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

Deep learning optimization for drug-target interaction prediction in COVID-19 using graphic processing unit

The exponentially increasing bioinformatics data raised a new problem: the computation time length. The amount of data that needs to be processed is not matched by an increase in hardware performance, so it burdens researchers on computation time, especially on drug-target interaction prediction, where the computational complexity is exponential. One of the focuses of high-performance computing research is the utilization of the graphics processing unit (GPU) to perform multiple computations in parallel. This study aims to see how well the GPU performs when used for deep learning problems to predict drug-target interactions. This study used the gold-standard data in drug-target interaction (DTI) and the coronavirus disease (COVID-19) dataset. The stages of this research are data acquisition, data preprocessing, model building, hyperparameter tuning, performance evaluation and COVID-19 dataset testing. The results of this study indicate that the use of GPU in deep learning models can speed up the training process by 100 times. In addition, the hyperparameter tuning process is also greatly helped by the presence of the GPU because it can make the process up to 55 times faster. When tested using the COVID-19 dataset, the model showed good performance with 76% accuracy, 74% F-measure and a speed-up value of 179

Computational Modeling of (De)-Solvation Effects and Protein Flexibility in Protein-Ligand Binding using Molecular Dynamics Simulations

Water is a crucial participant in virtually all cellular functions. Evidently, water molecules in the binding site contribute significantly to the strength of intermolecular interactions in the aqueous phase by mediating protein-ligand interactions, solvating and de-solvating both ligand and protein upon protein-ligand dissociation and association. Recently many published studies use water distributions in the binding site to retrospectively explain and rationalize unexpected trends in structure-activity relationships (SAR). However, traditional approaches cannot quantitatively predict the thermodynamic properties of water molecules in the binding sites and its associated contribution to the binding free energy of a ligand. We have developed and validated a computational method named WATsite to exploit high-resolution solvation maps and thermodynamic profiles to elucidate the water molecules’ potential contribution to protein-ligand and protein-protein binding. We have also demonstrated the utility of the computational method WATsite to help direct medicinal chemistry efforts by using explicit water de-solvation. In addition, protein conformational change is typically involved in the ligand-binding process which may completely change the position and thermodynamic properties of the water molecules in the binding site before or upon ligand binding. We have shown the interplay between protein flexibility and solvent reorganization, and we provide a quantitative estimation of the influence of protein flexibility on desolvation free energy and, therefore, protein-ligand binding. Different ligands binding to the same target protein can induce different conformational adaptations. In order to apply WATsite to an ensemble of different protein conformations, a more efficient implementation of WATsite based on GPU-acceleration and system truncation has been developed. Lastly, by extending the simulation protocol from pure water to mixed water-organic probes simulations, accurate modeling of halogen atom-protein interactions has been achieved

Potential Inhibitors for Novel Coronavirus Protease Identified by Virtual Screening of 606 Million Compounds

The rapid outbreak of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China followed by its spread around the world poses a serious global concern for public health. To this date, no specific drugs or vaccines are available to treat SARS-CoV-2 despite its close relation to the SARS-CoV virus that caused a similar epidemic in 2003. Thus, there remains an urgent need for the identification and development of specific antiviral therapeutics against SARS-CoV-2. To conquer viral infections, the inhibition of proteases essential for proteolytic processing of viral polyproteins is a conventional therapeutic strategy. In order to find novel inhibitors, we computationally screened a compound library of over 606 million compounds for binding at the recently solved crystal structure of the main protease (M; pro; ) of SARS-CoV-2. A screening of such a vast chemical space for SARS-CoV-2 M; pro; inhibitors has not been reported before. After shape screening, two docking protocols were applied followed by the determination of molecular descriptors relevant for pharmacokinetics to narrow down the number of initial hits. Next, molecular dynamics simulations were conducted to validate the stability of docked binding modes and comprehensively quantify ligand binding energies. After evaluation of potential off-target binding, we report a list of 12 purchasable compounds, with binding affinity to the target protease that is predicted to be more favorable than that of the cocrystallized peptidomimetic compound. In order to quickly advise ongoing therapeutic intervention for patients, we evaluated approved antiviral drugs and other protease inhibitors to provide a list of nine compounds for drug repurposing. Furthermore, we identified the natural compounds (-)-taxifolin and rhamnetin as potential inhibitors of M; pro; . Rhamnetin is already commercially available in pharmacies

Discovery of new selective human aldose reductase inhibitors through virtual screening multiple binding pocket conformations

Aldose reductase reduces glucose to sorbitol. It plays a key role in many of the complications arising from diabetes. Thus, aldose reductase inhibitors (ARI) have been identified as promising therapeutic agents for treating such complications of diabetes, as neuropathy, nephropathy, retinopathy, and cataracts. In this paper, a virtual screening protocol applied to a library of compounds in house has been utilized to discover novel ARIs. IC50's were determined for 15 hits that inhibited ALR2 to greater than 50% at 50 uM, and ten of these have an IC50 of 10 uM or less, corresponding to a rather substantial hit rate of 14% at this level. The specificity of these compounds relative to their cross-reactivity with human ALR1 was also assessed by inhibition assays. This resulted in identification of novel inhibitors with IC50's comparable to the commercially available drug, epalrestat, and greater than an order of magnitude better selectivity

効率的で安全な集合間類似結合に関する研究

筑波大学 (University of Tsukuba)201

PKD1 3D Structure Model and Docking Studies for New PKD Inhibitors

Protein kinase Ds (PKDs) are diacylglycerol (DAG)-regulated serine/threonine protein kinases. In intact cells, PKDs are key mediators in cellular processes pertaining to multiple diseases, including cancer, heart diseases, angiogenesis and immune dysfunctions. A number of the novel, potent, and structurally diverse ATP-competitive PKD inhibitors have been reported to selectively modulate the PKD activity and thus, to achieve a potential therapeutic effect on related diseases. Due to a lack of the crystal structure, we have constructed a 3D structure of the human PKD1 protein by using homology modeling. Then, by using our established protein docking protocol, we docked novel PKD inhibitory small molecules and found the hit compounds exhibiting higher binding scores with reasonable binding mode in comparison with the reported active PKD1 inhibitors. Also, we calculated both 2D and 3D molecular similarity between our identified compounds and previously reported PKD1 inhibitors. Moreover, we predicted the possible off-targets of our compounds and our prediction has been validated through a topomer similarity study. In this study, we demonstrated that computational tools, i.e., docking and molecular similarity calculation can be applied to explore the PKD1/inhibitor interactions. In addition, the docking studies and the detailed docking poses provide insight for better understanding of the possible mechanism of a bioactive PKD1 inhibitor in order to guide future optimization for new drug design and discovery

Implementation of relativistic coupled cluster theory for massively parallel GPU-accelerated computing architectures

In this paper, we report a reimplementation of the core algorithms of relativistic coupled cluster theory aimed at modern heterogeneous high-performance computational infrastructures. The code is designed for efficient parallel execution on many compute nodes with optional GPU coprocessing, accomplished via the new ExaTENSOR back end. The resulting ExaCorr module is primarily intended for calculations of molecules with one or more heavy elements, as relativistic effects on electronic structure are included from the outset. In the current work, we thereby focus on exact 2-component methods and demonstrate the accuracy and performance of the software. The module can be used as a stand-alone program requiring a set of molecular orbital coefficients as starting point, but is also interfaced to the DIRAC program that can be used to generate these. We therefore also briefly discuss an improvement of the parallel computing aspects of the relativistic self-consistent field algorithm of the DIRAC program