Machine learning (ML) methods have been applied to the analysis of a range of biological systems. This thesis evaluates the application of these methods to the problem domain of skin permeability. ML methods offer great potential in both predictive ability and their ability to provide mechanistic insight to, in this case, the phenomena of skin permeation. Historically, refining mathematical models used to predict percutaneous drug absorption has been thought of as a key factor in this field. Quantitative Structure-Activity Relationships (QSARs) models are used extensively for this purpose. However, advanced ML methods successfully outperform the traditional linear QSAR models. In this thesis, the application of ML methods to percutaneous absorption are investigated and evaluated. The major approach used in this thesis is Gaussian process (GP) regression method. This research seeks to enhance the prediction performance by using local non-linear models obtained from applying clustering algorithms. In addition, to increase the model's quality, a kernel is generated based on both numerical chemical variables and categorical experimental descriptors. Monte Carlo algorithm is also employed to generate reliable models from variable data which is inevitable in biological experiments. The datasets used for this study are small and it may raise the over-fitting/under-fitting problem. In this research I attempt to find optimal values of skin permeability using GP optimisation algorithms within small datasets. Although these methods are applied here to the field of percutaneous absorption, it may be applied more broadly to any biological system.

Computational drug design methods have great potential in drug discovery particularly in lead identification and lead optimization. 3-Phosphoinositide dependent kinase-1 (PDK1) is a protein kinase and a well validated anti-cancer target. Inhibitors of PDK1 have the potential to be developed as anti-cancer drugs. In this work, we have applied various novel computational drug design strategies to design and identify new PDK1 inhibitors with potential anti-cancer activity. We have pursued novel structure-based drug design strategies and identified a new binding mode for celecoxib and its derivatives binding with PDK1. This new binding mode provides a valuable basis for rational design of potent PDK1 inhibitors. In order to understand the structure-activity relationship of indolinone-based PDK1 inhibitors, we have carried out a combined molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling study. The predictive ability of the developed 3D-QSAR models were validated using an external test set of compounds. An efficient strategy of the hierarchical virtual screening with increasing complexity was pursued to identify new hits against PDK1. Our approach uses a combination of ligand-based and structure-based virtual screening including shape-based filtering, rigid docking, and flexible docking. In addition, a more sophisticated molecular dynamics/molecular mechanics- Poisson-Boltzmann surface area (MD/MM-PBSA) analysis was used as the final filter in the virtual screening. Our screening strategy has led to the identification of a new PDK1 inhibitor. The anticancer activities of this compound have been confirmed by the anticancer activity assays of national cancer institute-developmental therapeutics program (NCI-DTP) using 60 cancer cell lines. The PDK1-inhibitor binding mode determined in this study may be valuable in future de novo drug design. The virtual screening approach tested and used in this study could also be applied to lead identification in other drug discovery efforts.

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The BioChemical Library (BCL) cheminformatics toolkit is an application-based academic open-source software package designed to integrate traditional small molecule cheminformatics tools with machine learning-based quantitative structure-activity/ property relationship (QSAR/QSPR) modeling. In this pedagogical article we provide a detailed introduction to core BCL cheminformatics functionality, showing how traditional tasks (e.g., computing chemical properties, estimating druglikeness) can be readily combined with machine learning. In addition, we have included multiple examples covering areas of advanced use, such as reaction-based library design. We anticipate that this manuscript will be a valuable resource for researchers in computer-aided drug discovery looking to integrate modular cheminformatics and machine learning tools into their pipelines.

Over the course of more than ten years, there has been a significant increase in the approach employed to inhibit the function of soluble epoxide hydrolase (sEH). The phenomenon of upregulating soluble epoxide hydrolase (sEH) has been found to result in a decrease in the ratio of epoxyeicosatrienoic acids (EETs) to dihydroeicosatrienoic acids (DHETs) in the body. This has garnered significant attention due to the diverse biological functions attributed to EETs, including the regulation of vasodilation, neuroprotection, increased fibrinolysis, calcium ion influx, and anti-inflammatory effects. Consequently, there has been a growing interest in developing and discovering sEH inhibitors through chemical syntheses and natural extracts, with the aim of increasing the availability of these anti-inflammatory molecules by reducing their hydrolysis. A comprehensive examination of this project was conducted to explore the inhibitory effects of YMSV, a tetrapeptide derived from the castor bean (Ricinus communis), on sEH, as well as to elucidate its underlying mechanism of action. YMSV was determined to function as a mixed-competitive inhibitor of soluble epoxide hydrolase (sEH), and the interaction between the peptide and the protein resulted in the disruption of the secondary structural composition of sEH. Furthermore, the hydrogen bond interactions between YMSV and the Asp 333 residue in the active region of soluble epoxide hydrolase (sEH) were demonstrated using molecular docking investigations. However, quantitative structure-activity relationship (QSAR) research revealed that nonpolar, hydrophobic, and bulky amino acids are favored at the N- and C- terminals of peptides for sEH inhibition. The results of this study indicate that peptides obtained from dietary sources possess unique characteristics as inhibitors of soluble epoxide hydrolase (sEH), displaying significant potency. Consequently, these peptides have promise for further development as therapeutic medicines targeting inflammation and depression in the future.

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