In recent years, various virtual screening (VS) tools have been developed, and many successful screening campaigns have been showcased. However, whether by conventional molecular docking or pharmacophore screening, the selection of virtual hits is based on the ranking of compounds by scoring functions or fit values, which remains the bottleneck of VS due to insufficient accuracy. As the limitations of individual methods persist, a comprehensive comparison and integration of different methods may provide insights into selecting suitable methods for VS. Here, we evaluated the performance of molecular docking, fingerprint-based 2D similarity and multicomplex pharmacophore in an individual and a combined manner, through a retrospective VS study on VEGFR-2 inhibitors. An integrated two-layer workflow was developed and validated through VS of VEGFR-2 inhibitors against the DUD-E database, which demonstrated improved VS performance through a ligand-based method ECFP_4, followed by molecular docking, and then a strict multicomplex pharmacophore. Through a retrospective comparison with six published papers, this integrated approach outperformed 43 out of 45 methods, indicating a great effectiveness. This kind of integrated VS approach can be extended to other targets for the screening and discovery of inhibitors.

In this paper, we propose a new term weighting scheme called Term Frequency -Inverse Corpus Frequency (TF-ICF). It does not require term frequency information from other documents within the document collection and thus, it enables us to generate the document vectors of N streaming documents in linear time. In the context of a machine learning application, unsupervised document clustering, we evaluated the effectiveness of the proposed approach in comparison to five widely used term weighting schemes through extensive experimentation. Our results show that TF-ICF can produce document clusters that are of comparable quality as those generated by the widely recognized term weighting schemes and it is significantly faster than those methods.

Free fatty acids (FFAs) are vitally important components of lipids that modulate biological metabolism in various ways. Although the molecular structures are simple, the analysis of FFAs is still challenging due to their unique properties and wide concentration range. In the present study, a high-coverage liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was established for the quantification of FFAs in serum samples using two structural analogues 5-(dimethylamino)naphthalene-1-sulfonyl piperazine (Dns-PP) and (diethylamino)naphthalene-1-sulfonyl piperazine (Dens-PP) as twin derivatization reagents. The Dns labeling of FFAs could significantly enhance their MS response via the introduction of the easily ionizable moiety of a tertiary amine-containing part and aid fragmentation in the multiple reaction monitoring (MRM) mode. Our results demonstrated that the detection sensitivities of FFAs were increased by 50-1500 fold compared with the nonderivatization method. At the same time, Dens-labeled standards were used as one-to-one internal standards to ensure accurate quantifications. Thirty-eight FFAs, covering short-, medium-, and long-chain, could be quantified in wide dynamic range with the lower limit of quantification (LLOQ) varied from 2 to 20 nM. Using this method, we analyzed serum FFAs in rat models of cisplatin-induced nephrotoxicity and irinotecan-induced gastrointestinal toxicity, respectively. The findings were further compared with those revealed by previous untargeted metabolomics. The results indicate that twin derivatization-based LC-MS provides a more accurate view of global FFA alternation and has great application potential in the fields of targeted metabolomics.

Computational and experimental studies were applied to the discovery of a series of novel vascular endothelial growth factor receptor 2 (VEGFR-2) inhibitors. Eight compounds exhibited nanomolar IC values against VEGFR-2, and compounds 6, 19, 22, and 23 showed potent antiproliferative effects against several cell lines. Particularly, compound 23 behaved better than FDA approved drugs, sorafenib and sunitinib, in antiproliferative activity against cell lines related to all nine tumor types tested (GI values), and it was better or comparable in safety (LC values). Compound 23 even demonstrated a high potency on one of the drug-resistant cell lines (NCI/ADR-RES) responsible for ovarian cancer and cell lines contributing to prostate cancer, regarded as one of the VEGF/VEGFR pathway drug-resistant tumors. This compound is likely a promising candidate for the treatment of leukemia, non-small cell lung cancer (NSCLC), colon cancer, ovarian cancer, and breast cancer with a suitable balance of both efficacy and safety.