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57 results about "Protein ligand" patented technology
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In biochemistry, a protein ligand is an atom, a molecule or an ion which can bind to a specific site on a protein. Alternative names used to mean a protein ligand are affinity reagents or protein binders. To date, antibodies are the most widely used protein ligands in life-science investigations, however, other molecules such as protein scaffolds, nucleic acids, peptides are also being used. Main methods to study protein–ligand interactions are principal hydrodynamic and calorimetric techniques, and principal spectroscopic and structural methods such as Fourier transform spectroscopy Raman spectroscopy Fluorescence spectroscopy Circular dichroism Nuclear magnetic resonance Mass spectrometry Atomic force microscope Paramagnetic probes Dual Polarisation Interferometry Other techniques include: fluorescence intensity, bimolecular fluorescence complementation, FRET / FRET quenching surface plasmon resonance, Bio-Layer Interferometry, Coimmunopreciptation indirect ELIS, equilibrium dialysis, gel electrophoresis, far western blot, fluorescence polarization anisotropy, electron paramagnetic resonance, Microscale Thermophoresis
Protein-ligand interaction prediction method and related device
The embodiment of the invention discloses a protein-ligand interaction prediction method and a related device. Determining a prediction interaction pair formed by atoms of the protein and atoms of the ligand molecule according to the data of the protein and the data of the ligand molecule, establishing a loss function according to the node characteristics and the edge characteristics of the real interaction pair and the node characteristics and the edge characteristics of the prediction interaction pair, and adjusting model parameters according to the loss function, the target neural network model can be used for predicting the interaction between any protein and ligand molecules. Physical priori knowledge of protein-ligand interaction is fused in model training and prediction, so that the model can learn characteristics with more biological significance, and the predicted biological correlation is improved. According to the method, the advantages of deep learning are utilized, high-dimensional features are automatically extracted, complex pattern recognition is carried out, the complex relation in protein-ligand interaction is captured, and the stability and accuracy of protein-ligand interaction prediction are improved.
Owner:SHENZHEN READLINE BIOTECH CO LTD
Compounds containing fibroblast activation protein ligands and uses thereof
ActiveJP7783396B2Bioreactor/fermenter combinationsHeavy metal active ingredientsFibroblastBiochemistry
To provide a compound comprising a cyclic peptide and a chelator, and its use.SOLUTION: The present invention provides a compound selected from the group consisting of compound Hex-[Cys(tMeBn(DOTA-AET))-Pro-Pro-Thr-Gln-Phe-Cys]-OH (3BP-3554) and compound Hex-[Cys(tMeBn(DOTA-PP))-Pro-Pro-Thr-Gln-Phe-Cys]-Asp-NH2 (3BP-3407).SELECTED DRAWING: None
Owner:3B PHARM GMBH
A machine learning-based target-specific virtual screening method and system
The application provides a target-specific virtual screening method and system based on machine learning. Active molecules and inactive molecules are docked with multiple conformations of a target, and protein-ligand interaction features of the docked molecules and related features of the ligands are extracted as input features of a machine learning model. A scoring function model is constructed using multiple machine learning models, and based on the advantages of multiple target-specific scoring function models, an integrated target-specific scoring function model is finally obtained by combining a model integration method. The integrated model can more effectively process complex protein-ligand interaction data, has excellent prediction performance and virtual screening capability, provides more reliable prediction results, and improves the virtual screening capability.
Owner:SHANDONG UNIV
Bivalent ligand molecules targeting bcl6 protein degradation and uses thereof
The application discloses a kind of bivalent ligand molecules for targeting BCL6 protein degradation and application thereof, it is related to the technical field of drug development;The structure general formula of the bivalent ligand molecules for targeting BCL6 protein degradation is as follows: M1-L-M2;Wherein, M1 and M2 are independent structure same or different BCL6 protein ligand;L is any chain or cyclic hydrocarbon fragment capable of forming covalent bond with BCL6 ligand.The application also provides the application of the bivalent ligand molecules in the preparation of anti-tumor and / or immune disease drugs.The bivalent ligand molecules can exert good anti-tumor activity on a variety of BCL6-dependent tumors by selectively inducing BCL6 protein degradation, and are also related to the occurrence of a variety of immune diseases, and can be used in related tumor and immune disease drugs.
Owner:SOUTHWEST JIAOTONG UNIV
Carbonic anhydrase IX ligands for targeted delivery
PendingJP2025536589ARadioactive preparation carriersMacromolecular non-active ingredientsAntigenDisease
The present invention relates to protein ligands for carbonic anhydrase IX (CAIX) as a biomedically relevant target. In particular, highly specific ligands can interact exclusively with an antigen, i.e., CAIX, expressed on the surface of tumor cells, sparing healthy target-expressing organs, thereby enabling efficient in vivo drug delivery applications. Ligands can exhibit particularly low dissociation constants and / or enzyme isoform specificity, making them suitable for targeted delivery of payloads, such as therapeutic and / or diagnostic agents, to sites affected by or at risk of diseases or disorders characterized by CAIX expression.
Owner:フィロケム·アーゲー
Machine learning methods for predicting properties of proteins and ligands
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a predicted property score of a protein and a ligand. In one aspect, a method comprises: obtaining a network input that characterizes a protein and a ligand; processing the network input characterizing the protein and the ligand using an embedding neural network to generate a protein-ligand embedding representing the protein and the ligand, wherein the embedding neural network has been jointly trained with a generative model that is configured to: receive an input protein-ligand embedding; and generate, while conditioned on the input protein-ligand embedding, a predicted joint three-dimensional (3D) structure of an input protein and an input ligand represented by the input protein-ligand embedding; and generating a property score that defines a predicted property of the protein and the ligand using the protein-ligand embedding.
Owner:ISOMORPHIC LABS LTD
Protein ligand binding affinity prediction method based on two-channel hierarchical interactive learning
The invention discloses a protein ligand binding affinity prediction method based on two-channel hierarchical interactive learning, and particularly relates to an affinity prediction method based on two-channel hierarchical interactive learning. The method comprises the following steps: S1, based on a protein-ligand compound space structure containing a three-dimensional structure and chemical data, constructing six chemical entity interaction diagram networks from an atomic scale to a substructure scale; s2, extracting covalent and non-covalent interaction information of protein-ligands from the internal and external channels at the same time by taking a dual-channel coding framework as a backbone; and S3, adopting a hierarchical interactive learning strategy. According to the affinity prediction method based on the two-channel hierarchical interactive learning, provided by the invention, understanding of protein-ligand complex interaction is enhanced through a technology of modeling richness and complexity of protein-ligand interaction information, so that the protein-ligand binding affinity is comprehensively and accurately predicted.
Owner:NORTHWEST NORMAL UNIVERSITY
Molecular fragment library based on protein binding site properties as well as construction method and application of molecular fragment library
The invention discloses a molecular fragment library based on protein binding site properties and a construction method and application thereof, and belongs to the field of computer-aided drug design. The construction method comprises the following steps: acquiring three-dimensional structure data of a protein-ligand compound, and preprocessing to obtain standardized protein binding sites and ligand molecules; cutting the standardized ligand molecules by adopting an iterative molecular cutting algorithm to generate molecular fragments; identifying and quantifying an interaction between the molecular fragment and an amino acid residue in the normalized protein binding site; and associating the molecular fragments, the information of the amino acid residues interacting with the molecular fragments and the corresponding interaction modes, and constructing a molecular fragment library. The interaction mode of molecular fragments and specific amino acid residues is labeled, so that the targeting property of the fragment library is improved; and the candidate fragments with specific interaction potential can be quickly positioned, so that the drug design efficiency is improved.
Owner:CHINA PHARM UNIV
Predicting joint three-dimensional (3D) structures of proteins and ligands by cofolding
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a predicted joint 3D structure of a protein and one or more ligands. In one aspect, a method comprises: obtaining a network input that characterizes the protein and the one or more ligands; processing the network input using an embedding neural network to generate a protein-ligand embedding of the protein and the one or more ligands; and generating, using a generative model and while the generative model is conditioned on the protein-ligand embedding, the predicted joint three-dimensional (3D) structure of the protein and the one or more ligands.
Owner:ISOMORPHIC LABS LTD
Predicting the joint three-dimensional (3D) structure of a protein and a ligand by co-folding
PendingCN122139223ABiostatisticsNeural learning methodsTheoretical computer scienceNetwork processing
Methods, systems, and apparatus for generating predicted joint 3D structures of proteins and one or more ligands, comprising computer programs encoded on a computer storage medium. In one aspect, a method includes: obtaining network input characterizing the protein and the one or more ligands; processing the network input using an embedded neural network to generate protein-ligand embeddings of the protein and the one or more ligands; and using a generative model and, when the generative model is conditioned on the protein-ligand embeddings, generating the predicted joint three-dimensional (3D) structure of the protein and the one or more ligands.
Owner:ISOMOFICO LABORATORIES LTD
Pamam-based nanoparticle protein degradation system and method of preparation and use thereof
A PAMAM-based protein degradation system and a method of preparation and use thereof are provided. The protein degradation system comprises: a silica nanoparticle core; and a poly(amidoamine) dendrimer (PAMAM) layer coated on a surface of the silica nanoparticle, wherein the PAMAM layer is linked via amide bonds to three small-molecule ligands: MDM2 protein ligand Idasanutlin, GLUT1 protein ligand Lavendustin B and E3 ubiquitin ligase ligand Thalidomide-NH—CH2—COOH. The nanoparticle protein degradation system cooperatively degrades MDM2 protein and GLUT1 protein. This cooperative degradation strategy not only effectively suppresses proliferation and energy metabolism of tumor cells, but also significantly enhances the stability of p53 protein. By restoring the normal function of p53 protein, tumor cell growth is further inhibited, providing a new strategy for cancer therapy.
Owner:QILU UNIVERSITY OF TECHNOLOGY (SHANDONG ACADEMY OF SCIENCES)
Method, device and terminal for determining three-dimensional structure of protein-ligand complex
The application discloses a kind of protein-ligand complex three-dimensional structure determination method, device and terminal, the method includes: simultaneously processing protein data and ligand data, generate protein pocket data and ligand pose data;Matching protein pocket data and ligand pose data, obtain first three-dimensional structure;Voxelization first three-dimensional structure, obtain several grids, including corresponding position protein-ligand binding information in grid;The grid is input into trained neural network, and the grid is sorted;And according to the first grid of sorting, obtain optimal binding site and optimal ligand pose, and combine to obtain protein-ligand complex three-dimensional structure.Through simultaneously extracting and processing protein data and ligand data, then in the form of voxelization is formed including protein-ligand binding information grid, again through neural network for grid sorting, the three-dimensional structure of protein-ligand complex can be more quickly and accurately determined by the application.
Owner:CHONGQING KANGZHOU ZHITONG PHARM TECH CO LTD +1
Method, apparatus, medium and program product for structural optimization of ligands in protein-ligand complexes
PendingCN121122496AMolecular designChemical machine learningTheoretical computer scienceProtein–ligand complex
The invention aims to provide a method and equipment for optimizing the structure of a ligand in a protein-ligand compound, a medium and a program product. The method comprises the following steps: determining an optimization space corresponding to an initial structure of the ligand in the protein-ligand compound; and determining an optimized structure corresponding to the ligand in the optimized space by using a differential evolution method and combining target scoring functions, the target scoring functions including an affinity scoring function, a ligand structure rationality scoring function and a protein-ligand atomic distance constraint scoring function. According to the method, the obtained optimized structure meets the affinity condition and also meets the distance constraint condition, the situation that the ligand collides with the protein and does not conform to the physical law does not occur, and the calculation efficiency of finding the ligand structure conforming to the condition is improved.
Owner:SHANGHAI MOLECULAR HEART INTELLIGENT TECH CO LTD
Protein-ligand binding affinity prediction method based on graph convolutional network
The invention provides a protein-ligand binding affinity prediction method based on a graph convolutional network. The method comprises the following steps: firstly, constructing a protein map based on a three-dimensional structure of a protein pocket, and carrying out feature coding on sequence residues by using ESM3; meanwhile, a molecular map containing geometric information is constructed for the ligand, and chemical characteristics are extracted in combination with molecular fingerprints. And then cross-graph interaction of the protein graph and the ligand graph is realized by adopting a multi-layer message passing graph neural network, and an attention mechanism is introduced to capture key interaction sites and spatial dependency relationships. And generating a unified representation vector of a compound level through pooling, and outputting binding affinity through a prediction network. According to the method, the multi-modal association of the structure and the sequence can be automatically learned, and the accuracy and efficiency of virtual screening and lead compound optimization are effectively improved.
Owner:CHANGCHUN UNIV OF TECH
Compounds comprising a fibroblast activation protein ligand and use thereof
The present invention is related to a compound comprising a cyclic peptide and a chelator, and its use.
Owner:3B PHARM GMBH
Method for predicting the pose of the docking between a protein and a ligand based on a graph neural network
The application discloses a kind of based on graph neural network's protein and ligand between the prediction method of pose of docking pose, first, obtain the biological information sample set of protein-ligand complex, sample set includes sample data and sample annotation data;Second, construct the pose generation model of docking pose based on graph neural network and the pose evaluation model of docking pose based on multi-view, further adjust the parameter of model, the sample data is handled by the structure generation model obtained by training, obtain the pose of protein ligand and actually output;Finally, the stability of output result is evaluated using mainstream pose docking structure evaluation index.This application directly uses the biological structure information of ligand protein to generate the optimal docking pose structure, and evaluates the generation result through multi-angle comprehensive evaluation model, so as to improve the accuracy of ligand-protein pose structure docking prediction, and improve the effectiveness of ligand-protein pose structure docking prediction result evaluation.
Owner:NANJING NORMAL UNIVERSITY
Protein degraders developed on basis of bcl-2 family proteins ligand compounds and uses thereof
Owner:SHANGHAI TECH UNIV +1
Fibroblast activating protein ligand and application thereof
PendingCN121532406AOrganic active ingredientsCell receptors/surface-antigens/surface-determinantsDiseaseFibroblast
The invention relates to a fibroblast activating protein ligand and application thereof, in particular to a compound shown in a formula (I) or pharmaceutically acceptable salt thereof, a pharmaceutical composition containing the compound, and application of the compound or the pharmaceutically acceptable salt in diagnosis or treatment of diseases related to fibroblast activating protein, such as tumors or cancers.
Owner:JIANGSU HENGRUI MEDICINE CO LTD +1
High-throughput parallel synthesis of small molecule degraders
Disclosed herein are high-throughput synthetic methods for deliberately and prospectively discovering molecular glues that can be used to form complex protein-ligand surfaces that facilitate interfacial binding to other proteins on a dispersed surface. Specifically, the present application discloses a high-throughput method that uses sulfur(VI) fluoride exchange (SuFEx) transformation and N-hydroxysuccinimide (NHS)-ester derivatized amide coupling to prospectively repurpose known ligands of a protein of interest as degraders and compounds capable of inducing proximity to other proteins. Disclosed herein are methods of developing known ligands of a target protein into degraders of the target protein. Also disclosed are methods of developing a novel small molecule chromatin-competitive inhibitor of the 11-19 Leukemia (ENL) YEATS domain into an effective degrader of ENL.
Owner:THE SCRIPPS RES INST
Antibody protac conjugates
PendingJP2026004408AAntibody ingredientsImmunoglobulins against cell receptors/antigens/surface-determinantsProtein targetAntibody conjugate
Branched antibody-PROTAC conjugates (APCs) are provided.SOLUTION: An immunoconjugate having the formula Ab - [L1 - (A-AB-B) m] n, wherein (a) Ab is L2 or a binding fragment thereof, (b) L1 and L2 are each independently a linker, L1 and L2 are the same or different, and L1 binds to L2, (c) A is a target-protein ligand / binder, (d) B is a ubiquitin ligase ligand / binder, and (e) n and m are independently an integer from 1 to 8. Target proteins include kinases, G-protein coupled receptors, transcription factors, phosphatases and RAS superfamily members.SELECTED DRAWING: None
Owner:DEV CENT FOR BIOTECHNOLOGY
Bivalent ligand molecules targeting egfr and uses thereof
This invention discloses a bivalent ligand molecule targeting EGFR and its applications, belonging to the field of drug development technology. Its general structural formula is: [Formula omitted for brevity], where L is a linking group, and M1 and M2 are EGFR protein ligands. This invention forms a bivalent EGFR ligand molecule by covalently linking two EGFR ligands through a linking group. This bivalent ligand molecule can induce additional protein-protein interactions between EGFR monomers, which greatly enhances the binding strength and stability of the drug to EGFR, thereby overcoming the drug resistance problem of traditional EGFR inhibitors and providing a new treatment strategy for cancer patients carrying EGFR mutations and other patients with other diseases.
Owner:SOUTHWEST JIAOTONG UNIV
Compounds containing fibroblast-activating protein ligands and their use
The present invention provides compounds containing fibroblast-activating protein (FAP) ligands, methods for diagnosing diseases, methods for treating diseases, and methods for delivering effectors to FAP-expressing tissues. [Solution] The present invention relates to a compound comprising a cyclic peptide of formula (I) and an N-terminal modification group A attached to Xaa1, wherein each of Xaa1, Xaa2, Xaa3, Xaa4, Xaa5, Xaa6, and Xaa7, and any one thereof, are amino acid residues, and Yc has the structure of formula (X). TIFF2026062901000653.tif38135
Owner:3B PHARM GMBH
Cell-penetrating peptide modified enzyme-sensitive PDC-type PROTAC and preparation method and application thereof
ActiveCN119792559BPeptide preparation methodsPharmaceutical non-active ingredientsTumor targetCathepsin B
The application discloses a cell-penetrating peptide modified enzyme-sensitive PDC type PROTAC as well as a preparation method and application thereof, and belongs to the technical field of tumor targeted therapy. The cell-penetrating peptide modified enzyme-sensitive PDC type PROTAC is obtained by modifying a cell-penetrating peptide and an enzyme-sensitive linker (GFLG) to a PROTAC of an anti-tumor drug target protein ligand, has the ability to release the PDC type PROTAC under catalysis of cathepsin B, has a smaller influence on cell viability of U251 cells, U87 cells and HEK293 cells, has proliferation inhibition activity on the U251 cells and the U87 cells, can degrade target proteins in the U251 cells and the U87 cells, can induce apoptosis of the U251 cells and the U87 cells, has an influence on U251 cell cycles, can be used for preparing an anti-tumor drug (target protein degradation and membrane penetration), has a good application prospect in preparation of a drug for targeting human brain glioma cells, and can be used as another important field for PROTAC drug development.
Owner:XI AN JIAOTONG UNIV
A method and system for predicting ligand-target dissociation kinetics parameters
This application relates to the field of computer-aided drug design technology, and in particular to a method and system for predicting ligand-target dissociation kinetic parameters. The method includes the following steps: constructing and parameterizing a protein-ligand system; applying constraints to the system to maintain structural stability; setting multiple sets of simulation condition pairs; globally scaling the non-bonded interaction potential energy of the system or the force derived from that potential energy under each set of simulation condition pairs; performing molecular dynamics simulations and detecting ligand dissociation events under each set of simulation condition pairs; estimating the dissociation rate under each simulation condition pair; and extrapolating to the target condition based on the relationship between the dissociation rate and a unified extrapolation independent variable to obtain the dissociation rate and residence time under the target condition. This invention solves the problem in existing technologies where it is difficult to reliably obtain the dissociation rate of ligands and corresponding target receptors at an affordable computational cost, and it also features controllable parameters and ease of parallelization.
Owner:LINGNAN INST OF TECH
Protein-ligand affinity evaluation method based on domestic supercomputing platform
The application provides a protein-ligand affinity evaluation method based on a domestic supercomputing platform, comprising: a domestic supercomputing production environment building step comprising: compiling dependent libraries required for running a deep learning model, and completing framework configuration of SWPyTorch; a deep learning model design and implementation step comprising: constructing a deep learning model based on a protein-ligand affinity evaluation dataset, implementing the deep learning model based on a PyTorch framework under an X86 platform, after model implementation, model transplantation, and transplantation of the deep learning model to the domestic supercomputing platform; a deep learning model parallel optimization step comprising: based on the domestic supercomputing platform, optimizing the deep learning model from data parallelism, calculation parallelism, communication parallelism, operator library optimization, and SWPyTorch multi-node parallelism; and a job submission and running step comprising: configuring computing node resources of the domestic supercomputing platform, activating a dependent environment of the domestic supercomputing platform, and submitting a running job.
Owner:青岛国实科技集团有限公司
Protein degradation system based on polyamidoamine dendrimers and preparation method and application thereof
The application belongs to the technical field of biological medicine, and relates to a protein degradation system based on a polyamide-amine dendritic polymer, and a preparation method and application thereof. The protein degradation system is in a nanoparticle structure, the nanoparticle structure comprises a silica nanoparticle and a polyamide-amine dendritic polymer layer coated on the surface of the silica nanoparticle, and MDM2 protein ligands, GLUT1 protein ligands and E3 ubiquitin enzyme ligands are connected on the polyamide-amine dendritic polymer layer through chemical bonds. The protein degradation system provided by the application can synergistically degrade MDM2 protein and GLUT1 protein. This synergistic degradation strategy can not only effectively inhibit the proliferation and energy metabolism of tumor cells, but also significantly enhance the stability of p53 protein. By restoring the normal function of p53 protein, the growth of tumor cells is further inhibited, thereby providing a new strategy for tumor treatment.
Owner:QILU UNIVERSITY OF TECHNOLOGY (SHANDONG ACADEMY OF SCIENCES)
Preparation and application of fibroblast activating protein ligand and radionuclide complex thereof
The invention relates to the field of nuclear medicine, and relates to a fibroblast activating protein (FAP) ligand and preparation and application of a radionuclide complex thereof. The fibroblast activating protein inhibitor has a structure as shown in a general formula (I), and C represents a chelating agent capable of containing radionuclide; d is L-(T) n; l represents a linker; t represents a targeting ligand. The invention also provides a radionuclide complex containing a radionuclide and the fibroblast activating protein inhibitor.
Owner:3D MEDICINES (SHANGHAI) CO LTD
Protein-ligand binding affinity prediction method and system for drug research and development
The invention relates to the technical field of drugs and the technical field of artificial intelligence, in particular to a protein-ligand binding affinity prediction method and system for drug research and development, and the method comprises the steps: obtaining a protein-ligand pair, and extracting structure-perceived protein characterization, functional characterization and ligand molecular characterization; wherein the protein characterization is determined by an amino acid sequence and structural information of the protein, and the function characterization is determined by the protein with function annotation information; performing token dimension alignment on the protein characterization and the functional characterization based on a multi-layer perceptron (MLP), performing multi-modal fusion on the aligned protein characterization and functional characterization, and obtaining a fused embedded representation by combining a token-by-token adaptive weight alpha; and splicing the fused embedded representation and ligand molecular representation to obtain a comprehensive feature vector for predicting binding affinity. Through deep interaction and fusion of multi-source information such as sequence, structure and function annotation, the accuracy of protein-ligand affinity prediction is improved.
Owner:SHANTOU UNIV
PROTACs compounds targeting degradation of bcl6 and applications thereof
The present application relates to the technical field of antitumor drugs, and particularly relates to a kind of PROTACs compound targeted to degrade BCL6 and application thereof, and the compound is the compound shown in formula (I) or its stereoisomer, geometric isomer, tautomer, nitroxide, solvate, pharmaceutically acceptable salt, metabolite or prodrug.The PROTACs compound targeted to degrade BCL6 provided in the present application connects BCL6 protein ligand and E3 ubiquitin ligase ligand through chemical groups;The compound can be used to inhibit the proliferation, growth, migration, infiltration, clonal formation and metastasis of tumor cells, promote the apoptosis of cancer cells, promote the autophagy of tumor cells, overcome the drug resistance of chemotherapy or targeted therapy of tumor, inhibit the growth of tumor stem cells, and prolong the survival period of tumor patients.
Owner:KUNMING MEDICAL UNIVERSITY
Protein ligand binding affinity prediction method and system based on multi-scale topology and storage medium
The invention provides a protein ligand binding affinity prediction method and system based on multi-scale topology and a storage medium, and the method comprises the steps: obtaining original information of a protein-ligand compound, and extracting specific element information; carrying out protein atom screening; performing permutation and combination on elements in the screened specific protein atom set and the screened specific ligand atom set, and generating a plurality of protein-ligand element combination pairing modes; constructing a distance matrix corresponding to each element combination pairing mode, and performing multi-scale filtering; performing spectral analysis on all the sub-topological structures to generate multi-scale topological features; the multi-scale topological features are input into a preset neural network model for prediction, and a combined affinity prediction result is obtained; according to the method, the prediction accuracy, robustness and generalization ability under the background of multiple molecular structures are remarkably improved, and the problem that a traditional model is insufficient in adaptability when facing different molecular types and scale changes is effectively solved.
Owner:SUN YAT SEN UNIV