Journal of Computer-Aided Molecular Design最新文献

{"title":"TeM-DTBA: time-efficient drug target binding affinity prediction using multiple modalities with Lasso feature selection","authors":"Tanya Liyaqat,&nbsp;Tanvir Ahmad,&nbsp;Chandni Saxena","doi":"10.1007/s10822-023-00533-1","DOIUrl":"10.1007/s10822-023-00533-1","url":null,"abstract":"<div><p>Drug discovery, especially virtual screening and drug repositioning, can be accelerated through deeper understanding and prediction of Drug Target Interactions (DTIs). The advancement of deep learning as well as the time and financial costs associated with conventional wet-lab experiments have made computational methods for DTI prediction more popular. However, the majority of these computational methods handle the DTI problem as a binary classification task, ignoring the quantitative binding affinity that determines the drug efficacy to their target proteins. Moreover, computational space as well as execution time of the model is often ignored over accuracy. To address these challenges, we introduce a novel method, called Time-efficient Multimodal Drug Target Binding Affinity (TeM-DTBA), which predicts the binding affinity between drugs and targets by fusing different modalities based on compound structures and target sequences. We employ the Lasso feature selection method, which lowers the dimensionality of feature vectors and speeds up the proposed model training time by more than 50%. The results from two benchmark datasets demonstrate that our method outperforms state-of-the-art methods in terms of performance. The mean squared errors of 18.8% and 23.19%, achieved on the KIBA and Davis datasets, respectively, suggest that our method is more accurate in predicting drug-target binding affinity.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 12","pages":"573 - 584"},"PeriodicalIF":3.5,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41104275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Conformational energies of reference organic molecules: benchmarking of common efficient computational methods against coupled cluster theory","authors":"Ioannis Stylianakis,&nbsp;Nikolaos Zervos,&nbsp;Jenn-Huei Lii,&nbsp;Dimitrios A. Pantazis,&nbsp;Antonios Kolocouris","doi":"10.1007/s10822-023-00531-3","DOIUrl":"10.1007/s10822-023-00531-3","url":null,"abstract":"","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 12","pages":"657 - 657"},"PeriodicalIF":3.5,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41100576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the accuracy of the FMO binding affinity prediction of ligand-receptor complexes containing metals","authors":"R. Paciotti,&nbsp;A. Marrone,&nbsp;C. Coletti,&nbsp;N. Re","doi":"10.1007/s10822-023-00532-2","DOIUrl":"10.1007/s10822-023-00532-2","url":null,"abstract":"<div><p>Polarization and charge transfer strongly characterize the ligand-receptor interaction when metal atoms are present, as for the Au(I)-biscarbene/DNA G-quadruplex complexes. In a previous work (<i>J Comput Aided Mol Des</i>2022, 36, 851–866) we used the ab initio FMO2 method at the RI-MP2/6-31G* level of theory with the PCM [1] solvation approach to calculate the binding energy (<i>ΔE</i>^<i>FMO</i>) of two Au(I)-biscarbene derivatives, [Au(9-methylcaffein-8-ylidene)₂]⁺ and [Au(1,3-dimethylbenzimidazole-2-ylidene)₂]⁺, able to interact with DNA G-quadruplex motif. We found that <i>ΔE</i>^<i>FMO</i> and ligand-receptor pair interaction energies (<i>E</i>^<i>INT</i>) show very large negative values making the direct comparison with experimental data difficult and related this issue to the overestimation of the embedded charge transfer energy between fragments containing metal atoms. In this work, to improve the accuracy of the FMO method for predicting the binding affinity of metal-based ligands interacting with DNA G-quadruplex (Gq), we assess the effect of the following computational features: <i>(i)</i> the electron correlation, considering the Hartree–Fock (HF) and a post-HF method, namely RI-MP2; <i>(ii)</i> the two (FMO2) and three-body (FMO3) approaches; <i>(iii)</i> the basis set size (polarization functions and double-ζ vs. triple-ζ) and <i>(iv)</i> the embedding electrostatic potential (ESP). Moreover, the partial screening method was systematically adopted to simulate the solvent screening effect for each calculation. We found that the use of the ESP computed using the screened point charges for all atoms (ESP-SPTC) has a critical impact on the accuracy of both <i>ΔE</i>^<i>FMO</i> and <i>E</i>^<i>INT</i>, eliminating the overestimation of charge transfer energy and leading to energy values with magnitude comparable with typical experimental binding energies. With this computational approach, E^INT values describe the binding efficiency of metal-based binders to DNA Gq more accurately than <i>ΔE</i>^<i>FMO</i>. Therefore, to study the binding process of metal containing systems with the FMO method, the adoption of partial screening solvent method combined with ESP-SPCT should be considered. This computational protocol is suggested for FMO calculations on biological systems containing metals, especially when the adoption of the default ESP treatment leads to questionable results.\u0000</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 12","pages":"707 - 719"},"PeriodicalIF":3.5,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41094051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring DrugCentral: from molecular structures to clinical effects","authors":"Liliana Halip,&nbsp;Sorin Avram,&nbsp;Ramona Curpan,&nbsp;Ana Borota,&nbsp;Alina Bora,&nbsp;Cristian Bologa,&nbsp;Tudor I. Oprea","doi":"10.1007/s10822-023-00529-x","DOIUrl":"10.1007/s10822-023-00529-x","url":null,"abstract":"<div><p>DrugCentral, accessible at https://drugcentral.org, is an open-access online drug information repository. It covers over 4950 drugs, incorporating structural, physicochemical, and pharmacological details to support drug discovery, development, and repositioning. With around 20,000 bioactivity data points, manual curation enhances information from several major digital sources. Approximately 724 mechanism-of-action (MoA) targets offer updated drug target insights. The platform captures clinical data: over 14,300 on- and off-label uses, 27,000 contraindications, and around 340,000 adverse drug events from pharmacovigilance reports. DrugCentral encompasses information from molecular structures to marketed formulations, providing a comprehensive pharmaceutical reference. Users can easily navigate basic drug information and key features, making DrugCentral a versatile, unique resource. Furthermore, we present a use-case example where we utilize experimentally determined data from DrugCentral to support drug repurposing. A minimum activity threshold <i>t</i> should be considered against novel targets to repurpose a drug. Analyzing 1156 bioactivities for human MoA targets suggests a general threshold of 1 µM: <i>t</i> = 6 when expressed as − log[Activity(M)]). This applies to 87% of the drugs. Moreover, <i>t</i> can be refined empirically based on water solubility (S): <i>t</i> = 3 − logS, for logS &lt; − 3. Alongside the drug repurposing classification scheme, which considers intellectual property rights, market exclusivity protections, and market accessibility, DrugCentral provides valuable data to prioritize candidates for drug repurposing programs efficiently.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 12","pages":"681 - 694"},"PeriodicalIF":3.5,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10236569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovery of novel and potent InhA direct inhibitors by ensemble docking-based virtual screening and biological assays","authors":"Qianqian Zhang,&nbsp;Jianting Han,&nbsp;Yongchang Zhu,&nbsp;Fansen Yu,&nbsp;Xiaopeng Hu,&nbsp;Henry H. Y. Tong,&nbsp;Huanxiang Liu","doi":"10.1007/s10822-023-00530-4","DOIUrl":"10.1007/s10822-023-00530-4","url":null,"abstract":"<div><p>Multidrug-resistant tuberculosis (MDR-TB) continues to spread worldwide and remains one of the leading causes of death among infectious diseases. The enoyl-acyl carrier protein reductase (InhA) belongs to FAS-II family and is essential for the formation of the Mycobacterium tuberculosis cell wall. Recent years, InhA direct inhibitors have been extensively studied to overcome MDR-TB. However, there are still no inhibitors that have entered clinical research. Here, the ensemble docking-based virtual screening along with biological assay were used to identify potent InhA direct inhibitors from Chembridge, Chemdiv, and Specs. Ultimately, 34 compounds were purchased and first assayed for the binding affinity, of which four compounds can bind InhA well with K_D values ranging from 48.4 to 56.2 µM. Among them, compound 9,222,034 has the best inhibitory activity against InhA enzyme with an IC₅₀ value of 18.05 µM. In addition, the molecular dynamic simulation and binding free energy calculation indicate that the identified compounds bind to InhA with “extended” conformation. Residue energy decomposition shows that residues such as Tyr158, Met161, and Met191 have higher energy contributions in the binding of compounds. By analyzing the binding modes, we found that these compounds can bind to a hydrophobic sub-pocket formed by residues Tyr158, Phe149, Ile215, Leu218, etc., resulting in extensive van der Waals interactions. In summary, this study proposed an efficient strategy for discovering InhA direct inhibitors through ensemble docking-based virtual screening, and finally identified four active compounds with new skeletons, which can provide valuable information for the discovery and optimization of InhA direct inhibitors.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 12","pages":"695 - 706"},"PeriodicalIF":3.5,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10112751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ChemFlow_py: a flexible toolkit for docking and rescoring","authors":"Luca Monari,&nbsp;Katia Galentino,&nbsp;Marco Cecchini","doi":"10.1007/s10822-023-00527-z","DOIUrl":"10.1007/s10822-023-00527-z","url":null,"abstract":"<div><p>The design of accurate virtual screening tools is an open challenge in drug discovery. Several structure-based methods have been developed at different levels of approximation. Among them, molecular docking is an established technique with high efficiency, but typically low accuracy. Moreover, docking performances are known to be target-dependent, which makes the choice of the docking program and corresponding scoring function critical when approaching a new protein target. To compare the performances of different docking protocols, we developed ChemFlow_py, an automated tool to perform docking and rescoring. Using four protein systems extracted from DUD-E with 100 known active compounds and 3000 decoys per target, we compared the performances of several rescoring strategies including consensus scoring. We found that the average docking results can be improved by consensus ranking, which emphasizes the relevance of consensus scoring when little or no chemical information is available for a given target. ChemFlow_py is a free toolkit to optimize the performances of virtual high-throughput screening (vHTS). The software is publicly available at https://github.com/IFMlab/ChemFlow_py.</p><h3>Graphical abstract</h3>\u0000 <figure><div><div><div><picture><source><img></source></picture></div></div></div></figure>\u0000 </div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 11","pages":"565 - 572"},"PeriodicalIF":3.5,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-023-00527-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"6727403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conformational energies of reference organic molecules: benchmarking of common efficient computational methods against coupled cluster theory","authors":"Ioannis Stylianakis,&nbsp;Nikolaos Zervos,&nbsp;Jenn-Huei Lii,&nbsp;Dimitrios A. Pantazis,&nbsp;Antonios Kolocouris","doi":"10.1007/s10822-023-00513-5","DOIUrl":"10.1007/s10822-023-00513-5","url":null,"abstract":"<div><p>We selected 145 reference organic molecules that include model fragments used in computer-aided drug design. We calculated 158 conformational energies and barriers using force fields, with wide applicability in commercial and free softwares and extensive application on the calculation of conformational energies of organic molecules, e.g. the UFF and DREIDING force fields, the Allinger’s force fields MM3-96, MM3-00, MM4-8, the MM2-91 clones MMX and MM+, the MMFF94 force field, MM4, ab initio Hartree–Fock (HF) theory with different basis sets, the standard density functional theory B3LYP, the second-order post-HF MP2 theory and the Domain-based Local Pair Natural Orbital Coupled Cluster DLPNO-CCSD(T) theory, with the latter used for accurate reference values. The data set of the organic molecules includes hydrocarbons, haloalkanes, conjugated compounds, and oxygen-, nitrogen-, phosphorus- and sulphur-containing compounds. We reviewed in detail the conformational aspects of these model organic molecules providing the current understanding of the steric and electronic factors that determine the stability of low energy conformers and the literature including previous experimental observations and calculated findings. While progress on the computer hardware allows the calculations of thousands of conformations for later use in drug design projects, this study is an update from previous classical studies that used, as reference values, experimental ones using a variety of methods and different environments. The lowest mean error against the DLPNO-CCSD(T) reference was calculated for MP2 (0.35 kcal mol⁻¹), followed by B3LYP (0.69 kcal mol⁻¹) and the HF theories (0.81–1.0 kcal mol⁻¹). As regards the force fields, the lowest errors were observed for the Allinger’s force fields MM3-00 (1.28 kcal mol⁻¹), ΜΜ3-96 (1.40 kcal mol⁻¹) and the Halgren’s MMFF94 force field (1.30 kcal mol⁻¹) and then for the MM2-91 clones MMX (1.77 kcal mol⁻¹) and MM+ (2.01 kcal mol⁻¹) and MM4 (2.05 kcal mol⁻¹). The DREIDING (3.63 kcal mol⁻¹) and UFF (3.77 kcal mol⁻¹) force fields have the lowest performance. These model organic molecules we used are often present as fragments in drug-like molecules. The values calculated using DLPNO-CCSD(T) make up a valuable data set for further comparisons and for improved force field parameterization.</p><h3>Graphical abstract</h3>\u0000 <div><figure><div><div><picture><source><img></source></picture></div></div></figure></div>\u0000 </div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 12","pages":"607 - 656"},"PeriodicalIF":3.5,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10027087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study","authors":"Nour Jamal Jaradat,&nbsp;Mamon Hatmal,&nbsp;Dana Alqudah,&nbsp;Mutasem Omar Taha","doi":"10.1007/s10822-023-00528-y","DOIUrl":"10.1007/s10822-023-00528-y","url":null,"abstract":"<div><p>STAT3 belongs to a family of seven transcription factors. It plays an important role in activating the transcription of various genes involved in a variety of cellular processes. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. However, since STAT3 inhibitors bind to the shallow SH2 domain of the protein, it is expected that hydration water molecules play significant role in ligand-binding complicating the discovery of potent binders. To remedy this issue, we herein propose to extract pharmacophores from molecular dynamics (MD) frames of a potent co-crystallized ligand complexed within STAT3 SH2 domain. Subsequently, we employ genetic function algorithm coupled with machine learning (GFA-ML) to explore the optimal combination of MD-derived pharmacophores that can account for the variations in bioactivity among a list of inhibitors. To enhance the dataset, the training and testing lists were augmented nearly a 100-fold by considering multiple conformers of the ligands. A single significant pharmacophore emerged after 188 ns of MD simulation to represent STAT3-ligand binding. Screening the National Cancer Institute (NCI) database with this model identified one low micromolar inhibitor most likely binds to the SH2 domain of STAT3 and inhibits this pathway.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 12","pages":"659 - 678"},"PeriodicalIF":3.5,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10018108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A least-squares-fitting procedure for an efficient preclinical ranking of passive transport across the blood–brain barrier endothelium","authors":"Christian Jorgensen,&nbsp;Evan P. Troendle,&nbsp;Jakob P. Ulmschneider,&nbsp;Peter C. Searson,&nbsp;Martin B. Ulmschneider","doi":"10.1007/s10822-023-00525-1","DOIUrl":"10.1007/s10822-023-00525-1","url":null,"abstract":"<div><p>The treatment of various disorders of the central nervous system (CNS) is often impeded by the limited brain exposure of drugs, which is regulated by the human blood–brain barrier (BBB). The screening of lead compounds for CNS penetration is challenging due to the biochemical complexity of the BBB, while experimental determination of permeability is not feasible for all types of compounds. Here we present a novel method for rapid preclinical screening of libraries of compounds by utilizing advancements in computing hardware, with its foundation in transition-based counting of the flux. This method has been experimentally validated for in vitro permeabilities and provides atomic-level insights into transport mechanisms. Our approach only requires a single high-temperature simulation to rank a compound relative to a library, with a typical simulation time converging within 24 to 72 h. The method offers unbiased thermodynamic and kinetic information to interpret the passive transport of small-molecule drugs across the BBB.</p><h3>Graphical abstract</h3>\u0000 <figure><div><div><div><picture><source><img></source></picture></div></div></div></figure>\u0000 </div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 11","pages":"537 - 549"},"PeriodicalIF":3.5,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-023-00525-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"6727392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation","authors":"Youjin Xiong,&nbsp;Yiqing Wang,&nbsp;Yisheng Wang,&nbsp;Chenmei Li,&nbsp;Peng Yusong,&nbsp;Junyu Wu,&nbsp;Yiqing Wang,&nbsp;Lingyun Gu,&nbsp;Christopher J. Butch","doi":"10.1007/s10822-023-00523-3","DOIUrl":"10.1007/s10822-023-00523-3","url":null,"abstract":"<div><p>Generative approaches to molecular design are an area of intense study in recent years as a method to generate new pharmaceuticals with desired properties. Often though, these types of efforts are constrained by limited experimental activity data, resulting in either models that generate molecules with poor performance or models that are overfit and produce close analogs of known molecules. In this paper, we reduce this data dependency for the generation of new chemotypes by incorporating docking scores of known and de novo molecules to expand the applicability domain of the reward function and diversify the compounds generated during reinforcement learning. Our approach employs a deep generative model initially trained using a combination of limited known drug activity and an approximate docking score provided by a second machine learned Bayes regression model, with final evaluation of high scoring compounds by a full docking simulation. This strategy results in molecules with docking scores improved by 10–20% compared to molecules of similar size, while being 130 × faster than a docking only approach on a typical GPU workstation. We also show that the increased docking scores correlate with (1) docking poses with interactions similar to known inhibitors and (2) result in higher MM-GBSA binding energies comparable to the energies of known DDR1 inhibitors, demonstrating that the Bayesian model contains sufficient information for the network to learn to efficiently interact with the binding pocket during reinforcement learning. This outcome shows that the combination of the learned latent molecular representation along with the feature-based docking regression is sufficient for reinforcement learning to infer the relationship between the molecules and the receptor binding site, which suggest that our method can be a powerful tool for the discovery of new chemotypes with potential therapeutic applications.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 11","pages":"507 - 517"},"PeriodicalIF":3.5,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-023-00523-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"6727405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}