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The discovery of selective inhibitors of biological target proteins is the primary goal of many drug discovery campaigns. However, this goal has proven elusive, especially for inhibitors targeting the well-conserved orthosteric adenosine triphosphate (ATP) binding pocket of kinase enzymes. The human kinome is large and it is rather difficult to profile early lead compounds against around 500 targets to gain an upfront knowledge on selectivity. Further, selectivity can change drastically during derivatization of an initial lead compound. Here, we have introduced a computational model to support the profiling of compounds early in the drug discovery pipeline. On the basis of the extensive profiled activity of 70 kinase inhibitors against 379 kinases, including 81 tyrosine kinases, we developed a quantitative structure–activity relation (QSAR) model using artificial neural networks, to predict the activity of these kinase inhibitors against the panel of 379 kinases. The model’s performance in predicting activity ranges from 0.6 to 0.8 depending on the kinase, from the area under the curve (AUC) of the receiver operating characteristics (ROC). The profiler is available online at http://www.meilerlab.org/index.php/servers/show?s_id=23.

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.

Diabetes mellitus is a metabolic syndrome characterized by an increase in the blood glucose level. Treatment of diabetes is complicated due to multifactorial nature of the disease. Azadirachta indica Adr. Juss and Bougainvillea spectabilis are reported to have medicinal values including antidiabetic properties. In the present study using in vivo diabetic murine model, A. indica and B. spectabilis chloroform, methanolic and aqueous extracts were investigated for the biochemical parameters important for controlling diabetes. It was found that A. indica chloroform extract and B. spectabilis aqueous, methanolic extracts showed a good oral glucose tolerance and significantly reduced the intestinal glucosidase activity. Interestingly, A. indica chloroform and B. spectabilis aqueous extracts showed significant increase in glucose-6-phosphate dehydrogenase activity and hepatic, skeletal muscle glycogen content after 21 days of treatment. In immunohistochemical analysis, we observed a regeneration of insulin-producing cells and corresponding increase in the plasma insulin and c-peptide levels with the treatment of A. indica chloroform and B. spectabilis aqueous, methanolic extracts. Analyzing the results, it is clear that A. indica chloroform and B. spectabilis aqueous extracts are good candidates for developing new neutraceuticals treatment for diabetes.

The interaction of a small molecule with a protein target depends on its ability to adopt a three-dimensional structure that is complementary. Therefore, complete and rapid prediction of the conformational space a small molecule can sample is critical for both structure- and ligand-based drug discovery algorithms such as small molecule docking or three-dimensional quantitative structure–activity relationships. Here we have derived a database of small molecule fragments frequently sampled in experimental structures within the Cambridge Structure Database and the Protein Data Bank. Likely conformations of these fragments are stored as ‘rotamers’ in analogy to amino acid side chain rotamer libraries used for rapid sampling of protein conformational space. Explicit fragments take into account correlations between multiple torsion bonds and effect of substituents on torsional profiles. A conformational ensemble for small molecules can then be generated by recombining fragment rotamers with a Monte Carlo search strategy. BCL::C onf was benchmarked against other conformer generator methods including C onfgen , M oe , O mega and RDK it in its ability to recover experimentally determined protein bound conformations of small molecules, diversity of conformational ensembles, and sampling rate. BCL::C onf recovers at least one conformation with a root mean square deviation of 2 Å or better to the experimental structure for 99 % of the small molecules in the V ernalis benchmark dataset. The ‘rotamer’ approach will allow integration of BCL::C onf into respective computational biology programs such as R osetta . Graphical abstract: Conformation sampling is carried out using explicit fragment conformations derived from crystallographic structure databases. Molecules from the database are decomposed into fragments and most likely conformations/rotamers are used to sample correspondng sub-structure of a molecule of interest.

Building on the role of human intuition in small molecule drug design, we explored whether crowdsourcing could recruit citizen scientists to this task while in parallel building awareness for this scientific process. Here, we introduce Drugit ( https://drugit.org ), the small molecule design mode of the online citizen science game Foldit. We demonstrate its utility by identifying distinct binders to the von Hippel Lindau E3 ligase. Several thousand molecules were suggested by players in a series of ten puzzle rounds. The proposed molecules were further evaluated in silico and manually by an expert panel. Selected candidates were synthesized and tested. One of these molecules shows dose-dependent shift perturbations in protein-observed NMR experiments. The co-crystal structure in complex with the E3 ligase reveals that the observed binding mode matches the player’s original idea. The completion of one full design cycle is a proof of concept for the Drugit approach and highlights the potential of involving citizen scientists in early drug discovery. Citizen science taps the efforts of non-experts. Here, authors describe Drugit, an extension of the crowdsourcing game Foldit, and its use in designing a non-peptide binder of Von Hippel Lindau E3 ligase for use with proteolysis targeting chimeras.

Lower gastrointestinal bleeding (LGIB), which manifests as blood in the colon or anorectum, is a common reason for hospitalization. In most patients, LGIB stops spontaneously with no in-hospital intervention. A risk score that could identify patients at low risk of experiencing adverse outcomes could help improve the triage process and allow greater numbers of patients to receive outpatient management of LGIB. To externally validate the Oakland Score, which was previously developed using a score threshold of 8 points to identify patients with LGIB who are at low risk of adverse outcomes. This multicenter prognostic study was conducted in 140 US hospitals in the Hospital Corporation of America network. A total of 46 179 adult patients (aged ≥16 years) admitted to the hospital with a primary diagnosis of LGIB between June 1, 2016, and October 15, 2018, were initially identified using diagnostic codes. Of those, 51 patients were excluded because they were more likely to have upper gastrointestinal bleeding, leaving a study population of 46 128 patients with LGIB. For the statistical analysis of the Oakland Score, an additional 8061 patients were excluded because they were missing data on Oakland Score components or clinical outcomes, resulting in 38 067 patients included in the analysis. The study used area under the receiver operating characteristic curves with 95% CIs for external validation of the model. Sensitivity and specificity were calculated for each score threshold (≤8 points, ≤9 points, and ≤10 points). Data were analyzed from October 16, 2018, to September 4, 2019. Identification of patients who met the criteria for safe discharge from the hospital and comparison of the performance of 2 score thresholds (≤8 points vs ≤10 points). Safe discharge was defined as the absence of blood transfusion, rebleeding, hemostatic intervention, hospital readmission, and death. Among 46 128 adult patients with LGIB, the mean (SD) age was 70.1 (16.5) years; 23 091 patients (50.1%) were female. Of those, 22 074 patients (47.9%) met the criteria for safe discharge from the hospital. In this group, the mean (SD) age was 67.9 (18.1) years, and 11 056 patients (50.1%) were female. In the statistical analysis of the Oakland Score, which included only the 38 067 patients with complete data, the area under the receiver operating characteristic curve for safe discharge was 0.87 (95% CI, 0.87-0.87). An Oakland Score threshold of 8 points or lower identified 3305 patients (8.7%), with a sensitivity and specificity for safe discharge of 98.4% and 16.0%, respectively. Extension of the Oakland Score threshold to 10 points or lower identified 6770 patients (17.8%), with a sensitivity and specificity for safe discharge of 96.0% and 31.9%, respectively. In this study, the Oakland Score consistently identified patients with acute LGIB who were at low risk of experiencing adverse outcomes and whose conditions could safely be managed without hospitalization. The score threshold to identify low-risk patients could be extended from 8 points or lower to 10 points or lower to allow identification of a greater proportion of low-risk patients.

Computational approaches have become important tools in drug discovery. Drug discovery is a lengthy process that begins with target identification, lead compound discovery, and lead compound optimization, followed by preclinical studies. Computational tools have been developed which complement the experimental drug discovery efforts at each of these steps. Target discovery is often achieved by phenotypic screens using micro-array analysis. This is achieved computationally through bioinformatics analysis of gene expression data and protein-protein interaction networks. Often experimental screening for lead compounds is preceded by computational screening of hundreds of thousands of compound. Computational virtual-high throughput compound screening technologies are used to prioritize molecules for experimental testing and are estimated to increase the chance of finding a lead molecule by about ten times. Computational prescreening saves time, resources, and efforts required for experimental screening by reducing the number of compounds to be tested. The goal of lead compound optimization is to improve its potency against the target of interest. This is achieved by medicinal chemistry approaches through synthesis of a number of derivatives. Computational modelling of target-ligand interactions is often used to direct the medicinal chemistry studies. Results from experimental optimization can also be used to create computational models to predict the pharmacophore of the lead compound. Lead optimization can be further aided by computational models that predict drug-likeness and toxicity, saving substantial efforts in the downstream preclinical studies. Development of novel computational technologies for drug discovery has been the primary focus of my PhD thesis. A novel knowledge based conformation sampling algorithm was implemented which derives information from structural databases. Molecular conformation sampling is ubiquitous and critical in computational drug discovery technologies. The new algorithm performs better than other conformation algorithm currently available in the field and has already been incorporated into a major macromolecular modelling software. Another focus of my work has been the application of computational technologies for discovery of novel and selective binders of the kinase domain of Discoidin Domain Receptor (DDR1). At least one novel chemical scaffold was discovered and confirmed as a DDR1 inhibitor through these efforts. Computer aided-drug discovery is split into domains that focus on either structure-based or ligand-based techniques. Structure-based approaches are feasible when the structure of the biological target protein or its homologues is available. These techniques include ligand docking/design and structure-based pharmacophore maps. In the absence of a structural model, ligand-based approaches provide an alternative way of identifying new active molecules and optimizing their activity. Ligand approaches leverage quantitative structure activity relationship (QSAR) models and pharmacophore maps. A comprehensive review of successful applications of computational technologies in drug discovery processes is provided in the first chapter of this thesis. It is an abridged version of the review article: Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E. W., Jr., Computational methods in drug discovery. Pharmacol Rev 2014, 66 (1), 334-95. Reprinted with permission of the American Society for Pharmacology and Experimental Therapeutics. All rights reserved. Molecules are comprised of one or more atoms connected by bonds, many of which are rotatable. This rotation about the bonds allows molecules to adopt distinct orientations, known as conformations, in 3-dimension space. In solution, a given molecule can exist in multiple different low-energy conformations. A small molecule may bind to its protein target in one of the conformations favored in solution, or alternatively one that is induced by the interactions with the target protein of interest. Computational modeling for binding prediction, whether structure-based or ligand-based, thus needs to take into account small molecule flexibility. The success of structure-based drug discovery technologies depends significantly on the availability of high-quality ligand conformations that are necessary to accurately model interactions between the target and the ligand molecules. Ligand conformations are also important for ligand-based methods, for example, to align multiple active molecules for shape matching and developing pharmacophore models. Several conformation sampling methods exist that use either physics-based approaches i.e. molecular mechanics or pre-existing knowledge about small molecular conformations. The primary focus of this doctoral thesis is the development of a high-quality conformation generation algorithm that uses extended fragment conformation information. This algorithm, called BCL::CONF, is described in Chapter 2 of this thesis and is an adaptation of manuscript: Kothiwale, S.; Mendenhall, J. L.; Meiler, J., BCL::CONF: small molecule conformational sampling using a knowledge based rotamer library. J Cheminform 2015, 7, 47. http://creativecommons.org/licenses/by/4.0/ (Abstract shortened by ProQuest.)