Dual-electron-enhanced effect in K-doped MoS2 few layers for high electrocatalytic activity as the counter electrode in dye-sensitized solar cells.

Designing counter electrodes (CEs) with high efficiency and understanding the mechanism of dye-sensitized solar cells (DSSCs) are still challenges. In this paper, we synthesized K-doped molybdenum disulfide (K-MoS2) with few layers and it has a great enhancement effect on the electrocatalytic activity compared to pure MoS2 CE and reference Pt CE. A dual electron-path model is proposed to explain the mechanism, which is supported by first-principles calculations. When an electron in MoS2 is transferred to the triiodide, the K atoms can act as an electron reservoir to provide another electron in a short time to improve the catalytic activity. So the proposed dual-electron effect in this paper is helpful to understand the charge transfer mechanism on the interfaces of these CEs and may be crucial for obtaining high photoelectric efficiencies in DSSCs.

Discovery of Isoindoline Amide Derivatives as Potent and Orally Bioavailable ADAMTS-4/5 Inhibitors for the Treatment of Osteoarthritis.

Osteoarthritis (OA) treatment is a highly unmet medical need. Development of a disease-modifying OA drug (DMOAD) is challenging with no approved drugs on the market. Inhibition of ADATMS-4/5 is a promising OA therapeutics to target cartilage degradation and potentially can reduce joint pain and restore its normal function. Starting from the reported ADAMTS-5 inhibitor GLPG1972, we applied a scaffold hopping strategy to generate a novel isoindoline amide scaffold. Representative compound 18 showed high potency in ADATMS-4/5 inhibition, as well as good selectivity over a panel of other metalloproteases. In addition, compound 18 exhibited excellent druglike properties and showed better pharmacokinetic (PK) profiles than GLPG1972 cross-species. Compound 18 demonstrated dose-dependent efficacy in two in vivo rat osteoarthritis models.

Discovery of novel spiro compound as RAF kinase inhibitor with in vitro potency against KRAS mutant cancer.

The development of RAF inhibitors targeting cancers with wild type RAF kinase and/or RAS mutation has been challenging due to the paradoxical activation of the RAS-RAF-MEK-ERK cascade following RAF inhibitor treatment. Herein is the discovery and optimization of a series of RAF inhibitors with a novel spiro structure. The most potent spiro molecule 9 showed excellent in vitro potency against b/c RAF enzymes and RAS mutant H358 cancer cells with minimal paradoxical RAF signaling activation. Compound 9 also exhibited good drug-like properties as demonstrated by in vitro cytochrome P450 (CYP), liver microsome stability (LMS) data and moderate oral pharmacokinetics (PK) profiles in rat and mouse.

Discovery of spiro amide SHR902275: A potent, selective, and efficacious RAF inhibitor targeting RAS mutant cancers.

The RAS-RAF-MEK-ERK signaling pathway plays a key role to regulate multiple cellular functions. Acquired resistance to the first-generation RAF inhibitors that only targeted the bRAFV600E mutation prompted the need for a new generation of RAF inhibitors to target cancers bearing mutant RAS and wild type RAF activity by inhibition of paradoxical activation. Starting from the company's previously reported RAF inhibitor 1, extensive drug potency and drug-like properties optimizations led to the discovery of molecule 33 (SHR902275) with greatly improved in vitro potency and solubility. Molecule 33 exhibited good DMPK (Drug Metabolism and Pharmacokinetics) properties, excellent permeability, and outstanding mouse/rat oral PK. It was further evaluated in an in vivo RAS mutant Calu6 xenograft mouse model and demonstrated dose dependent efficacy. To achieve high exposure in a toxicity study, pro-drug 48 was also explored.

Protein-Ligand Empirical Interaction Components for Virtual Screening.

A major shortcoming of empirical scoring functions is that they often fail to predict binding affinity properly. Removing false positives of docking results is one of the most challenging works in structure-based virtual screening. Postdocking filters, making use of all kinds of experimental structure and activity information, may help in solving the issue. We describe a new method based on detailed protein-ligand interaction decomposition and machine learning. Protein-ligand empirical interaction components (PLEIC) are used as descriptors for support vector machine learning to develop a classification model (PLEIC-SVM) to discriminate false positives from true positives. Experimentally derived activity information is used for model training. An extensive benchmark study on 36 diverse data sets from the DUD-E database has been performed to evaluate the performance of the new method. The results show that the new method performs much better than standard empirical scoring functions in structure-based virtual screening. The trained PLEIC-SVM model is able to capture important interaction patterns between ligand and protein residues for one specific target, which is helpful in discarding false positives in postdocking filtering.

Interaction Entropy for Computational Alanine Scanning.

The theoretical calculation of protein-protein binding free energy is a grand challenge in computational biology. Accurate prediction of critical residues along with their specific and quantitative contributions to protein-protein binding free energy is extremely helpful to reveal binding mechanisms and identify drug-like molecules that alter protein-protein interactions. In this paper, we propose an interaction entropy approach combined with the molecular mechanics/generalized Born surface area (MM/GBSA) method for solvation to compute residue-specific protein-protein binding free energy. In the current approach, the entropic loss in binding free energy of individual residues is explicitly computed from moledular dynamics (MD) simulation by using the interaction entropy method. In this approach the entropic contribution to binding free energy is determined from fluctuation of the interaction in MD simulation. Studies for an extensive set of realistic protein-protein interaction systems showed that by including the entropic contribution, the computed residue-specific binding free energies are in better agreement with the corresponding experimental data.