GCdiscrimination: identification of gastric cancer based on a milliliter of blood.

Gastric cancer (GC) continues to be one of the major causes of cancer deaths worldwide. Meanwhile, liquid biopsies have received extensive attention in the screening and detection of cancer along with better understanding and clinical practice of biomarkers. In this work, 58 routine blood biochemical indices were tentatively used as integrated markers, which further expanded the scope of liquid biopsies and a discrimination system for GC consisting of 17 top-ranked indices, elaborated by random forest method was constructed to assist in preliminary assessment prior to histological and gastroscopic diagnosis based on the test data of a total of 2951 samples. The selected indices are composed of eight routine blood indices (MO%, IG#, IG%, EO%, P-LCR, RDW-SD, HCT and RDW-CV) and nine blood biochemical indices (TP, AMY, GLO, CK, CHO, CK-MB, TG, ALB and γ-GGT). The system presented a robust classification performance, which can quickly distinguish GC from other stomach diseases, different cancers and healthy people with sensitivity, specificity, total accuracy and area under the curve of 0.9067, 0.9216, 0.9138 and 0.9720 for the cross-validation set, respectively. Besides, this system can not only provide an innovative strategy to facilitate rapid and real-time GC identification, but also reveal the remote correlation between GC and these routine blood biochemical parameters, which helped to unravel the hidden association of these parameters with GC and serve as the basis for subsequent studies of the clinical value in prevention program and surveillance management for GC. The identification system, called GC discrimination, is now available online at http://lishuyan.lzu.edu.cn/GC/.

(2E)-1-(2,4,6-Trimethoxyphenyl)-3-(4-chlorophenyl)prop-2-en-1-one, a Chalcone Derivative, Promotes Apoptosis by Suppressing RAS-ERK and AKT/FOXO3a Pathways in Hepatocellular Carcinoma Cells.

BACKGROUND: Liver cancer is an extremely common cancer with the highest mortality rate and poor prognosis. Owing to their low systemic toxicity and few side effects, natural compounds may provide better therapeutic effects for patients. (2E)-1-(2,4,6-trimethoxyphenyl)-3-(4-chlorophenyl)prop-2-en-1-one (TMOCC), a chalcone derivative, exhibits cytotoxicity towards many tumor cells. However, the anticancer mechanism of TMOCC has not been elucidated in human hepatocellular carcinoma (HCC). METHODS: Cell Counting Kit-8 and colony formation assays were used to evaluate the effects of TMOCC on viability and proliferation. Mitochondrial transmembrane potential and flow cytometry assays were used to detect apoptosis. The expression levels of proteins related to apoptosis, the RAS-ERK and AKT/FOXO3a signaling pathways were assessed using western blot. Potential targets of TMOCC were detected using molecular docking analysis. RESULTS: TMOCC inhibited viability and proliferation, and induced the loss of mitochondrial transmembrane potential, apoptosis and DNA double-strand breaks in both HCC cells. The RAS-ERK and AKT/FOXO3a signaling pathways were suppressed by TMOCC. Finally, ERK1, PARP-1, and BAX were identified as potential targets of TMOCC. CONCLUSION: Taken together, our results show that TMOCC promotes apoptosis by suppressing the RAS-ERK and AKT/FOXO3a signaling pathways. TMOCC may be a potential multi-target compound that is effective against liver cancer.

High-Throughput Screening for Thermoelectric Semiconductors with Desired Conduction Types by Energy Positions of Band Edges.

The conduction type of semiconductors is vitally important in many fields (e.g., photovoltaics, transistors, and thermoelectrics), but so far, there is no effective and simple indicator to quickly judge or predict the conduction type of various semiconductors. In this work, based on the relationship between the formation energy of charged defect and the Fermi level, we propose a simple and low-cost strategy for high-throughput screening the potential n-type or p-type semiconductors from the material database by using energy positions of band edges as indicators. As a case study, we validate this strategy in searching potential n-type thermoelectric materials from copper (Cu)-containing metal chalcogenides. A new promising thermoelectric material, CuIn5Se8, with potential intrinsic n-type conduction, is successfully screened from 407 Cu-containing metal chalcogenides and validated in the subsequent experiments. Upon doping iodine in CuIn5Se8, a peak thermoelectric figure of merit zT of 0.84 is obtained at 850 K. Beyond thermoelectrics, the strategy proposed in this study also sheds light on the new material development with desired conduction types in photovoltaics, transistors, and other fields.

Conformational dynamics is critical for the allosteric inhibition of cGAS upon acetyl-mimic mutations.

Detection of cytosolic dsDNA by cyclic GMP-AMP synthase (cGAS) is critical for the immune system to sense and fight against infection, but chronic activation of cGAS by self-DNA leads to autoimmune diseases without effective treatment yet. It was found that acetylation on either Lys384, Lys394, or Lys414 could inhibit the catalytic production of cGAMP by cGAS, and further suppressed self-DNA-induced autoimmunity. However, the implied mechanism remains unclear. Here, extensive molecular dynamics simulations combined with multiple analytical approaches were employed to uncover the allosteric inhibition mechanisms by using the K-to-Q mutations to mimic acetylation. Results suggested that the exterior loops contributed most to the conformational dynamics of cGAS, and two concerted intrinsic motions were observed: the inward/outward or twisting movement for the outer appendage of lobe 1 and the open/closed swing of the active-site loops. Mutations slightly affected the binding of dsDNA and cGAMP. The shift of the conformational sampling of the active-site loops or residues around cGAMP upon mutation might potentially explain the inhibition of cGAS activity. Moreover, the intra- and inter-molecular coupling was weakened upon mutations more or less but via distinct pathways. Hence, conformational dynamics play a vital role in the allosteric inhibition of cGAS upon the studied acetyl-mimic mutations. As the studied acetyl-mimic mutations are located at either the inter-lobe or inter-molecular interfaces, hence except for acetylation, our findings might help the development of new therapeutics against autoimmune diseases due to abnormal cGAS activation by designing inter-lobe or intermolecular allosteric inhibitors.

ATBdiscrimination: An in Silico Tool for Identification of Active Tuberculosis Disease Based on Routine Blood Test and T-SPOT.TB Detection Results.

Tuberculosis remains one of the deadliest infectious diseases worldwide. Only 5-15% of people infected with Mycobacterium tuberculosis develop active TB disease (ATB), while others remain latently infected (LTBI) during their lifetime, which has a completely different clinical treatment schedule. However, most current clinical diagnostic methods are based on the immune response of M. tuberculosis infections and cannot distinguish ATB from LTBIs. Thus, the rapid diagnosis of active or latent tuberculosis infections remains a serious challenge for clinicians. In this work, based on the test data of a total of 478 patients, 36 blood biochemical data were specially included with T-SPOT.TB detection results which are all from routine clinical practice as commercially available. Then a discrimination method to detect ATB infections was successfully developed based on these data by the random forest algorithm. This method presents a robust classification performance with AUC as 0.9256 and 0.8731 for the cross-validation set and the external validation set, respectively. This work suggests an innovative strategy for identification of ATB disease from a single drop of blood with advantages of being timely, efficient, and economical. It also provides valuable information for the comprehensive understanding of TB with deep associations between TB infection and routine blood test data. The web server of this identification method, called ATBdiscrimination, is now available online at http://lishuyan.lzu.edu.cn/ATB/ATBdiscrimination.html .

Discovery of High-Performance Thermoelectric Chalcogenides through Reliable High-Throughput Material Screening.

High-throughput (HTP) material design is an emerging field and has been proved to be powerful in the prediction of novel functional materials. In this work, an HTP effort has been carried out for thermoelectric chalcogenides with diamond-like structures on the newly established Materials Informatics Platform (MIP). Specifically, the relaxation time is evaluated by a reliable yet efficient method, which greatly improves the accuracy of HTP electrical transport calculations. The results show that all the compounds may have power factors over 10 µW/cm·K2 if fully optimized. A new series of diamond-like chalcogenides with an atomic ratio of 1:2:4 possess relatively higher electrical transport properties among all the compounds investigated. One particular compound, CdIn2Te4, and its variations have been verified experimentally with a peak ZT over 1.0. Further analysis reveals the existence of general conductive networks and the similar Pisarenko relations under the same anion sublattice, and the transport distribution function is found to be a good indicator for the power factors for the compounds investigated. This work demonstrates a successful case study in HTP material screening.

Inhibition of invasion by N-trans-feruloyloctopamine via AKT, p38MAPK and EMT related signals in hepatocellular carcinoma cells.

N-trans-feruloyloctopamine (FO) isolated from Garlic skin was identified as the primary antioxidant constituents, however, the effect of which on HCC invasion is still unclear. Herein, the FO was synthesized and its antitumor activities were evaluated in HCC cell lines. Cellular functional analyses have revealed that the reformed FO owns strong abilities of inhibiting cell proliferation and invasion in HCC cells. Molecular data have further showed that FO could significantly decrease the phosphorylation levels of Akt and p38 MAPK. In addition, the expression of Slug was inhibited and the level of E-cadherin increased. Molecular docking analysis indicates that the H-bond and hydrophobic interactions were critical for FO and E-cadherin binding, but FO did not seem to act directly on phosphorylated Akt and p38 MAPK. We have thus concluded that reformed FO inhibits cell invasion might be directly through EMT related signals (E-cadherin) and indirectly through PI3K/Akt, p38 MAPK signaling pathways. FO might be a promising drug in HCC treatment and prognosis.

Part-crystalline part-liquid state and rattling-like thermal damping in materials with chemical-bond hierarchy.

Understanding thermal and phonon transport in solids has been of great importance in many disciplines such as thermoelectric materials, which usually requires an extremely low lattice thermal conductivity (LTC). By analyzing the finite-temperature structural and vibrational characteristics of typical thermoelectric compounds such as filled skutterudites and Cu3SbSe3, we demonstrate a concept of part-crystalline part-liquid state in the compounds with chemical-bond hierarchy, in which certain constituent species weakly bond to other part of the crystal. Such a material could intrinsically manifest the coexistence of rigid crystalline sublattices and other fluctuating noncrystalline sublattices with thermally induced large-amplitude vibrations and even flow of the group of species atoms, leading to atomic-level heterogeneity, mixed part-crystalline part-liquid structure, and thus rattling-like thermal damping due to the collective soft-mode vibrations similar to the Boson peak in amorphous materials. The observed abnormal LTC close to the amorphous limit in these materials can only be described by an effective approach that approximately treats the rattling-like damping as a "resonant" phonon scattering.

Pinning down high-performance Cu-chalcogenides as thin-film solar cell absorbers: A successive screening approach.

Photovoltaic performances of Cu-chalcogenides solar cells are strongly correlated with the absorber fundamental properties such as optimal bandgap, desired band alignment with window material, and high photon absorption ability. According to these criteria, we carry out a successive screening for 90 Cu-chalcogenides using efficient theoretical approaches. Besides the well-recognized CuInSe2 and Cu2ZnSnSe4 materials, several novel candidates are identified to have optimal bandgaps of around 1.0-1.5 eV, spike-like band alignments with CdS window layer, sharp photon absorption edges, and high absorption coefficients. These new systems have great potential to be superior absorbers for photovolatic applications if their carrrier transport and defect properties are properly optimized.

In Silico Identification of Protein S-Palmitoylation Sites and Their Involvement in Human Inherited Disease.

S-Palmitoylation is a key regulatory mechanism controlling protein targeting, localization, stability, and activity. Since increasing evidence shows that its disruption is implicated in many human diseases, the identification of palmitoylation sites is attracting more attention. However, the computational methods that are published so far for this purpose have suffered from a poor balance of sensitivity and specificity; hence, it is difficult to get a good generalized prediction ability on an external validation set, which holds back the further analysis of associations between disruption of palmitoylation and human inherited diseases. In this work, we present a reliable identification method for protein S-palmitoylation sites, called SeqPalm, based on a series of newly composed features from protein sequences and the synthetic minority oversampling technique. With only 16 extracted key features, this approach achieves the most favorable prediction performance up to now with sensitivity, specificity, and Matthew's correlation coefficient values of 95.4%, 96.3%, and 0.917, respectively. Then, all known disease-associated variations are studied by SeqPalm. It is found that 243 potential loss or gain of palmitoylation sites are highly associated with human inherited disease. The analysis presents several potential therapeutic targets for inherited diseases associated with loss or gain of palmitoylation function. There are even biological evidence that are coordinate with our prediction results. Therefore, this work presents a novel approach to discover the molecular basis of pathogenesis associated with abnormal palmitoylation. SeqPalm is now available online at http://lishuyan.lzu.edu.cn/seqpalm , which can not only annotate the palmitoylation sites of proteins but also distinguish loss or gain of palmitoylation sites by protein variations.

[Changes of Intestinal Mucosal Barrier and Intestinal Flora in Rats with Severe Acute Pancreatitis].

This paper is to explore changes of intestinal mucosal barrier, intestinal flora, and bacterial translocation in rats with severe acute pancreatitis (SAP). Twenty four male SD rats were randomly divided into the control group (n = 10) and the experimental group (n = 14). The model of severe acute pancreatitis of rats was induced by the method of injecting adversely 5% sodium taurocholate into the common biliary-pancreatic duct. All of the rats were killed after 24 hours and the level of the serum amylase and the plasma endotoxin was determined after that. The pathological changes of pancreas and small intestine were observed through hematoxylin-eosin staining (HE staining) and the abdominal viscera bacterial translocation rates were tested. With the method of real-time polymerase chain reaction (RT-PCR) the quantity of the intestinal flora was analyzed. In the control group, the level of Escherichia coli, Lactobacillus and Bifidobacterium were 2.08 ± 1.29, 11.04 ± 7.55 and 12.21 ± 4.95, respectively. On the contrast, the level of Escherichia coli in the cecum contents was much higher (9.72 ± 3.58, P < 0.01), while the Lactobacillus number was decreased significantly (0.67 ± 0.34, P < 0.01), and the Bifidobacterium number was also decreased (4.59 ± 3.42, P < 0.05) in the experimental group, so the ratio of Bifidobacterium/Escherichia coli was reversed. Besides, in the experimental group, the plasma endotoxin positive rates and the bacterial translocation rates were much higher (P < 0.01 or P < 0.05) and the pathology scores of pancreas and small intestines were also significantly higher (P < 0.01) than those in the control group. These results indicated that in severe acute pancreatitis rats, the intestinal mucosal barrier was severely damaged and the dysbacteriosis occurs in the intestinal canal. And these might relate to the occurrence and development of multiple organ infection.

Electronic structure of antifluorite Cu2X (X = S, Se, Te) within the modified Becke-Johnson potential plus an on-site Coulomb U.

The traditional photon absorbers Cu2-xX (X = S, Se, and Te) have regained significant research attention in the search of earth-abundant photovoltaic materials. These moderate- and narrow-gap materials have also been shown to exhibit excellent thermoelectric properties recently. However, semimetallic band structures with inverted band orderings are predicted for antifluorite structure Cu2X using density functional theory with the local density approximation or the generalized gradient approximation. We find that semiconducting band structures and normal band orderings can be obtained using the modified Becke-Johnson potential plus an on-site Coulomb U (the mBJ+U approach), which is consistent with our earlier finding for diamond-like Cu-based multinary semiconductors [Y. Zhang, J. Zhang, W. Gao, T. A. Abtew, Y. Wang, P. Zhang, and W. Zhang, J. Chem. Phys. 139, 184706 (2013)]. The trend of the chemical bonding of Cu2X is analyzed, which shows that the positions of the valence band maximum and conduction band minimum are strongly affected by the inter-site pd and intra-site sp hybridizations, respectively. The calculated gaps of Cu2S and Cu2Se still seem to be underestimated compared with experimental results. We also discuss the effects of different structural phases and Cu disordering and deficiency on the bandgaps of these materials.

In silico study combining docking and QSAR methods on a series of matrix metalloproteinase 13 inhibitors.

Matrix metalloproteinase 13 (MMP-13) plays an important role in the degradation of articular cartilage and has been considered as an attractive target for the treatment of osteoarthritis; hence, the development of efficient inhibitors of MMP-13 has become a hot study field. Taking a series of carboxylic acid-based MMP-13 inhibitors as research object, this work utilized an extended QSAR method to analyze the structure-activity relationships. We focused on two important topics in QSAR: bioactive conformation and descriptors. Firstly, molecular docking was carried out to dock all molecules into the MMP-13 active site in order to obtain the bioactive conformation. Secondly, based on the docked complex, descriptors characterizing receptor-ligand interactions and the ligand structure were calculated. Thirdly, a genetic algorithm (GA) and multiple linear regression (MLR) were employed to select important descriptors related to inhibitory activities, simultaneously, to build the predictive model. The built model gave satisfactory results with highly accurate fitting and strong external predictive abilities for chemicals not used in model development. Furthermore, the selected descriptors were explored to elucidate important factors influencing the inhibition activities. This study demonstrates that the selection strategy of the docking-guided bioactive conformation is rational and useful in predicting MMP-13 inhibitor activities, and receptor-ligand complex descriptors have an advantage over directly reflecting receptor-ligand interactions.

Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble research.

The aim to investigate the predictive efficacy of automatic breast volume scanner (ABVS), clinical and serological features alone or in combination at model level for predicting HER2 status. The model weighted combination method was developed to identify HER2 status compared with single data source model method and feature combination method. 271 patients with invasive breast cancer were included in the retrospective study, of which 174 patients in our center were randomized into the training and validation sets, and 97 patients in the external center were as the test set. Radiomics features extracted from the ABVS-based tumor, peritumoral 3 mm region, and peritumoral 5 mm region and clinical features were used to construct the four types of the optimal single data source models, Tumor, R3mm, R5mm, and Clinical model, respectively. Then, the model weighted combination and feature combination methods were performed to optimize the combination models. The proposed weighted combination models in predicting HER2 status achieved better performance both in validation set and test set. For the validation set, the single data source model, the feature combination model, and the weighted combination model achieved the highest area under the curve (AUC) of 0.803 (95% confidence interval [CI] 0.660-947), 0.739 (CI 0.556,0.921), and 0.826 (95% CI 0.689,0.962), respectively; with the sensitivity and specificity were 100%, 62.5%; 81.8%, 66.7%; 90.9%,75.0%; respectively. For the test set, the single data source model, the feature combination model, and the weighted combination model attained the best AUC of 0.695 (95% CI 0.583, 0.807), 0.668 (95% CI 0.555,0.782), and 0.700 (95% CI 0.590,0.811), respectively; with the sensitivity and specificity were 86.1%, 41.9%; 61.1%, 71.0%; 86.1%, 41.9%; respectively. The model weighted combination was a better method to construct a combination model. The optimized weighted combination models composed of ABVS-based intratumoral and peritumoral radiomics features and clinical features may be potential biomarkers for the noninvasive and preoperative prediction of HER2 status in breast cancer.

Licraside as novel potent FXR agonist for relieving cholestasis: structure-based drug discovery and biological evaluation studies.

Cholestasis is a common clinical disease caused by a disorder in bile acids (BAs) homeostasis, which promotes its development. The Farnesoid X receptor (FXR) plays a critical role in regulating BAs homeostasis, making it an essential target for cholestasis treatment. Although several active FXR agonists have been identified, effective drugs for cholestasis are still lacking. To address this, a molecular docking-based virtual screening method was used to identify potential FXR agonists. A hierarchical screening strategy was employed to improve the screening accuracy, and six compounds were selected for further evaluation. Dual-luciferase reporter gene assay was used to demonstrate FXR activation by the screened compounds, and their cytotoxicity was then evaluated. Among the compounds, licraside showed the best performance and was selected for in vivo evaluation using an ANIT-induced cholestasis animal model. Results demonstrated that licraside significantly reduced biliary TBA, serum ALT, AST, GGT, ALP, TBIL, and TBA levels. Liver histopathological analysis showed that licraside also had a therapeutic effect on ANIT-induced liver injury. Overall, these findings suggest that licraside is an FXR agonist with potential therapeutic effects on cholestasis. This study provides valuable insights into the development of novel lead compounds from traditional Chinese medicine for cholestasis treatment.

Computational Insights into the Allosteric Modulation of a Phthalate-Degrading Hydrolase by Distal Mutations.

Phthalate esters (PAEs) are a ubiquitous kind of environmental endocrine that disrupt chemicals, causing environmental and health issues. EstJ6 is an effective phthalate-degrading hydrolase, and its mutant with a combination of three non-conservative distal mutations has an improved activity against PAEs with unknown molecular mechanisms. Herein, we attempt to fill the significant gap between distal mutations and the activity of this enzyme using computational approaches. We found that mutations resulted in a redistribution of the enzyme's preexisting conformational states and dynamic changes of key functional regions, especially the lid over the active site. The outward motion of the lid upon the mutations made it easier for substrates or products to enter or exit. Additionally, a stronger substrate binding affinity and conformational rearrangements of catalytic reaction-associated residues in the mutant, accompanied by the strengthened communication within the protein, could synergistically contribute to the elevated catalytic efficiency. Finally, an attempt was made to improve the thermostability of EstJ6 upon introducing a distal disulfide bond between residues A23 and A29, and the simulation results were as expected. Together, our work explored the allosteric effects caused by distal mutations, which could provide insights into the rational design of esterases for industrial applications in the future.

A Critical Review of Machine Learning Techniques on Thermoelectric Materials.

Thermoelectric (TE) materials can directly convert heat to electricity and vice versa and have broad application potential for solid-state power generation and refrigeration. Over the past few decades, efforts have been made to develop new TE materials with high performance. However, traditional experiments and simulations are expensive and time-consuming, limiting the development of new materials. Machine learning (ML) has been increasingly applied to study TE materials in recent years. This paper reviews the recent progress in ML-based TE material research. The application of ML in predicting and optimizing the properties of TE materials, including electrical and thermal transport properties and optimization of functional materials with targeted TE properties, is reviewed. Finally, future research directions are discussed.

Band engineering enhances the electrochemical properties by constructing TiO2 NRs-MoS2 NSFs flexible electrode.

Research and development of flexible electrodes with high performance are crucial to largely determine the performance of flexible lithium-ion batteries (FLIBs) to a large extent. In this work, a flexible anode (TiO2 NRs-MoS2 NSFs/CC) is rationally designed and successfully constructed, in which TiO2 nanorods arrays (NRs) vertically grown on CC as a supporting backbone for MoS2 nanosheets flowers (NSFs) to form a TiO2 NRs-MoS2 NSFs heterostructure. The backbone can not only serve as a mechanical support MoS2 and improve its electronic conductivity, but also limit the dissolution of polysulfides issue during cycling. The density functional theory (DFT) analysis manifests that the obvious interaction between O and S at the interface for the TiO2 NRs-MoS2 NSFs heterostructure changes the electronic structure and reduces the band gap of TiO2 NRs-MoS2 NSFs. The small band gap and high electron state at the Fermi level are both beneficial to the transport of electrons, enhancing the kinetics, and giving the long cycling stability at high density and excellent rate capacity. Furthermore, the assembled TiO2 NRs-MoS2 NSFs/CC//NCM622 full cell delivers superior rate capacity and good cycling stability. Meanwhile, the soft-packed cell shows good mechanical flexibility, which can be lighted up successfully and keep brightness when folding with different angles. This result illustrates that it is a highly potential strategy for constructing flexible electrodes with the controlled electronic structure through band engineering to not only improve the electrochemical performance, but also possibly meet the requirements of high-performance FLIBs.

A sequence-based computational model for the prediction of the solvent accessible surface area for α-helix and ß-barrel transmembrane residues.

Predicting the solvent accessible surface area (ASA) of transmembrane (TM) residues is of great importance for experimental researchers to elucidate diverse physiological processes. TM residues fall into two major structural classes (α-helix membrane protein and ß-barrel membrane protein). The reported solvent ASA prediction models were developed for these two types of TM residues respectively. However, this prevents the general use of these methods because one cannot determine which model is suitable for a given TM residue without information of its type. To conquer this limitation, we developed a new computational model that can be used for predicting the ASA of both TM α-helix and ß-barrel residues. The model was developed from 78 α-helix membrane protein chains and 24 ß-barrel membrane protein. Its prediction ability was evaluated by cross validation method and its prediction result on an independent test set of 20 membrane protein chains. The results show that our model performs well for both types of TM residues and outperforms other prediction model which was developed for the specific type of TM residues. The prediction results also proved that the random forest model incorporating conservation score is an effective sequence-based computational approach for predicting the solvent ASA of TM residues.

Quantitative structure-activity relationship analysis of a series of human renal organic anion transporter inhibitors.

Organic anion transporters (OATs) have been proved to play important roles in the membrane transport of numerous potentially toxic xenobiotics, drugs, and endogenous metabolites. In general, OATs substrates can compete with one another for the transporter to mutually decrease renal secretion and thus delay the clearance and prolong the duration of action of each compound. Such interactions have the potential to bring about adverse outcomes for clinical cases. Therefore, it is very important to assess the molecular bioactivity to inhibit OATs during the development of new drugs and co-administration. In this work, the relationships between 45 chemicals and their corresponding hOAT1 and hOAT3 inhibitory activities were analyzed. The quantitative structure-activity relationship (QSAR) model was developed by genetic algorithm and multiple linear regression method. The predictive power of the proposed model was strictly evaluated, and the applicability domain was also defined. The proposed models were robust and satisfactory and could provide a feasible and effective tool for hOAT1 or hOAT3 inhibitor screening.