Uptake and cellular responses of Microcystis aeruginosa to PFOS in various environmental conditions.

Although PFOS has been banned as a persistent organic pollutant, it still exists in large quantities within the environment, thus impacting the health of aquatic ecosystems. Previous studies focused solely on high PFOS concentrations, disregarding the connection with environmental factors. To gain a more comprehensive understanding of the PFOS effects on aquatic ecosystems amidst changing environmental conditions, this study investigated the cellular responses of Microcystis aeruginosa to varying PFOS concentrations under heatwave and nutrient stress conditions. The results showed that PFOS concentrations exceeding 5.0 µg/L had obvious effects on multiple physiological responses of M. aeruginosa, resulting in the suppression of algal cell growth and the induction of oxidative damage. However, PFOS concentration at levels below 20.0 µg/L has been found to enhance the growth of algal cells and trigger significant oxidative damage under heatwave conditions. Heatwave conditions could enhance the uptake of PFOS in algal cells, potentially leading to heightened algal growth when PFOS concentration was equal to or less than 5.0 µg/L. Conversely, deficiency or limitation of nitrogen and phosphorus significantly decreased algal abundance and chlorophyll content, inducing severe oxidative stress that could be mitigated by exposure to PFOS. This study holds significance in managing the impact of PFOS on algal growth across diverse environmental conditions.

Curcumin and analogues against head and neck cancer: From drug delivery to molecular mechanisms.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is one of the most life-threatening diseases which also causes economic burden worldwide. To overcome the limitations of traditional therapies, investigation into alternative adjuvant treatments is crucial. PURPOSE: Curcumin, a turmeric-derived compound, demonstrates significant therapeutic potential in diverse diseases, including cancer. Furthermore, research focuses on curcumin analogues and novel drug delivery systems, offering approaches for improved efficacy. This review aims to provide a comprehensive overview of curcumin's current findings, emphasizing its mechanisms of anti-HNSCC effects and potential for clinical application. METHOD: An electronic search of Web of Science, MEDLINE, and Embase was conducted to identify literature about the application of curcumin or analogues in HNSCC. Titles and abstracts were screened to identify potentially eligible studies. Full-text articles will be obtained and independently evaluated by two authors to make the decision of inclusion in the review. RESULTS: Curcumin's clinical application is hindered by poor bioavailability, prompting the exploration of methods to enhance it, such as curcumin analogues and novel drug delivery systems. Curcumin could exhibit anti-cancer effects by targeting cancer cells and modulating the tumor microenvironment in HNSCC. Mechanisms of action include cell cycle arrest, apoptosis promotion, reactive oxygen species induction, endoplasmic reticulum stress, inhibition of epithelial-mesenchymal transition, attenuation of extracellular matrix degradation, and modulation of tumor metabolism in HNSCC cells. Curcumin also targets various components of the tumor microenvironment, including cancer-associated fibroblasts, innate and adaptive immunity, and lymphovascular niches. Furthermore, curcumin enhances the anti-cancer effects of other drugs as adjunctive therapy. Two clinical trials report its potential clinical applications in treating HNSCC. CONCLUSION: Curcumin has demonstrated therapeutic potential in HNSCC through in vitro and in vivo studies. Its effectiveness is attributed to its ability to modulate cancer cells and interact with the intricate tumor microenvironment. The development of curcumin analogues and novel drug delivery systems has shown promise in improving its bioavailability, thereby expanding its clinical applications. Further research and exploration in this area hold great potential for harnessing the full therapeutic benefits of curcumin in HNSCC treatment.

MetNC: Predicting Metabolites in vivo for Natural Compounds.

Natural compounds (NCs) undergo complicated biotransformation in vivo to produce diverse forms of metabolites dynamically, many of which are of high medicinal value. Predicting the profiles of chemical products may help to narrow down possible candidates, yet current computational methods for predicting biotransformation largely focus on synthetic compounds. Here, we proposed a method of MetNC, a tailor-made method for NC biotransformation prediction, after exploring the overall patterns of NC in vivo metabolism. Based on 850 pairs of the biotransformation dataset validated by comprehensive in vivo experiments with sourcing compounds from medicinal plants, MetNC was designed to produce a list of potential metabolites through simulating in vivo biotransformation and then prioritize true metabolites into the top list according to the functional groups in compound structures and steric hindrance around the reaction sites. Among the well-known peers of GLORYx and BioTransformer, MetNC gave the highest performance in both the metabolite coverage and the ability to short-list true products. More importantly, MetNC seemed to display an extra advantage in recommending the microbiota-transformed metabolites, suggesting its potential usefulness in the overall metabolism estimation. In summary, complemented to those techniques focusing on synthetic compounds, MetNC may help to fill the gap of natural compound metabolism and narrow down those products likely to be identified in vivo.

HIT 2.0: an enhanced platform for Herbal Ingredients' Targets.

Literature-described targets of herbal ingredients have been explored to facilitate the mechanistic study of herbs, as well as the new drug discovery. Though several databases provided similar information, the majority of them are limited to literatures before 2010 and need to be updated urgently. HIT 2.0 was here constructed as the latest curated dataset focusing on Herbal Ingredients' Targets covering PubMed literatures 2000-2020. Currently, HIT 2.0 hosts 10 031 compound-target activity pairs with quality indicators between 2208 targets and 1237 ingredients from more than 1250 reputable herbs. The molecular targets cover those genes/proteins being directly/indirectly activated/inhibited, protein binders, and enzymes substrates or products. Also included are those genes regulated under the treatment of individual ingredient. Crosslinks were made to databases of TTD, DrugBank, KEGG, PDB, UniProt, Pfam, NCBI, TCM-ID and others. More importantly, HIT enables automatic Target-mining and My-target curation from daily released PubMed literatures. Thus, users can retrieve and download the latest abstracts containing potential targets for interested compounds, even for those not yet covered in HIT. Further, users can log into 'My-target' system, to curate personal target-profiling on line based on retrieved abstracts. HIT can be accessible at http://hit2.badd-cao.net.

Comparative Network Pharmacology Analysis of Classical TCM Prescriptions for Chronic Liver Disease.

Chronic liver disease (CLD) has become a major global health problem while herb prescriptions are clinically observed with significant efficacy. Three classical Traditional Chinese Medicine (TCM) formulae, Yinchenhao Decoction (YCHT), Huangqi Decoction (HQT), and Yiguanjian (YGJ) have been widely applied in China to treat CLD, but no systematic study has yet been published to investigate their common and different mechanism of action (MOA). Partial limitation may own to deficiency of effective bioinformatics methods. Here, a computational framework of comparative network pharmacology is firstly proposed and then applied to herbal recipes for CLD disease. The analysis showed that, the three formulae modulate CLD mainly through functional modules of immune response, inflammation, energy metabolism, oxidative stress, and others. On top of that, each formula can target additional unique modules. Typically, YGJ ingredients can uniquely target the ATP synthesis and neurotransmitter release cycle. Interestingly, different formulae may regulate the same functional module in different modes. For instance, YCHT and YGJ can activate oxidative stress-related genes of SOD family while HQT are found to inhibit SOD1 gene. Overall, our framework of comparative network pharmacology proposed in our work may not only explain the MOA of different formulae treating CLD, but also provide hints to further investigate the biological basis of CLD subtypes.

In Silico Study of Anti-Insomnia Mechanism for Suanzaoren Prescription.

Insomnia is a common and widespread sleeping disorder caused by various risk factors. Though beneficial, conventional treatments of insomnia have significant limitations. As an alternative treatment, Chinese herbal formula Suanzaoren prescription (SZRP), composed of Suanzaoren [seeds of Ziziphus jujuba var. spinosa (Bunge) Hu ex H.F.Chow] and four additional herbs, has been reported with significant anti-insomnia effects. Yet the anti-insomnia mechanism of the herb formulae remains unknown. In this study, we attempted to extrapolate the holistic anti-insomnia mechanism of SZRP through herbal targeting and network pharmacology. The results indicated that the ingredients of Suanzaoren can target multi-neurotransmitter receptors at synapse interface, which was reported to be associated with sedative and hypnotic effects, while the four additional herbs can hit multiple pathways downstream of membrane neurotransmitters. Furthermore, the four additional herbs showed highly cooperative targeting patterns in the paralleled and cross-talked pathways related to inflammatory regulation and endocrine system, which may contribute to the additional relief of insomnia caused by inflammation, anxiety, or endocrine disorder. The interesting complementary mechanism we found among the herbal groups of SZRP may provide an example to study Chinese herbal formula and offers clues to future design of anti-insomnia strategy.

Exploring the Mechanism of Flavonoids Through Systematic Bioinformatics Analysis.

Flavonoids are the largest class of plant polyphenols, with common structure of diphenylpropanes, consisting of two aromatic rings linked through three carbons and are abundant in both daily diets and medicinal plants. Fueled by the recognition of consuming flavonoids to get better health, researchers became interested in deciphering how flavonoids alter the functions of human body. Here, systematic studies were performed on 679 flavonoid compounds and 481 corresponding targets through bioinformatics analysis. Multiple human diseases related pathways including cancers, neuro-disease, diabetes, and infectious diseases were significantly regulated by flavonoids. Specific functions of each flavonoid subclass were further analyzed in both target and pathway level. Flavones and isoflavones were significantly enriched in multi-cancer related pathways, flavan-3-ols were found focusing on cellular processing and lymphocyte regulation, flavones preferred to act on cardiovascular related activities and isoflavones were closely related with cell multisystem disorders. Relationship between chemical constitution fragment and biological effects indicated that different side chain could significantly affect the biological functions of flavonoids subclasses. Results will highlight the common and preference functions of flavonoids and their subclasses, which concerning their pharmacological and biological properties.

Key symptoms selection for two major syndromes diagnosis of Chinese medicine in chronic hepatitis B.

OBJECTIVE: To identify key symptoms of two major syndromes in chronic hepatitis B (CHB), which can be the clinical evidence for Chinese medicine (CM) doctors to make decisions. METHODS: Standardization scales on diagnosis for CHB in CM were designed including physical symptoms, tongue and pulse appearance. The total of 695 CHB cases with dampness-heat (DH) syndrome or Pi (Spleen) deficiency (SD) syndrome were collected for feature selection and modeling, another 275 CHB patients were collected in different locations for validation. Key symptoms were selected based on modified information gain (IG), and 5 classifiers were applied to assist with models training and validation. Classification accuracy and area under receiver operating characteristic curves (AUC) were evaluated. RESULTS: (1) Thirteen DH syndrome key symptoms and 13 SD syndrome key symptoms were selected from original 125 symptoms; (2) The key symptoms could achieve similar or better diagnostic accuracy than the original total symptoms; (3) In the validation phase, the key symptoms could identify syndromes effectively, especially in DH syndrome, which average prediction accuracy on 5 classifiers could achieve 0.864 with the average AUC 0.772. CONCLUSION: The selected key symptoms could be simple DH and SD syndromes diagnostic elements applied in clinical directly. (Registration N0.: ChiCTR-DCC-10000759).

In Silico Insight into Potential Anti-Alzheimer's Disease Mechanisms of Icariin.

Herbal compounds that have notable therapeutic effect upon Alzheimer's disease (AD) have frequently been found, despite the recent failure of late-stage clinical drugs. Icariin, which is isolated from Epimedium brevicornum, is widely reported to exhibit significant anti-AD effects in in vitro and in vivo studies. However, the molecular mechanism remains thus far unclear. In this work, the anti-AD mechanisms of icariin were investigated at a target network level assisted by an in silico target identification program (INVDOCK). The results suggested that the anti-AD effects of icariin may be contributed by: attenuation of hyperphosphorylation of tau protein, anti-inflammation and regulation of Ca(2+) homeostasis. Our results may provide assistance in understanding the molecular mechanism and further developing icariin into promising anti-AD agents.

Screening Ingredients from Herbs against Pregnane X Receptor in the Study of Inductive Herb-Drug Interactions: Combining Pharmacophore and Docking-Based Rank Aggregation.

The issue of herb-drug interactions has been widely reported. Herbal ingredients can activate nuclear receptors and further induce the gene expression alteration of drug-metabolizing enzyme and/or transporter. Therefore, the herb-drug interaction will happen when the herbs and drugs are coadministered. This kind of interaction is called inductive herb-drug interactions. Pregnane X Receptor (PXR) and drug-metabolizing target genes are involved in most of inductive herb-drug interactions. To predict this kind of herb-drug interaction, the protocol could be simplified to only screen agonists of PXR from herbs because the relations of drugs with their metabolizing enzymes are well studied. Here, a combinational in silico strategy of pharmacophore modelling and docking-based rank aggregation (DRA) was employed to identify PXR's agonists. Firstly, 305 ingredients were screened out from 820 ingredients as candidate agonists of PXR with our pharmacophore model. Secondly, DRA was used to rerank the result of pharmacophore filtering. To validate our prediction, a curated herb-drug interaction database was built, which recorded 380 herb-drug interactions. Finally, among the top 10 herb ingredients from the ranking list, 6 ingredients were reported to involve in herb-drug interactions. The accuracy of our method is higher than other traditional methods. The strategy could be extended to studies on other inductive herb-drug interactions.

Integrating clinical indexes into four-diagnostic information contributes to the Traditional Chinese Medicine (TCM) syndrome diagnosis of chronic hepatitis B.

Traditional Chinese Medicine (TCM) treatment has been commonly used to treat Chronic Hepatitis B (CHB) in Asian countries based on TCM syndrome diagnosis, also called "ZHENG". The syndrome is identified through the four-diagnostic methods, with certain degree of subjectivity and ambiguity from individual doctors. Normally those CHB patients also receive series of parameters from modern clinical examination, while they are routinely believed to be unrelated with the TCM syndrome diagnosis. In this study, we investigated whether these biomedical indexes in modern medicine could be beneficial to TCM syndrome diagnostics in an integrative way. Based on 634 patient samples from health controls and three subtypes of CHB syndromes, a two-view based hierarchical classification model was tested for TCM syndromes prediction based on totally 222 parameters integrated from both TCM practice and modern clinical tests. The results indicated that the performance of syndrome classification based on a proper integration of TCM and modern clinical indexes was significantly higher than those based on one view of parameters only. Furthermore, those indexes correlated with CHB syndrome diagnosis were successfully identified for CM indexes and biochemical indexes respectively, where potential associations between them were hinted to the MAPK signaling pathway.

Target network differences between western drugs and Chinese herbal ingredients in treating cardiovascular disease.

BACKGROUND: Western drugs have achieved great successes in CVDs treatment. However, they may lead to some side effects and drug resistance. On the other hand, more and more studies found that Traditional Chinese herbs have efficient therapeutic effects for CVDs, while their therapeutic mechanism is still not very clear. It may be a good view towards molecules, targets and network to decipher whether difference exists between anti-CVD western drugs and Chinese herbal ingredients. RESULTS: Anti-CVD western drugs and Chinese herbal ingredients, as well as their targets were thoroughly collected in this work. The similarities and the differences between the herbal ingredients and the western drugs were deeply explored based on three target-based perspectives including biochemical property, regulated pathway and disease network. The biological function of herbal ingredients' targets is more complex than that of the western drugs' targets. The signal transduction and immune system associated signaling pathways, apoptosis associated pathways may be the most important pathway for herbal ingredients, however the western drugs incline to regulate vascular smooth muscle contraction associated pathways. Chinese herbal ingredients prefer to regulate the downstream proteins of apoptosis associated pathway; while the western drugs incline to regulate the upstream proteins of VECC (Vascular Epidermal Cells Contraction) related pathways. CONCLUSION: In summary, the characteristics identified in this study would be valuable for designing new network-based multi-target CVD drugs or vaccine adjuvants.

HIM-herbal ingredients in-vivo metabolism database.

BACKGROUND: Herbal medicine has long been viewed as a valuable asset for potential new drug discovery and herbal ingredients' metabolites, especially the in vivo metabolites were often found to gain better pharmacological, pharmacokinetic and even better safety profiles compared to their parent compounds. However, these herbal metabolite information is still scattered and waiting to be collected. DESCRIPTION: HIM database manually collected so far the most comprehensive available in-vivo metabolism information for herbal active ingredients, as well as their corresponding bioactivity, organs and/or tissues distribution, toxicity, ADME and the clinical research profile. Currently HIM contains 361 ingredients and 1104 corresponding in-vivo metabolites from 673 reputable herbs. Tools of structural similarity, substructure search and Lipinski's Rule of Five are also provided. Various links were made to PubChem, PubMed, TCM-ID (Traditional Chinese Medicine Information database) and HIT (Herbal ingredients' targets databases). CONCLUSIONS: A curated database HIM is set up for the in vivo metabolites information of the active ingredients for Chinese herbs, together with their corresponding bioactivity, toxicity and ADME profile. HIM is freely accessible to academic researchers at http://www.bioinformatics.org.cn/.

Similar connotation in chronic hepatitis B and nonalcoholic Fatty liver patients with dampness-heat syndrome.

The phenomenon that the same syndrome turns up in different diseases appears in the sight of people around the world, which raises the thought for possibility of "Same Treatment for Different Diseases." Actually, treatment based on ZHENG classification in Traditional Chinese Medicine could bring revelation for the former finding. The dampness-heat syndrome in chronic hepatitis B and nonalcoholic fatty liver is regarded as the breakthrough point. We discussed the molecular mechanism of similar connotation that exists in chronic hepatitis B and nonalcoholic fatty liver by metabonomics to give the modern understanding of dampness-heat syndrome. Both urine and serum metabolic profiling revealed that obvious differences existed between dampness-heat syndrome and non-dampness-heat syndrome but the commonality was proved to appear in chronic hepatitis B and nonalcoholic fatty liver patients with dampness-heat syndrome. Furthermore, disorder of body fluid metabolism, decline in digestive capacity, and imbalance of intestinal flora were found to be the new guiding for treatment, with the hope to provide the basis for Chinese personalized medicine.

Network-based drug discovery by integrating systems biology and computational technologies.

Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple '-omics' databases. The newly developed algorithm- or network-based computational models can tightly integrate '-omics' databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various '-omics' platforms and computational tools would accelerate development of network-based drug discovery and network medicine.

Towards a bioinformatics analysis of anti-Alzheimer's herbal medicines from a target network perspective.

With the growth of aging population all over the world, a rising incidence of Alzheimer's disease (AD) has been recently observed. In contrast to FDA-approved western drugs, herbal medicines, featured as abundant ingredients and multi-targeting, have been acknowledged with notable anti-AD effects although the mechanism of action (MOA) is unknown. Investigating the possible MOA for these herbs can not only refresh but also extend the current knowledge of AD pathogenesis. In this study, clinically tested anti-AD herbs, their ingredients as well as their corresponding target proteins were systematically reviewed together with applicable bioinformatics resources and methodologies. Based on above information and resources, we present a systematically target network analysis framework to explore the mechanism of anti-AD herb ingredients. Our results indicated that, in addition to the binding of those symptom-relieving targets as the FDA-approved drugs usually do, ingredients of anti-AD herbs also interact closely with a variety of successful therapeutic targets related to other diseases, such as inflammation, cancer and diabetes, suggesting the possible cross-talks between these complicated diseases. Furthermore, pathways of Ca(2+) equilibrium maintaining upstream of cell proliferation and inflammation were densely targeted by the anti-AD herbal ingredients with rigorous statistic evaluation. In addition to the holistic understanding of the pathogenesis of AD, the integrated network analysis on the MOA of herbal ingredients may also suggest new clues for the future disease modifying strategies.

Therapeutic effects of astragaloside IV on myocardial injuries: multi-target identification and network analysis.

Astragaloside IV (AGS-IV) is a main active ingredient of Astragalus membranaceus Bunge, a medicinal herb used for cardiovascular diseases (CVD). In this work, we investigated the therapeutic mechanisms of AGS-IV at a network level by computer-assisted target identification with the in silico inverse docking program (INVDOCK). Targets included in the analysis covered all signaling pathways thought to be implicated in the therapeutic actions of all CVD drugs approved by US FDA. A total of 39 putative targets were identified. Three of these targets, calcineurin (CN), angiotensin-converting enzyme (ACE), and c-Jun N-terminal kinase (JNK), were experimentally validated at a molecular level. Protective effects of AGS-IV were also compared with the CN inhibitor cyclosporin A (CsA) in cultured cardiomyocytes exposed to adriamycin. Network analysis of protein-protein interactions (PPI) was carried out with reference to the therapeutic profiles of approved CVD drugs. The results suggested that the therapeutic effects of AGS-IV are based upon a combination of blocking calcium influx, vasodilation, anti-thrombosis, anti-oxidation, anti-inflammation and immune regulation.

Proteomic characterization of the possible molecular targets of pyrrolizidine alkaloid isoline-induced hepatotoxicity.

Pyrrolizidine alkaloids (PAs) are distributed in plants worldwide including medicinal herbs or teas. In the present study, we investigated the effects of isoline, which is a retronecine-type PA isolated from traditional Chinese medicinal herb Ligularia duciformis, on mouse liver proteins by using proteomic approaches. Firstly, our results showed that 110mg/kg isoline increased alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activities in serum, and hepatic tissue pathological observation further confirmed isoline-induced liver injury. Proteomic analysis showed that the liver samples from mice of isoline group demonstrated about 13 differentially expressed proteins compared with normal group, and those proteins may be involved in isoline-induced liver injury in mice. Next, all these 13 protein spots were identified by MALDI-TOF-TOF MS or LTQ MS; and among them 9 differentially expressed proteins are involved in the process of oxidative stress or cellular energy metabolism. Further lipid peroxidation analysis and ATPase assay confirmed the existing of oxidative injury induced by isoline and consequent disruption of energy metabolism. Furthermore, an in silico drug target searching program INVDOCK identified 2 potential protein targets of isoline, and the results are in support of proteomic analysis. In summary, the possible signaling molecules related with isoline-induced liver injury were demonstrated in this study.

Quantitatively integrating molecular structure and bioactivity profile evidence into drug-target relationship analysis.

BACKGROUND: Public resources of chemical compound are in a rapid growth both in quantity and the types of data-representation. To comprehensively understand the relationship between the intrinsic features of chemical compounds and protein targets is an essential task to evaluate potential protein-binding function for virtual drug screening. In previous studies, correlations were proposed between bioactivity profiles and target networks, especially when chemical structures were similar. With the lack of effective quantitative methods to uncover such correlation, it is demanding and necessary for us to integrate the information from multiple data sources to produce an comprehensive assessment of the similarity between small molecules, as well as quantitatively uncover the relationship between compounds and their targets by such integrated schema. RESULTS: In this study a multi-view based clustering algorithm was introduced to quantitatively integrate compound similarity from both bioactivity profiles and structural fingerprints. Firstly, a hierarchy clustering was performed with the fused similarity on 37 compounds curated from PubChem. Compared to clustering in a single view, the overall common target number within fused classes has been improved by using the integrated similarity, which indicated that the present multi-view based clustering is more efficient by successfully identifying clusters with its members sharing more number of common targets. Analysis in certain classes reveals that mutual complement of the two views for compound description helps to discover missing similar compound when only single view was applied. Then, a large-scale drug virtual screen was performed on 1267 compounds curated from Connectivity Map (CMap) dataset based on the fused similarity, which obtained a better ranking result compared to that of single-view. These comprehensive tests indicated that by combining different data representations; an improved assessment of target-specific compound similarity can be achieved. CONCLUSIONS: Our study presented an efficient, extendable and quantitative computational model for integration of different compound representations, and expected to provide new clues to improve the virtual drug screening from various pharmacological properties. Scripts, supplementary materials and data used in this study are publicly available at http://lifecenter.sgst.cn/fusion/.

Virtual drug screen schema based on multiview similarity integration and ranking aggregation.

The current drug virtual screen (VS) methods mainly include two categories. i.e., ligand/target structure-based virtual screen and that, utilizing protein-ligand interaction fingerprint information based on the large number of complex structures. Since the former one focuses on the one-side information while the later one focuses on the whole complex structure, they are thus complementary and can be boosted by each other. However, a common problem faced here is how to present a comprehensive understanding and evaluation of the various virtual screen results derived from various VS methods. Furthermore, there is still an urgent need for developing an efficient approach to fully integrate various VS methods from a comprehensive multiview perspective. In this study, our virtual screen schema based on multiview similarity integration and ranking aggregation was tested comprehensively with statistical evaluations, providing several novel and useful clues on how to perform drug VS from multiple heterogeneous data sources. (1) 18 complex structures of HIV-1 protease with ligands from the PDB were curated as a test data set and the VS was performed with five different drug representations. Ritonavir ( 1HXW ) was selected as the query in VS and the weighted ranks of the query results were aggregated from multiple views through four similarity integration approaches. (2) Further, one of the ranking aggregation methods was used to integrate the similarity ranks calculated by gene ontology (GO) fingerprint and structural fingerprint on the data set from connectivity map, and two typical HDAC and HSP90 inhibitors were chosen as the queries. The results show that rank aggregation can enhance the result of similarity searching in VS when two or more descriptions are involved and provide a more reasonable similarity rank result. Our study shows that integrated VS based on multiple data fusion can achieve a remarkable better performance compared to that from individual ones and, thus, serves as a promising way for efficient drug screening, taking advantages of the rapidly accumulated molecule representations and heterogeneous data in the pharmacological area.