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

Graph kernel extensions and experiments with application to molecule classification, lead hopping and multiple targets

The discovery of drugs that can effectively treat disease and alleviate pain is one of the core challenges facing modern medicine. The tools and techniques of machine learning have perhaps the greatest potential to provide a fast and efficient route toward the fabrication of novel and effective drugs. In particular, modern structured kernel methods have been successfully applied to range of problem domains and have been recently adapted for graph structures making them directly applicable to pharmaceutical drug discovery. Specifically graph structures have a natural fit with molecular data, in that a graph consists of a set of nodes that represent atoms that are connected by bonds. In this thesis we use graph kernels that utilize three different graph representations: molecular, topological pharmacophore and reduced graphs. We introduce a set of novel graph kernels which are based on a measure of the number of finite walks within a graph. To calculate this measure we employ a dynamic programming framework which allows us to extend graph kernels so they can deal with non-tottering, softmatching and allows the inclusion of gaps. In addition we review several graph colouring methods and subsequently incorporate colour into our graph kernels models. These kernels are designed for molecule classification in general, although we show how they can be adapted to other areas in drug discovery. We conduct three sets of experiments and discuss how our augmented graph kernels are designed and adapted for these areas. First, we classify molecules based on their activity in comparison to a biological target. Second, we explore the related problem of lead hopping. Here one set of chemicals is used to predict another that is structurally dissimilar. We discuss the problems that arise due to the fact that some patterns are filtered from the dataset. By analyzing lead hopping we are able to go beyond the typical cross-validation approach and construct a dataset that more accurately reflect real-world tasks. Lastly, we explore methods of integrating information from multiple targets. We test our models as a multi-response problem and later introduce a new approach that employs Kernel Canonical Correlation Analysis (KCCA) to predict the best molecules for an unseen target. Overall, we show that graph kernels achieve good results in classification, lead hopping and multiple target experiments

The 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry

The 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry was held on 1–15 July 2021. The scope of this online conference was to gather experts that are well-known worldwide who are currently working in chemical sensor technologies and to provide an online forum for the presention and discussion of new results. Throughout this event, topics of interest included, but were not limited to, the following: electrochemical devices and sensors; optical chemical sensors; mass-sensitive sensors; materials for chemical sensing; nano- and micro-technologies for sensing; chemical assays and validation; chemical sensor applications; analytical methods; gas sensors and apparatuses; electronic noses; electronic tongues; microfluidic devices; lab-on-a-chip; single-molecule sensing; nanosensors; and medico-diagnostic testing