Journal of Computer-Aided Molecular Design最新文献

{"title":"Examining unsupervised ensemble learning using spectroscopy data of organic compounds","authors":"Kedan He,&nbsp;Djenerly G. Massena","doi":"10.1007/s10822-022-00488-9","DOIUrl":"10.1007/s10822-022-00488-9","url":null,"abstract":"<div><p>One solution to the challenge of choosing an appropriate clustering algorithm is to combine different clusterings into a single consensus clustering result, known as cluster ensemble (CE). This ensemble learning strategy can provide more robust and stable solutions across different domains and datasets. Unfortunately, not all clusterings in the ensemble contribute to the final data partition. Cluster ensemble selection (CES) aims at selecting a subset from a large library of clustering solutions to form a smaller cluster ensemble that performs as well as or better than the set of all available clustering solutions. In this paper, we investigate four CES methods for the categorization of structurally distinct organic compounds using high-dimensional IR and Raman spectroscopy data. Single quality selection (SQI) forms a subset of the ensemble by selecting the highest quality ensemble members. The Single Quality Selection (SQI) method is used with various quality indices to select subsets by including the highest quality ensemble members. The Bagging method, usually applied in supervised learning, ranks ensemble members by calculating the normalized mutual information (NMI) between ensemble members and consensus solutions generated from a randomly sampled subset of the full ensemble. The hierarchical cluster and select method (HCAS-SQI) uses the diversity matrix of ensemble members to select a diverse set of ensemble members with the highest quality. Furthermore, a combining strategy can be used to combine subsets selected using multiple quality indices (HCAS-MQI) for the refinement of clustering solutions in the ensemble. The IR + Raman hybrid ensemble library is created by merging two complementary “views” of the organic compounds. This inherently more diverse library gives the best full ensemble consensus results. Overall, the Bagging method is recommended because it provides the most robust results that are better than or comparable to the full ensemble consensus solutions.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4840927","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":"Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host–guest binding: II. regression and dielectric constant","authors":"Xiao Liu,&nbsp;Lei Zheng,&nbsp;Yalong Cong,&nbsp;Zhihao Gong,&nbsp;Zhixiang Yin,&nbsp;John Z. H. Zhang,&nbsp;Zhirong Liu,&nbsp;Zhaoxi Sun","doi":"10.1007/s10822-022-00487-w","DOIUrl":"10.1007/s10822-022-00487-w","url":null,"abstract":"<div><p>End-point free energy calculations as a powerful tool have been widely applied in protein–ligand and protein–protein interactions. It is often recognized that these end-point techniques serve as an option of intermediate accuracy and computational cost compared with more rigorous statistical mechanic models (e.g., alchemical transformation) and coarser molecular docking. However, it is observed that this intermediate level of accuracy does not hold in relatively simple and prototypical host–guest systems. Specifically, in our previous work investigating a set of carboxylated-pillar[6]arene host–guest complexes, end-point methods provide free energy estimates deviating significantly from the experimental reference, and the rank of binding affinities is also incorrectly computed. These observations suggest the unsuitability and inapplicability of standard end-point free energy techniques in host–guest systems, and alteration and development are required to make them practically usable. In this work, we consider two ways to improve the performance of end-point techniques. The first one is the PBSA_E regression that varies the weights of different free energy terms in the end-point calculation procedure, while the second one is considering the interior dielectric constant as an additional variable in the end-point equation. By detailed investigation of the calculation procedure and the simulation outcome, we prove that these two treatments (i.e., regression and dielectric constant) are manipulating the end-point equation in a somehow similar way, i.e., weakening the electrostatic contribution and strengthening the non-polar terms, although there are still many detailed differences between these two methods. With the trained end-point scheme, the RMSE of the computed affinities is improved from the standard ~ 12 kcal/mol to ~ 2.4 kcal/mol, which is comparable to another altered end-point method (ELIE) trained with system-specific data. By tuning PBSA_E weighting factors with the host-specific data, it is possible to further decrease the prediction error to ~ 2.1 kcal/mol. These observations along with the extremely efficient optimized-structure computation procedure suggest the regression (i.e., PBSA_E as well as its GBSA_E extension) as a practically applicable solution that brings end-point methods back into the library of usable tools for host–guest binding. However, the dielectric-constant-variable scheme cannot effectively minimize the experiment-calculation discrepancy for absolute binding affinities, but is able to improve the calculation of affinity ranks. This phenomenon is somehow different from the protein–ligand case and suggests the difference between host–guest and biomacromolecular (protein–ligand and protein–protein) systems. Therefore, the spectrum of tools usable for protein–ligand complexes could be unsuitable for host–guest binding, and numerical validations are necessary to screen out really workable solutions in these ‘pr","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4702276","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":"Reliable gas-phase tautomer equilibria of drug-like molecule scaffolds and the issue of continuum solvation","authors":"Andreas H. Göller","doi":"10.1007/s10822-022-00480-3","DOIUrl":"10.1007/s10822-022-00480-3","url":null,"abstract":"<div><p>Accurate calculation of relative tautomer energies in different environments is a prerequisite to many parameters of relevance in drug discovery. This work provides a thorough benchmark of the semiempirical methods AM1, PM3 and GFN2-xTB, the force-field OPLS4, Hartree–Fock and HF-3c, the density functionals PBEh-3c, B97-3c, r2SCAN-3c, PBE, PBE0, TPSS, r2SCAN, ω-B97X-V, M06-2X, B3LYP, B2PLYP, and second-order perturbation theory MP2 versus the gold-standard coupled-cluster DLPNO-CCSD(T) using the def2-QZVPP basis set. The outperforming method identified is M06-2X, whereas r2SCAN-3c is the best-perfoming one in the set of cost-optimized methods. Application of the two methods on a challenging subset from the SAMPL2 challenge provides evidence that deviations from experiment are caused by deficiencies of current continuum solvation methods.\u0000</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4099831","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":"The FMO2 analysis of the ligand-receptor binding energy: the Biscarbene-Gold(I)/DNA G-Quadruplex case study","authors":"Roberto Paciotti,&nbsp;Cecilia Coletti,&nbsp;Alessandro Marrone,&nbsp;Nazzareno Re","doi":"10.1007/s10822-022-00484-z","DOIUrl":"10.1007/s10822-022-00484-z","url":null,"abstract":"<div><p>In this work, the ab initio fragment molecular orbital (FMO) method was applied to calculate and analyze the binding energy of two biscarbene-Au(I) derivatives, [Au(9-methylcaffein-8-ylidene)₂]⁺ and [Au(1,3-dimethylbenzimidazol-2-ylidene)₂]⁺, to the DNA G-Quadruplex structure. The FMO2 binding energy considers the ligand-receptor complex as well as the isolated forms of energy-minimum state of ligand and receptor, providing a better description of ligand-receptor affinity compared with simple pair interaction energies (PIE). Our results highlight important features of the binding process of biscarbene-Au(I) derivatives to DNA G-Quadruplex, indicating that the total deformation-polarization energy and desolvation penalty of the ligands are the main terms destabilizing the binding. The pair interaction energy decomposition analysis (PIEDA) between ligand and nucleobases suggest that the main interaction terms are electrostatic and charge-transfer energies supporting the hypothesis that Au(I) ion can be involved in π-cation interactions further stabilizing the ligand-receptor complex. Moreover, the presence of polar groups on the carbene ring, as C = O, can improve the charge-transfer interaction with K⁺ ion. These findings can be employed to design new powerful biscarbene-Au(I) DNA-G quadruplex binders as promising anticancer drugs. The procedure described in this work can be applied to investigate any ligand-receptor system and is particularly useful when the binding process is strongly characterized by polarization, charge-transfer and dispersion interactions, properly evaluated by ab initio methods.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-022-00484-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4051635","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":"From oncoproteins to spike proteins: the evaluation of intramolecular stability using hydropathic force field","authors":"Federica Agosta,&nbsp;Glen E. Kellogg,&nbsp;Pietro Cozzini","doi":"10.1007/s10822-022-00477-y","DOIUrl":"10.1007/s10822-022-00477-y","url":null,"abstract":"<div><p>Evaluation of the intramolecular stability of proteins plays a key role in the comprehension of their biological behavior and mechanism of action. Small structural alterations such as mutations induced by single nucleotide polymorphism can impact biological activity and pharmacological modulation. Covid-19 mutations, that affect viral replication and the susceptibility to antibody neutralization, and the action of antiviral drugs, are just one example. In this work, the intramolecular stability of mutated proteins, like Spike glycoprotein and its complexes with the human target, is evaluated through hydropathic intramolecular energy scoring originally conceived by Abraham and Kellogg based on the “Extension of the fragment method to calculate amino acid zwitterion and side-chain partition coefficients” by Abraham and Leo in <i>Proteins</i>: <i>Struct. Funct. Genet.</i> 1987, 2:130 − 52. HINT is proposed as a fast and reliable tool for the stability evaluation of any mutated system. This work has been written in honor of Prof. Donald J. Abraham (1936–2021).</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-022-00477-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5188513","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":"Physicochemical QSAR analysis of hERG inhibition revisited: towards a quantitative potency prediction","authors":"Kiril Lanevskij,&nbsp;Remigijus Didziapetris,&nbsp;Andrius Sazonovas","doi":"10.1007/s10822-022-00483-0","DOIUrl":"10.1007/s10822-022-00483-0","url":null,"abstract":"<div><p>In an earlier study (Didziapetris R &amp; Lanevskij K (2016). J Comput Aided Mol Des. 30:1175–1188) we collected a database of publicly available hERG inhibition data for almost 6700 drug-like molecules and built a probabilistic Gradient Boosting classifier with a minimal set of physicochemical descriptors (log <i>P</i>, p<i>K</i>_a, molecular size and topology parameters). This approach favored interpretability over statistical performance but still achieved an overall classification accuracy of 75%. In the current follow-up work we expanded the database (provided in Supplementary Information) to almost 9400 molecules and performed temporal validation of the model on a set of novel chemicals from recently published lead optimization projects. Validation results showed almost no performance degradation compared to the original study. Additionally, we rebuilt the model using AFT (Accelerated Failure Time) learning objective in XGBoost, which accepts both quantitative and censored data often reported in protein inhibition studies. The new model achieved a similar level of accuracy of discerning hERG blockers from non-blockers at 10 µM threshold, which can be conceived as close to the performance ceiling for methods aiming to describe only non-specific ligand interactions with hERG. Yet, this model outputs quantitative potency values (<i>IC</i>₅₀) and is not tied to a particular classification cut-off. p<i>IC</i>₅₀ from patch-clamp measurements can be predicted with R² ≈ 0.4 and MAE &lt; 0.5, which enables ligand ranking according to their expected potency levels. The employed approach can be valuable for quantitative modeling of various ADME and drug safety endpoints with a high prevalence of censored data.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-022-00483-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5098910","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":"Improved prediction and characterization of blood-brain barrier penetrating peptides using estimated propensity scores of dipeptides","authors":"Phasit Charoenkwan,&nbsp;Pramote Chumnanpuen,&nbsp;Nalini Schaduangrat,&nbsp;Pietro Lio’,&nbsp;Mohammad Ali Moni,&nbsp;Watshara Shoombuatong","doi":"10.1007/s10822-022-00476-z","DOIUrl":"10.1007/s10822-022-00476-z","url":null,"abstract":"<div><p>The blood-brain barrier (BBB) is the primary barrier with a highly selective semipermeable border between blood vascular endothelial cells and the central nervous system. Since BBB can prevent drugs circulating in the blood from crossing into the interstitial fluid of the brain where neurons reside, many researchers are working hard on developing drug delivery systems to penetrate the BBB which currently poses a challenge. Thus, blood-brain barrier penetrating peptides (B3PPs) are an alternative neurotherapeutic for brain-related disorder since they can facilitate drug delivery into the brain. In the meanwhile, developing computational methods that are effective for both the identification and characterization of B3PPs in a cost-effective manner plays an important role for basic reach and in the pharmaceutical industry. Even though few computational methods for B3PP identification have been developed, their performance might fail in terms of generalization ability and interpretability. In this study, a novel and efficient scoring card method-based predictor (termed SCMB3PP) is presented for improving B3PP identification and characterization. To overcome the limitation of black-box computational approaches, the SCMB3PP predictor can automatically estimate amino acid and dipeptide propensities to be B3PPs. Both cross-validation and independent tests indicate that SCMB3PP can achieve impressive performance and outperform various popular machine learning-based methods and the existing methods on multiple independent test datasets. Furthermore, SCMB3PP-derived amino acid propensities were utilized to identify informative biophysical and biochemical properties for characterizing B3PPs. Finally, an online user-friendly web server (http://pmlabstack.pythonanywhere.com/SCMB3PP) is established to identify novel and potential B3PP cost-effectively. This novel computational approach is anticipated to facilitate the large-scale identification of high potential B3PP candidates for follow-up experimental validation.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5023182","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":"Enabling data-limited chemical bioactivity predictions through deep neural network transfer learning","authors":"Ruifeng Liu,&nbsp;Srinivas Laxminarayan,&nbsp;Jaques Reifman,&nbsp;Anders Wallqvist","doi":"10.1007/s10822-022-00486-x","DOIUrl":"10.1007/s10822-022-00486-x","url":null,"abstract":"<p>The main limitation in developing deep neural network (DNN) models to predict bioactivity properties of chemicals is the lack of sufficient assay data to train the network’s classification layers. Focusing on feedforward DNNs that use atom- and bond-based structural fingerprints as input, we examined whether layers of a fully trained DNN based on large amounts of data to predict one property could be used to develop DNNs to predict other related or unrelated properties based on limited amounts of data. Hence, we assessed if and under what conditions the dense layers of a pre-trained DNN could be transferred and used for the development of another DNN associated with limited training data. We carried out a quantitative study employing more than 400 pairs of assay datasets, where we used fully trained layers from a large dataset to augment the training of a small dataset. We found that the higher the correlation <i>r</i> between two assay datasets, the more efficient the transfer learning is in reducing prediction errors associated with the smaller dataset DNN predictions. The reduction in mean squared prediction errors ranged from 10 to 20% for every 0.1 increase in <i>r</i>² between the datasets, with the bulk of the error reductions associated with transfers of the first dense layer. Transfer of other dense layers did not result in additional benefits, suggesting that deeper, dense layers conveyed more specialized and assay-specific information. Importantly, depending on the dataset correlation, training sample size could be reduced by up to tenfold without any loss of prediction accuracy.</p>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-022-00486-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5177069","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":"Protocol for iterative optimization of modified peptides bound to protein targets","authors":"Rodrigo Ochoa,&nbsp;Pilar Cossio,&nbsp;Thomas Fox","doi":"10.1007/s10822-022-00482-1","DOIUrl":"10.1007/s10822-022-00482-1","url":null,"abstract":"<div><p>Peptides are commonly used as therapeutic agents. However, they suffer from easy degradation and instability. Replacing natural by non-natural amino acids can avoid these problems, and potentially improve the affinity towards the target protein. Here, we present a computational pipeline to optimize peptides based on adding non-natural amino acids while improving their binding affinity. The workflow is an iterative computational evolution algorithm, inspired by the PARCE protocol, that performs single-point mutations on the peptide sequence using modules from the Rosetta framework. The modifications can be guided based on the structural properties or previous knowledge of the biological system. At each mutation step, the affinity to the protein is estimated by sampling the complex conformations and applying a consensus metric using various open protein-ligand scoring functions. The mutations are accepted based on the score differences, allowing for an iterative optimization of the initial peptide. The sampling/scoring scheme was benchmarked with a set of protein-peptide complexes where experimental affinity values have been reported. In addition, a basic application using a known protein-peptide complex is also provided. The structure- and dynamic-based approach allows users to optimize bound peptides, with the option to personalize the code for further applications. The protocol, called mPARCE, is available at: https://github.com/rochoa85/mPARCE/.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-022-00482-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5069090","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":"An overview of the SAMPL8 host–guest binding challenge","authors":"Martin Amezcua,&nbsp;Jeffry Setiadi,&nbsp;Yunhui Ge,&nbsp;David L. Mobley","doi":"10.1007/s10822-022-00462-5","DOIUrl":"10.1007/s10822-022-00462-5","url":null,"abstract":"<div><p>The SAMPL series of challenges aim to focus the community on specific modeling challenges, while testing and hopefully driving progress of computational methods to help guide pharmaceutical drug discovery. In this study, we report on the results of the SAMPL8 host–guest blind challenge for predicting absolute binding affinities. SAMPL8 focused on two host–guest datasets, one involving the cucurbituril CB8 (with a series of common drugs of abuse) and another involving two different Gibb deep-cavity cavitands. The latter dataset involved a previously featured deep cavity cavitand (TEMOA) as well as a new variant (TEETOA), both binding to a series of relatively rigid fragment-like guests. Challenge participants employed a reasonably wide variety of methods, though many of these were based on molecular simulations, and predictive accuracy was mixed. As in some previous SAMPL iterations (SAMPL6 and SAMPL7), we found that one approach to achieve greater accuracy was to apply empirical corrections to the binding free energy predictions, taking advantage of prior data on binding to these hosts. Another approach which performed well was a hybrid MD-based approach with reweighting to a force matched QM potential. In the cavitand challenge, an alchemical method using the AMOEBA-polarizable force field achieved the best success with RMSE less than 1 kcal/mol, while another alchemical approach (<i>ATM/GAFF2-AM1BCC/TIP3P/HREM</i>) had RMSE less than 1.75 kcal/mol. The work discussed here also highlights several important lessons; for example, retrospective studies of reference calculations demonstrate the sensitivity of predicted binding free energies to ethyl group sampling and/or guest starting pose, providing guidance to help improve future studies on these systems.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-022-00462-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4589337","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}