In the second method, pharmacophoric and morphological descriptors were incorporated to derive a novel QSAR regression model with good prediction accuracy. In the third method, a novel fingerprint-based artificial neural network QSAR model was developed to overcome the similar scaffold requirement of many fragment-based and other 3D-QSAR methods. These methods provide a foundation for virtual screening and hit ranking of chemical ligands from large chemical space. In addition, several novel CB2 selective ligands within nM binding affinities were discovered. These ligands were proven to be inverse agonists as validated by functional assays and could be useful probes to study CB2 signaling as well as potential drug candidates for autoimmune disesases

A Systems Biology Approach Reveals a Calcium-Dependent Mechanism for Basal Toxicity in Daphnia magna.

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Environmental Science & Technology, copyright © American Chemical Society after peer review and technical editing by the publisher.The expanding diversity and ever increasing amounts of man-made chemicals discharged to the environment pose largely unknown hazards to ecosystem and human health. The concept of adverse outcome pathways (AOPs) emerged as a comprehensive framework for risk assessment. However, the limited mechanistic information available for most chemicals and a lack of biological pathway annotation in many species represent significant challenges to effective implementation of this approach. Here, a systems level, multistep modeling strategy demonstrates how to integrate information on chemical structure with mechanistic insight from genomic studies, and phenotypic effects to define a putative adverse outcome pathway. Results indicated that transcriptional changes indicative of intracellular calcium mobilization were significantly overrepresented in Daphnia magna (DM) exposed to sublethal doses of presumed narcotic chemicals with log Kow ≥ 1.8. Treatment of DM with a calcium ATPase pump inhibitor substantially recapitulated the common transcriptional changes. We hypothesize that calcium mobilization is a potential key molecular initiating event in DM basal (narcosis) toxicity. Heart beat rate analysis and metabolome analysis indicated sublethal effects consistent with perturbations of calcium preceding overt acute toxicity. Together, the results indicate that altered calcium homeostasis may be a key early event in basal toxicity or narcosis induced by lipophilic compounds

Developing and validating predictive decision tree models from mining chemical structural fingerprints and high–throughput screening data in PubChem

<p>Abstract</p> <p>Background</p> <p>Recent advances in high-throughput screening (HTS) techniques and readily available compound libraries generated using combinatorial chemistry or derived from natural products enable the testing of millions of compounds in a matter of days. Due to the amount of information produced by HTS assays, it is a very challenging task to mine the HTS data for potential interest in drug development research. Computational approaches for the analysis of HTS results face great challenges due to the large quantity of information and significant amounts of erroneous data produced.</p> <p>Results</p> <p>In this study, Decision Trees (DT) based models were developed to discriminate compound bioactivities by using their chemical structure fingerprints provided in the PubChem system <url>http://pubchem.ncbi.nlm.nih.gov</url>. The DT models were examined for filtering biological activity data contained in four assays deposited in the PubChem Bioassay Database including assays tested for 5HT1a agonists, antagonists, and HIV-1 RT-RNase H inhibitors. The 10-fold Cross Validation (CV) sensitivity, specificity and Matthews Correlation Coefficient (MCC) for the models are 57.2~80.5%, 97.3~99.0%, 0.4~0.5 respectively. A further evaluation was also performed for DT models built for two independent bioassays, where inhibitors for the same HIV RNase target were screened using different compound libraries, this experiment yields enrichment factor of 4.4 and 9.7.</p> <p>Conclusion</p> <p>Our results suggest that the designed DT models can be used as a virtual screening technique as well as a complement to traditional approaches for hits selection.</p

Investigation of BCRP-inhibitors using QSAR and machine learning methods

BCRP is the second member of the subfamily G of the ABC transporters. BCRP is involved in several physiological functions, including protection of the human body from xenobiotics. The overexpression of this membrane protein in certain tumor cell lines leads to cross-resistance against various chemotherapeutic drugs. In this work, the inhibitory activity of several compounds against BCRP and P-gp were assayed using a Hoechst 33342 assay for BCRP and a Calcein AM assay for P-gp. Furthermore, the potency of the studied compounds has been rationalized using a classical QSAR approach. Finally, three machine learning algorithms (Self-Organizing Maps, Support Vector Machine and k-Nearest Neighbors) were used, in order to generate a global model useful to predict if small ligands could be (or not) BCRP-inhibitors

Recent Applications of Quantitative Structure-Activity Relationships in Drug Design

One of the most important challenges that face medicinal chemists today is the design of new drugs with improved properties and diminished side-effects for treating human disease such as AIDS and others. Medicinal chemists began the process by taking a lead structure and then finding analogs exhibiting the preferred biological activities. Next, they used their experience and chemical insight to eventually choose a nominee analog for further development. This process is difficult, expensive and took a long time. The conventional methods of drug discovery are now being supplemented by shortest approaches made possible by the accepting of the molecular processes involved in the original disease. In this view, the preliminary point in drug design is the molecular target which is receptor or enzyme in the body as an option of the existence of known lead structure