Ion beam synthesized polycrystalline semiconducting FeSi2 on Si(001) has been investigated by transmission measurements at temperatures between 10 and 300 K. The existence of a minimum direct band gap was demonstrated and its variation with the temperature was studied by means of a three-parameter thermodynamic model and the Einstein model. Band tail states and states on a shallow impurity level were found to give rise to the absorption below the fundamental edge. The presence of an Urbach exponential edge was shown and the temperature dependence of the Urbach tail width was also studied based on the Einstein model. A strong structural disorder associated with grain boundaries between and within the FeSi2 grains and their related defects was found to be the dominant contribution at room temperature.

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Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery.

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When designing amorphous solid dispersions, it isrecommended that these thermodynamic, kinetic and environmental aspects should be completely investigatedand compared to establish rationale formulations for amorphous solid dispersions with high physical stability.

Aggregation has been posing a great challenge in drug discovery. Current computational approaches aiming to filter out aggregated molecules based on their similarity to known aggregators, such as Aggregator Advisor, have low prediction accuracy, and therefore development of reliable in silico models to detect aggregators is highly desirable. In this study, we built a data set consisting of 12 119 aggregators and 24 172 drugs or drug candidates and then developed a group of classification models based on the combination of two ensemble learning approaches and five types of molecular representations. The best model yielded an accuracy of 0.950 and an area under the curve (AUC) value of 0.987 for the training set, and an accuracy of 0.937 and an AUC of 0.976 for the test set. The best model also gave reliable predictions to the external validation set with 5681 aggregators since 80% of molecules were predicted to be aggregators with a prediction probability higher than 0.9. More importantly, we explored the relationship between colloidal aggregation and molecular features, and generalized a set of simple rules to detect aggregators. Molecular features, such as log D, the number of hydroxyl groups, the number of aromatic carbons attached to a hydrogen atom, and the number of sulfur atoms in aromatic heterocycles, would be helpful to distinguish aggregators from nonaggregators. A comparison with numerous existing druglikeness and aggregation filtering rules and models used in virtual screening verified the high reliability of the model and rules proposed in this study. We also used the model to screen several curated chemical databases, and almost 20% of molecules in the evaluated databases were predicted as aggregators, highlighting the potential high risk of aggregation in screening. Finally, we developed an online Web server of ChemAGG (http://admet.scbdd.com/ChemAGG/index), which offers a freely available tool to detect aggregators.

This study proposes use of the phase separation of immiscible polymer blends as a formulation approach to improve the stabilization and solubilization of amorphous molecular dispersions of poorly soluble drugs. This approach uses the phase separation and different drug solubilization properties of the two immiscible polymers in the blend to optimize drug loading and stabilization. The model system tested in this study is a EUDRAGIT E PO-PVP-VA 50/50 (w/w) blend loaded with felodipine via hot melt extrusion. The phase separation behavior of the polymer blend and drug loaded polymer blend formulations were characterized using a range of thermal (MTDSC), spectroscopic (ATR-FTIR), and imaging (AFM and thermal transition mapping) techniques. The polymer blend formulations demonstrated superior performance in drug release as well as stabilization against stressed temperature, stressed humidity, and mechanical milling in comparison to the drug-polymer binary systems. This is attributed to the configuration of the phase separated microstructure of the polymer blend formulations where the hydrophilic polymer domains host high concentrations of molecularly dispersed drug which are protected from moisture induced recrystallization on aging by the hydrophobic polymer domains. Additionally drug incorporation as a molecular dispersion in different polymer phases reduces the drug recrystallization tendency on aging under high temperatures and during milling.