Price Limits and Stock Market Volatility in China

This paper explores the effects of price limits on the stock market of China during global market turmoils. The characteristics of stocks that hit the price limits more frequently under market turmoil are investigated. It is found that the price limit system increases volatility significantly during the downward price movement. Moreover, price limit delays the efficient price discovery for upward and downward price movements. Finally, actively-traded stocks with a higher positive correlation with the entire market in the property industry hit the price limits more frequently

Recent Advances in Nanostructured Inorganic Hole-Transporting Materials for Perovskite Solar Cells

Organic-inorganic halide perovskite solar cells (PSCs) have received particular attention in the last decade because of the high-power conversion efficiencies (PCEs), facile fabrication route and low cost. However, one of the most crucial obstacles to hindering the commercialization of PSCs is the instability issue, which is mainly caused by the inferior quality of the perovskite films and the poor tolerance of organic hole-transporting layer (HTL) against heat and moisture. Inorganic HTL materials are regarded as promising alternatives to replace organic counterparts for stable PSCs due to the high chemical stability, wide band gap, high light transmittance and low cost. In particular, nanostructure construction is reported to be an effective strategy to boost the hole transfer capability of inorganic HTLs and then enhance the PCEs of PSCs. Herein, the recent advances in the design and fabrication of nanostructured inorganic materials as HTLs for PSCs are reviewed by highlighting the superiority of nanostructured inorganic HTLs over organic counterparts in terms of moisture and heat tolerance, hole transfer capability and light transmittance. Furthermore, several strategies to boost the performance of inorganic HTLs are proposed, including fabrication route design, functional/selectively doping, morphology control, nanocomposite construction, etc. Finally, the challenges and future research directions about nanostructured inorganic HTL-based PSCs are provided and discussed. This review presents helpful guidelines for the design and fabrication of high-efficiency and durable inorganic HTL-based PSCs

Cocrystal Prediction of Bexarotene by Graph Convolution Network and Bioavailability Improvement

Bexarotene (BEX) was approved by the FDA in 1999 for the treatment of cutaneous T-cell lymphoma (CTCL). The poor aqueous solubility causes the low bioavailability of the drug and thereby limits the clinical application. In this study, we developed a GCN-based deep learning model (CocrystalGCN) for in-silico screening of the cocrystals of BEX. The results show that our model obtained high performance relative to baseline models. The top 30 of 109 coformer candidates were scored by CocrystalGCN and then validated experimentally. Finally, cocrystals of BEX-pyrazine, BEX-2,5-dimethylpyrazine, BEX-methyl isonicotinate, and BEX-ethyl isonicotinate were successfully obtained. The crystal structures were determined by single-crystal X-ray diffraction. Powder X-ray diffraction, differential scanning calorimetry, and thermogravimetric analysis were utilized to characterize these multi-component forms. All cocrystals present superior solubility and dissolution over the parent drug. The pharmacokinetic studies show that the plasma exposures (AUC0−8h) of BEX-pyrazine and BEX-2,5-dimethylpyrazine are 1.7 and 1.8 times that of the commercially available BEX powder, respectively. This work sets a good example for integrating virtual prediction and experimental screening to discover the new cocrystals of water-insoluble drugs

KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling

Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for PKMYT1 and selective inhibitors for drug-resistant mutants of FGFRs demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape

LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP

Abstract Lipophilicity is a fundamental physical property that significantly affects various aspects of drug behavior, including solubility, permeability, metabolism, distribution, protein binding, and toxicity. Accurate prediction of lipophilicity, measured by the logD7.4 value (the distribution coefficient between n-octanol and buffer at physiological pH 7.4), is crucial for successful drug discovery and design. However, the limited availability of data for logD modeling poses a significant challenge to achieving satisfactory generalization capability. To address this challenge, we have developed a novel logD7.4 prediction model called RTlogD, which leverages knowledge from multiple sources. RTlogD combines pre-training on a chromatographic retention time (RT) dataset since the RT is influenced by lipophilicity. Additionally, microscopic pKa values are incorporated as atomic features, providing valuable insights into ionizable sites and ionization capacity. Furthermore, logP is integrated as an auxiliary task within a multitask learning framework. We conducted ablation studies and presented a detailed analysis, showcasing the effectiveness and interpretability of RT, pKa, and logP in the RTlogD model. Notably, our RTlogD model demonstrated superior performance compared to commonly used algorithms and prediction tools. These results underscore the potential of the RTlogD model to improve the accuracy and generalization of logD prediction in drug discovery and design. In summary, the RTlogD model addresses the challenge of limited data availability in logD modeling by leveraging knowledge from RT, microscopic pKa, and logP. Incorporating these factors enhances the predictive capabilities of our model, and it holds promise for real-world applications in drug discovery and design scenarios. Graphical Abstrac