Visualization of Molecular Selectivity and Structure Generation for Selective Dopamine Inhibitors

Astragali Radix Isoflavones Synergistically Alleviate Cerebral Ischemia and Reperfusion Injury Via Activating Estrogen Receptor-PI3K-Akt Signaling Pathway

Isoflavones are major neuroprotective components of a medicinal herb Astragali Radix, against cerebral ischemia-reperfusion injury but the mechanisms of neuroprotection remain unclear. Calycosin and formononetin are two major AR isoflavones while daidzein is the metabolite of formononetin after absorption. Herein, we aim to investigate the synergistic neuroprotective effects of those isoflavones of Astragali Radix against cerebral ischemia-reperfusion injury. Calycosin, formononetin and daidzein were organized with different combinations whose effects observed in both in vitro and in vivo experimental models. In the in vitro study, primary cultured neurons were subjected to oxygen-glucose deprivation plus reoxygenation (OGD/RO) or l-glutamate treatment. In the in vivo study, rats were subjected to middle cerebral artery occlusion to induce cerebral ischemia and reperfusion. All three isoflavones pre-treatment alone decreased brain infarct volume and improved neurological deficits in rats, and dose-dependently attenuated neural death induced by l-glutamate treatment and OGD/RO in cultured neurons. Interestingly, the combined formulas of those isoflavones revealed synergistically activated estrogen receptor (estrogen receptors)-PI3K-Akt signaling pathway. Using ER antagonist and phosphatidylinositol 3-kinase (PI3K) inhibitor blocked the neuroprotective effects of those isoflavones. In conclusion, isoflavones could synergistically alleviate cerebral ischemia-reperfusion injury via activating ER-PI3K-Akt pathway.

e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

Multistep Reaction Based De Novo Drug Design: Generating Synthetically Feasible Design Ideas

We describe a "multistep reaction driven" evolutionary algorithm approach to de novo molecular design. Structures generated by the approach include a proposed synthesis path intended to aid the chemist in assessing the synthetic feasibility of the ideas that are generated. The methodology is independent of how the design ideas are scored, allowing multicriteria drug design to address multiple issues including activity at one or more pharmacological targets, selectivity, physical and ADME properties, and off target liabilities; the methods are compatible with common computer-aided drug discovery "scoring" methodologies such as 2D- and 3D-ligand similarity, docking, desirability functions based on physiochemical properties, and/or predictions from 2D/3D QSAR or machine learning models and combinations thereof to be used to guide design. We have performed experiments to assess the extent to which known drug space can be covered by our approach. Using a library of 88 generic reactions and a database of ∼20 000 reactants, we find that our methods can identify "close" analogs for ∼50% of the known small molecule drugs with molecular weight less than 300. To assess the quality of the in silico generated synthetic pathways, synthesis chemists were asked to rate the viability of synthesis pathways: both "real" and in silico generated. In silico reaction schemes generated by our methods were rated as very plausible with scores similar to known literature synthesis schemes.

Structure-based de novo design and identification of D816V mutant-selective c-KIT inhibitors

To identify potent and selective inhibitors of D816V, the most common gain-of-function c-KIT mutant, we carried out structure-based de novo design using 7-azaindole as the core and the scoring function improved by implementing an accurate solvation free energy term. This approach led to the identification of new c-KIT inhibitors specific for the D816V mutant. The 3-(3,4-dimethoxyphenyl)-7-azaindole scaffold was optimized and represents a lead structure for the design of the potent and specific inhibitors of the D816V mutant. The results of molecular dynamics simulations indicate that hydrogen bonding interactions between the 7-azadindole moiety and the backbone groups of Cys673 are the most significant determinant for the potency and selectivity of c-KIT inhibitors.

Mining basic active structures from a large-scale database

BACKGROUND

The Pubchem Database is a large-scale resource for chemical information, containing millions of chemical compound activities derived by high-throughput screening (HTS). The ability to extract characteristic substructures from such enormous amounts of data is steadily growing in importance. Compounds with shared basic active structures (BASs) exhibiting G-protein coupled receptor (GPCR) activity and repeated dose toxicity have been mined from small datasets. However, the mining process employed was not applicable to large datasets owing to a large imbalance between the numbers of active and inactive compounds. In most datasets, one active compound will appear for every 1000 inactive compounds. Most mining techniques work well only when these numbers are similar.

RESULTS

This difficulty was overcome by sampling an equal number of active and inactive compounds. The sampling process was repeated to maintain the structural diversity of the inactive compounds. An interactive KNIME workflow that enabled effective sampling and data cleaning processes was created. The application of the cascade model and subsequent structural refinement yielded the BAS candidates. Repeated sampling increased the ratio of active compounds containing these substructures. Three samplings were deemed adequate to identify all of the meaningful BASs. BASs expressing similar structures were grouped to give the final set of BASs. This method was applied to HIV integrase and protease inhibitor activities in the MDL Drug Data Report (MDDR) database and to procaspase-3 activators in the PubChem BioAssay database, yielding 14, 12, and 18 BASs, respectively.

CONCLUSIONS

The proposed mining scheme successfully extracted meaningful substructures from large datasets of chemical structures. The resulting BASs were deemed reasonable by an experienced medicinal chemist. The mining itself requires about 3 days to extract BASs with a given physiological activity. Thus, the method described herein is an effective way to analyze large HTS databases.

Interpreting linear support vector machine models with heat map molecule coloring

Background

Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity.

Results

We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor.

Conclusions

In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor.