Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure-activity relationship-based artificial intelligence and molecular docking of hit compounds.

Dipeptidyl peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus; however, some classes of these drugs exert side effects, including joint pain and pancreatitis. Studies suggest that these side effects might be related to secondary inhibition of DPP-8 and DPP-9. In this study, we identified DPP-4-inhibitor hit compounds selective against DPP-8 and DPP-9. We built a virtual screening workflow using a quantitative structure-activity relationship (QSAR) strategy based on artificial intelligence to allow faster screening of millions of molecules for the DPP-4 target relative to other screening methods. Five regression machine learning algorithms and four classification machine learning algorithms were applied to build virtual screening workflows, with the QSAR model applied using support vector regression (R2pred 0.78) and the classification QSAR model using the random forest algorithm with 92.2% accuracy. Virtual screening results of > 10 million molecules obtained 2 716 hits compounds with a pIC50 value of > 7.5. Additionally, molecular docking results of several potential hit compounds for DPP-4, DPP-8, and DPP-9 identified CH0002 as showing high inhibitory potential against DPP-4 and low inhibitory potential for DPP-8 and DPP-9 enzymes. These results demonstrated the effectiveness of this technique for identifying DPP-4-inhibitor hit compounds selective for DPP-4 and against DPP-8 and DPP-9 and suggest its potential efficacy for applications to discover hit compounds of other targets.

In Silico Studies for Bacterystic Evaluation against Staphylococcus aureus of 2-Naphthoic Acid Analogues.

BACKGROUND: Staphylococcus aureus is a gram-positive spherical bacterium commonly present in nasal fossae and in the skin of healthy people; however, in high quantities, it can lead to complications that compromise health. The pathologies involved include simple infections, such as folliculitis, acne, and delay in the process of wound healing, as well as serious infections in the CNS, meninges, lung, heart, and other areas. AIM: This research aims to propose a series of molecules derived from 2-naphthoic acid as a bioactive in the fight against S. aureus bacteria through in silico studies using molecular modeling tools. METHODS: A virtual screening of analogues was done in consideration of the results that showed activity according to the prediction model performed in the KNIME Analytics Platform 3.6, violations of the Lipinski rule, absorption rate, cytotoxicity risks, energy of binder-receptor interaction through molecular docking, and the stability of the best profile ligands in the active site of the proteins used (PDB ID 4DXD and 4WVG). RESULTS: Seven of the 48 analogues analyzed showed promising results for bactericidal action against S. aureus. CONCLUSION: It is possible to conclude that ten of the 48 compounds derived from 2-naphthoic acid presented activity based on the prediction model generated, of which seven presented no toxicity and up to one violation to the Lipinski rule.

Discovery of potent p38α MAPK inhibitors through a funnel like workflow combining in silico screening and in vitro validation.

This work describes the rational discovery of novel chemotypes of p38α MAPK inhibitors using a funnel approach consisting of several computer-aided drug discovery methods and biological experiments. Among the identified hits, four compounds belonging to different chemical families showed IC50 values lower than 10 µM. In particular, the 1,4-benzodioxane derivative 5 turned out to be a potent and efficient p38α MAPK inhibitor having IC50 = 0.07 µM, and LEexp and LipE values of 0.38 and 4.8, respectively; noteworthy, the compound had also a promising kinase selectivity profile and the capability to suppress p38α MAPK effects in human immune cells. Overall, the collected findings highlight that the applied strategy has been successful in generating chemical novelty in the inhibitor kinase field, providing suitable chemical candidates for further inhibitor optimization.

CellProfiler and KNIME: Open-Source Tools for High-Content Screening.

High-content screening (HCS) has established itself in the world of the pharmaceutical industry as an essential tool for drug discovery and drug development. HCS is currently starting to enter the academic world and might become a widely used technology. Given the diversity of problems tackled in academic research, HCS could experience some profound changes in the future, mainly with more imaging modalities and smart microscopes being developed. One of the limitations in the establishment of HCS in academia is flexibility and cost. Flexibility is important to be able to adapt the HCS setup to accommodate the multiple different assays typical of academia. Many cost factors cannot be avoided, but the costs of the software packages necessary to analyze large datasets can be reduced by using open-source software. We present and discuss the open-source software CellProfiler for image analysis and KNIME for data analysis and data mining that provide software solutions, which increase flexibility and keep costs low.

MouseTox: An online toxicity assessment tool for small molecules through Enalos Cloud platform.

Advances in the drug discovery research substantially depend on in silico methods and techniques that capitalize on experimental data to enable the accurate property/activity assessment by employing a variety of computational techniques. These in silico tools can significantly reduce expensive and time consuming experimental procedures required and are strongly recommended to avoid animal testing, especially as far as toxicity evaluation and risk assessment is concerned. In this context, in the present work we aim to develop a predictive model for the cytotoxic effects of a wide range of compounds based solely on calculated molecular descriptors that account for their topological, geometric and structural characteristics. The developed model was fully validated and was released online via Enalos Cloud platform accessible through http://enalos.insilicotox.com/MouseTox/. This ready-to-use web service offers, through a user-friendly interface, free access to the model results and therefore can act as a toxicity prediction tool for the risk assessment of novel compounds, without any special requirements or prior programming skills.

Screening Chemicals for Receptor-Mediated Toxicological and Pharmacological Endpoints: Using Public Data to Build Screening Tools within a KNIME Workflow.

Assessing compounds for their pharmacological and toxicological properties is of great importance for industry and regulatory agencies. In this study an approach using open source software and open access databases to build screening tools for receptor-mediated effects is presented. The retinoic acid receptor (RAR), as a pharmacologically and toxicologically relevant target, was chosen for this study. RAR agonists are used in the treatment of a number of dermal conditions and specific types of cancer, such as acute promyelocytic leukemia. However, when administered chronically, there is strong evidence that RAR agonists cause hepatosteatosis and liver injury. After compiling information on ligand-protein-interactions, common substructures and physico-chemical properties of ligands were identified manually and coded into SMARTS strings. Based on these SMARTS strings and calculated physico-chemical features, a rule-based screening workflow was built within the KNIME platform. The workflow was evaluated on two datasets: one with RAR agonists exclusively and another large, chemically diverse dataset containing only a few RAR agonists. Possible modifications and applications of screening workflows, dependent on their purpose, are presented.