Review pubs.acs.org/jcim
Computational Multitarget Drug Design Weilin Zhang,† Jianfeng Pei,*,‡ and Luhua Lai*,†,‡,§ †
Peking−Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University, Beijing 100871, People’s Republic of China ‡ Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University, Beijing 100871, People’s Republic of China § BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, People’s Republic of China ABSTRACT: Designing drugs that can simultaneously interact with multiple targets is a promising approach for treating complicated diseases. Compared to using combinations of single target drugs, multitarget drugs have advantages of higher efficacy, improved safety profile, and simpler administration. Many in silico methods have been developed to approach different aspects of this polypharmacology-guided drug design, particularly for drug repurposing and multitarget drug design. In this review, we summarize recent progress in computational multitarget drug design and discuss their advantages and limitations. Perspectives for future drug development will also be discussed.
1. . INTRODUCTION Target-based drug discovery has successfully produced targetspecific medicines. However, it is less effective for diseases with complex pathogenic mechanisms, such as cancer, inflammation, diabetes, and central nervous system disorders.1−3 From a systematical point of view, this may come from the complexity of disease-related molecular networks, which is robust with many redundant pathways. Polypharmacology is emerging as a new paradigm to treat complicated diseases by regulating multiple targets to achieve desired physiological responses.4−7 A drug molecule that can modulate multiple targets simultaneously is a simple approach for network control. Unlike “cocktail therapies” or drug combinations, an ideal multitarget drug interacting with multiple targets with high efficacy could alter the disease network from an imbalanced disease state to a normal healthy state.8 With decreased risk of drug−drug interactions, fewer pharmacokinetic and safety profile tests are needed.6 Multitarget drugs could also circumvent drug resistance resulting from single-target mutations or expression changes as simultaneous mutations of several targets among different pathways or in different positions of one cascade pathway are rare events.9,10 Many drugs are known to have activities toward multiple targets and their therapeutic ranges are enlarged. In earlier studies, many of the new targets for old drugs were identified by chance.11 These drugs can bypass many of the expensive and time-consuming steps needed to bring a drug to market in many successful projects. Drug repurposing has become one of the primary approaches to discover multitarget drugs or leads in drug discovery systematically.12,13 At the same time, rational drug design to achieve a desired multitarget activity profile, also known as multitarget drug © 2017 American Chemical Society
design, develops rapidly in recent years. Known promiscuous ligands can be optimized carefully or new leads can be designed with the desired multitarget binding properties. The design process is challenging because binding and quantitative structure activity relationships among multiple biological targets must be considered simultaneously, especially for targets from different protein families.6,7,14 Several reviews have presented good summaries of in silico methods for polypharmacology that are data-driven, ligandbased, or structure-based.6,15−19 Most of them have focused on methods used for drug repurposing (i.e., to find new targets for known drugs). In recent years, computational methods that rationally design drugs capable of binding multiple targets are emerging and the promising results have gathered increased attention to this field. In this review, we will first provide an overview of multitarget drug design methods. We will then introduce guidelines for target selection and computational methods for multitarget drug design, including pharmacophorebased, docking-based, and de novo-based methods. Finally, perspectives for future development will be discussed.
2. AN OVERVIEW OF MULTITARGET DRUG DESIGN METHODS The general principles to design multitarget drugs were proposed in the mid-2000s.4,5,20 A potential multitarget lead could be either a known compound that has shown promiscuous interactions with the desired targets or engineered by merging multiple known single-target compounds or their Received: August 19, 2016 Published: February 6, 2017 403
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Journal of Chemical Information and Modeling common substructures.21 These merged compounds are usually smaller than molecules that directly link two distinct structures via a flexible chain. Nonetheless, their ligand efficiencies are usually lower than commonly used clinical drugs. After initial experimental validation, optimization steps are needed to improve the activity ratio, global selectivity, physicochemical properties, as well as absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties.4,22 In silico methods have been applied with remarkable success to accelerate this process (see Figure 1). These methods are
targets. Because ligand-based and structure-based methods for drug repurposing have already been extensively reviewed,6,15−19,31,35 we will not cover them here. Different approaches have been applied to discover ligands with the desired multitarget activities. Known ligands that already showed multiple pharmacological activities were designed out to eliminate unwanted activity and finally reached a specific activity profile. When no known multitarget ligands were available, new ligands were designed using the targetcentric methods listed in Figure 1. Classical computationalaided drug design methods, such as pharmacophore studies, 3D-similarity searches, and docking, have been implemented in serial or parallel to fulfill this goal. Each of the top-ranking ligands in every screen is selected to test their interaction with multiple targets. Compared to virtual screening methods, which require pre-existing compound libraries, de novo design approaches can generate a candidate pool from simple building blocks (e.g., atoms, fragments) to obtain a focused library.8,36 This type of method could be more suitable for designing multitarget drugs with desired target profiles.
3. SELECTION OF TARGET COMBINATIONS FOR MULTITARGET DRUG DESIGN One of the key challenges in multitarget drug design is identifying a feasible target combination.37,38 In addition to systematic high-throughput screening (HTS), network analysis methods can be used. Understanding the overall topology and dynamics of a disease network provides valuable information that can be used to identify potential therapeutic interventions and manipulations.39−43 For example, we used three-node enzymatic networks as a model system to study the effects of multitarget interventions. We found that drug combinations only acted synergistically or antagonistically within certain network topologies.44 This discovery indicates that designing novel synergistic drug combinations based on network topology is a promising tool for multitarget drug design. The dynamic behavior of a network is also very informative. For a network with realistic dynamics, a phenotypic response could be related to the state of the network. A network model could be used to identify potentially intervention through multiple drug-target interactions, which drive the network from a disease state to a healthy state.45 We proposed a Monte Carlo simulated annealing-based search algorithm, called “Multiple Target Optimal Intervention,” to identify combinations of targets in ordinary differential equation (ODE)-based network models. In the human arachidonic acid metabolic network, we predicted several potential target combinations with high efficacy and fewer side effects. Some of these targets have been confirmed experimentally using drug combinations and designed multitarget compounds with broad dosage space.46−48 The target proteins in a target combination should be chemically tractable using small molecules.49 This can be estimated using binding sites detection methods, such as LIGSITE, Q-SiteFinder, SiteMap, or CAVITY.50−53 These programs can identify potential binding sites based on protein structures. Proteins with similar binding sites are more likely to bind to a common ligand. For cases where biological targets are distantly related or unrelated, computational approaches for binding site comparisons can provide useful hints. Skolnick and Gao reported similarity analysis of a nonredundant set of more than 20 000 protein−ligand complexes using the binding site comparison program Apoc.54 Their results showed that the overall space of the binding pocket with known ligands is
Figure 1. An overview of computational methods for multitarget drug design. Methods used to discover multitarget drugs are classified as either “ligand-centric” or “target-centric”.
usually categorized as either ligand-based or structure-based, depending on whether target structural information is available. For ligand-based methods, compounds are usually represented by various descriptors and ranked by similarity.23 Alignments can also be performed based on the shape of their threedimensional (3D) conformations. When experimental data are available, machine-learning techniques could be also used to create predictive models based on numerical descriptors. For structure-based methods, the ligands are first positioned into predefined binding pockets of all of the target molecules, and all possible conformations compatible with the surrounding residues are preserved. These complexes are ranked by scoring functions based on force-field or empirical models.24,25 Some of these methods are suitable for predicting potential targets or off-targets for specified ligands (“ligand-centric”), whereas others are used to find or optimize ligands for desired target activity profiles (“target-centric”). Several studies have reported successful drug repurposing or target fishing using ligand-based methods such as SEA,26 SPiDER,27 or other methods based on two-dimensional (2D) fingerprints or three-dimensional (3D) shape similarity.28 These methods are very efficient when active ligands are available for comparison. Structure-based methods such as INVDOCK,29 TarfisDOCK,30 or VinaMPI31 are good complementary tools when the number of known active ligands is insufficient. These methods dock potential ligands into many target structures that are prepared in advance.32,33Targets with well-formed complexes have a high chance of being potential targets. PharmMapper34 finds new targets for query ligands by examining their best mapping positions against all receptorbased pharmacophores in the PharmTargetDB. In silico predictions only provide a starting point in these studies. Experimental testing will then be carried out to find the true 404
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Journal of Chemical Information and Modeling smaller than expected. This is promising for binding-site selection in multitarget drug design. The Pocket Similarity score (PS-score) in Apoc is defined as a combination of backbone geometry, side-chain orientation, and chemical similarity of the aligned pocket-lining residues. This score could be used as a good indicator of the similarities and differences in sites from distantly related or unrelated proteins. After network-based analysis and structure-based binding-site assessment, multitarget drug design can be carried out.
4. PHARMACOPHORE- AND DOCKING-BASED MULTITARGET DRUG DESIGN METHODS Pharmacophore-based and docking-based virtual screenings are two computational approaches commonly used in single-target drug discovery. Many related tools have been extended for finding ligands with the required biological profile. A pharmacophore model with several key features can be built for each target based on the chemical structures of known ligands or the 3D structure of the binding site. Multiple conformations of virtual ligands are mapped onto pharmacophore model and fitness evaluations are performed. For docking-based methods, the ligands are put into the binding site and then evaluated using different scoring functions. One simple approach is to use these methods sequentially or in parallel to screen molecules that can bind multiple targets. When using two or more pharmacophore models to screen a compound library, the top-ranked molecules from the first model are fed to the next one. An alternative way is to query each model first, then combine the results to select out common top-ranked molecules. These sequential or parallel virtual screening strategies and selection of common hits involve multiple computational screening steps that are computationally expensive and could present multiple challenges, such as comparing binding scores or fitness scores for different targets. We proposed a common pharmacophore-guided multitarget drug design strategy to overcome these limitations based on the finding that the number of bound conformations of a ligand is limited, even for different targets.55,56 This strategy can be used in systems where a ligand binds all targets with common features. As illustrated in Figure 2, the pharmacophore model for each target can be first generated using the 3D structures of the targets or available active ligands.57 A common pharmacophore is then generated by aligning all possible pharmacophore combinations. This technique can be used to quickly screen for matching compounds. A comprehensive molecular docking of the topranking compounds is performed to identify compounds that bind well to all targets in the initial screen. The common pharmacophore can also be used as a post-filter after multiple docking screening to identify compounds that may bind to all targets. In addition, a shape-based comparison can also be used as an alternative approach for docking.58 We used this protocol to design compounds that can inhibit two human inflammation-related proteins, leukotriene A4 hydrolase (LTA4H-h) and human nonpancreatic secretory phospholipase A2 (hnps-PLA2).59,60 The common pharmacophore was constructed using structure-based pharmacophores generated by Pocket V2 using co-crystal structures of LTA4H-h and sPLA2.61 The common pharmacophore contains two hydrophobic sites and a metal-coordination site (Figure 3). Based on the assumption that compounds satisfying the common pharmacophore structure would inhibit both proteins, virtual ligands (∼150 000 entries) were docked into each
Figure 2. Common pharmacophore-based multitarget drug design.
binding site. Other than a normal sequential docking filter process which only included the top 2%−10% of the database, up to the top 60% ranked potential binding conformations were extracted and checked against the common pharmacophore model. After thorough exploration for the binding conformation of 163 compounds by docking with increased conformation sampling and different score functions, three out of nine compounds that passed the final common pharmacophore and potential impartment interactions check showed moderate activity against LTA4H-h and hnps-PLA2. Approximately 350 reported LTA4H-h inhibitors which, at that time, were also examined for the common pharmacophore. Compound 2, which is an analogue of a known LTA4H-h inhibitor, was identified to inhibit both enzymes. Generation of a common feature model is a key step for this type of design strategy. Instead of directly building a common pharmacophore from known pharmacophores, Hsu et al. used the information from a large-scale data set of docked compounds to build a core site-moiety map that was used to search for multitarget inhibitors.62 They docked compounds to the orthologous proteins, shikimate kinase from Mycobacterium tuberculosis and Helicobacter pylori (MtSK and HpSK, respectively). The top 2% of binding results (∼6000 entries) were selected to build a protein-compound profile. A consensus site-moiety map was created by analyzing the moiety of each docked compound in each subpocket, and this map was used to identify dual inhibitors. These multitarget drug design methods must address the same concerns as their implementation in single-target drug design. First, false positive hits must be addressed. False positive molecules from pharmacophore screening have minor variations, compared with active molecules. These variations eventually lead to incompatibilities with the target structure. When excluded volume or shape filters are applied, this issue can be alleviated. As for docking methods, the sampling of all possible binding conformations for a ligand in a static pocket is almost complete. False positive hits are primarily due to scoring functions and protein flexibility.63−65 Because docking scores 405
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Figure 3. Common pharmacophore-based design of a multitarget inhibitor of hnps-PLA2 and LTA4H-h. (Adapted with permission from ref 47. Copyright 2015, American Chemical Society, Washington, DC.) (a) Compound 1 fit a common pharmacophore generated by two targets; the binding conformation was confirmed using a docking method. (b) Compounds 1 and 2 showed the desired multitarget activities.
5. DE NOVO DESIGN-BASED METHODS FOR MULTITARGET DRUG DESIGNS De novo design methods can be used to construct new molecular entities from scratch, and extra controls can be added during the generation process to ensure the resulting molecules meet multiple predefined objectives. This design-on-demand nature could address the goal of multitarget drug design to achieve specific activity profiles. Compared to virtual screeningbased methods, the candidate pool in de novo design is usually diverse and broad, which allows for the rational design and optimization of highly integrated multitarget drugs, especially for proteins with dissimilar binding pockets. Many de novo drug design programs have been developed, including LUDI,73 SMoG,74,75 LigBuilder,76,77 SYNOPSIS,78 FLUX,79,80 DOGS,81 PhDD,82 etc. De novo drug design can be structure-based or ligand-based, depending on whether the explicit target−ligand interaction is considered during the design process. De novo-designed compounds usually are not commercially available and must be synthesized afterward. For structure-based methods, the synthetic accessibility of designed compounds can be analyzed using an embedded chemical reaction database and a retro-synthesis analyzer, which was first introduced by LigBuilder2.77 In ligand-based methods, the compounds are generated by transformations from the most common chemical reaction such as SYNOPSIS and DOGS.78,81 A typical de novo design scheme for multitarget drug design can generally be divided into several steps (see Figure 4). To the
and pharmacophore fitness scores were originally used to rank many ligands against one target, special treatments, such as statistical significance consideration, should be considered when comparing scores against different targets.66−68 Because of computational resource limitations, target flexibility is usually not fully taken into account in docking-based methods. The fit criteria of pharmacophore models can be adjusted to provide some flexibility to the model.69 This limitation can be partially compensated for using molecular dynamics (MD)-based validation, such as the MM/GBSA free-energy calculation,70 or using several representative conformations of target proteins from MD simulations.71,72 For a pharmacophore query, the challenges of building a good pharmacophore model are similar to those of single-target ligand design, such as properly selecting training-set ligands, handling the conformational flexibility of ligands, choosing key chemical features generated from ligand functional groups or target residues, and conducting proper pharmacophore alignments.69 Finally, one should carefully consider which virtual libraries to query. Low pocket similarities will make it difficult to identify a sufficient number of multitarget candidates for further studies. A well-designed library that covers a broad range of chemical space with a sufficient number of candidates is highly recommended. 406
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Figure 4. De novo multitarget drug design scheme.
Figure 5. Procedure for ligand-based de novo multitarget drug design used in ref 85. Donepezil was used as a template. When different sets of objectives were applied, compounds with different activities were identified.
determine their fitness. These objectives usually include the interaction requirements of the desired targets (calculated using similarity−comparison or machine-learning methods). The topranking compounds are advanced for another round of transformation/growth until the final criteria are achieved. Several representative compounds are selected for synthesis and experimental confirmation by using post-design criteria, such as novelty, synthetic accessibility, and ADMET properties. Several successful examples using this scheme have been reported.84−87 Hopkins et al.84 used donepezil, which is an approved acetylcholinesterase inhibitor that is used to treat Alzheimer’s disease, as a template ligand for different activity profiles. This compound was designed in silico and experimentally confirmed as a moderate D4 inverse agonist with minimal D2 activity. Their structure generation schemes
best of our knowledge, only one structure-based83 and several ligand-based de novo methods84−87 have been applied to multitarget drug design with promising results. These studies will be described below. 5.1. Ligand-Based De Novo Methods for Multitarget Drug Design. The known active ligands for the desired targets can be used as a reference or starting template to generate a virtual ligand library in ligand-based de novo multitarget drug design. They can also be incorporated into the training dataset as a prediction model of ligand-target interactions. As shown in Figure 4, the initial template or reference ligand that satisfies at least part of the objective functions is selected as the starting point, and then candidate pools are generated using a predefined transformation/growth scheme. All the compounds are examined using several optimal objective functions to 407
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Figure 6. Structure-based de novo multitarget drug design. (a) Fragment-based seeds are generated by extracting data from known ligands and confirmed experimentally; initial seeding positions are determined by docking, and the LigBuilder3 grow mode is used to design dual-target ligands. (b) Compounds generated in each design cycle. (Adapted with permission from ref 89. Copyright 2014, American Chemical Society, Washington, DC.)
5.2. Structure-Based De Novo Methods for Multitarget Drug Design. The structure-based de novo approach is intuitive and “rational”, because it directly considers the interactions between the ligand and multiple targets. As shown in Figure 4, initial seeds are first placed into the proposed binding sites with the assistance of physical energybased approaches, such as docking. Candidate pools are incubated according to a genetic algorithm-based growth scheme. The explicit interactions with proposed targets can be used as fitness criteria. All the compounds generated are tested against fitness criteria and other optimal objectives. The top-ranked candidates are retained for the next round of growth. Using a static target conformation could limit further optimization of primary hits. However, the flexibility of the target can be partially addressed using multiple target conformations in the structure-based de novo design. LigBuilder 3 was developed based on LigBuilder 2.77 We added de novo design and molecular optimization algorithms
were based on 700 unique structural transformations that were expanded from the common medicinal chemistry recorded in the literature with compounds having defined structure activity relationships. A Bayesian model was built to predict the probability that a compound will interact with the 784 targets from the ChEMBL database. Various target activity profiles (shown in Figure 5) and blood-brain barrier penetration ability were used to evaluate the fitness of the generated ligands. After evolution for several generations, several compounds (compounds 3−7) were selected using ADME property, novelty, and synthetic accessibility filters and validated experimentally. Ligand-based de novo design assumed that all information even the flexibility of the target is reflected in the known ligands. When sufficient ligand−target interaction data are known, the interaction mechanism of generated compounds will probably share similar features to those of the reference ligands. However, this method is limited for targets without sufficient ligand−interaction data to train predictive models. 408
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With the rapid accumulation of omics data and quantitative measurements, more disease-related molecular network models will soon be available for target combination analysis. In most cases, more than one target combination can be used to control a disease network. These multiple target combinations would allow investigators to select targets that are easily modulated by small molecules while achieving the same level of network control.90 The early stage experimental validation of multitarget ligands is very important for post-optimization. Emphasis on polypharmacology as a lead discovery strategy does not mean choosing any promiscuous lead for optimization. While computational methods are less expensive than HTS, ligands selected from in silico methods should also be carefully examined, to avoid nonspecific activities.91 In recent years, the Pan-Assay INterference compoundS (PAINS) criteria have been widely used to avoid false positive compounds that are chemically reactive, interfere with fluorescent assays, or aggregate.92−96 This type of analysis only accounts for possible false activity. Molecules should be carefully confirmed or excluded using further experimental studies, because there are examples that existing drugs fail in PAINS predictions.97 Another type of nonspecific false positive comes from colloidal aggregates, in which some chemical compounds aggregate in aqueous solutions, similar to micelle formation. Soluble and membrane proteins may absorb aggregates on their surfaces, leading to inhibition or, occasionally, activation.98−103 An in silico method has been devised to detect the similarity of compounds to known aggregators.104 This method could be helpful for early-stage-compound consideration, especially for validation using nonionic detergents in the screening assay. Again, aggregation itself is not a definite exclusion criteria and can be detected by light scattering, flow cytometry, or NMR integrated with other established assays.91 The pharmacophore- and docking-based multitarget drug design methods will continue to evolve. As the number of available chemical compounds increases, the gap between in silico chemoinformatics and biological exploration will narrow.105 The development of accurate scoring algorithms is expected to reduce false positive rates, and increasing computational power is expected to decrease runtime and allow broader searches to be conducted in a feasible time frame. Structure- and ligand-based de novo design will also benefit from integrated information. Fragment-based exploration, either experimental or in silico, could be used to detect the initial starting point.106−108 It could also provide clues for implementing “ensemble linking” strategies and promote the efficiency of the “fragment linking” algorithms that link fragments occupying different subpockets. With the development of automated robotic chemical synthesis and its coupling to de novo design software, designed compounds can be rapidly generated and tested.86,109,110 Artificial intelligence methods, such as deep learning, have been applied to predict ADMET properties, and more properties related to late-stage development can be incorporated during the de novo design process.111,112 With the rapid developments of both computational and experimental tools, designing multitarget drugs for feasible target combinations should become increasingly practical in the near future.
that could handle multiple targets. The algorithms are illustrated in Figure 6a. The synthetic accessibility of designed compounds can be analyzed in this program using an embedded chemical reaction database and a retro-synthesis analyzer inherited from LigBuilder2. LigBuilder 3 was applied to design dual-target inhibitors for cyclooxygenase 2 (COX-2) and LTA4H.83 These proteins are involved in different metabolic pathways that produce inflammatory mediators from arachidonic acid. This is the first successful example of a structure-based, multitarget, de novo design. Because smaller fragments are more likely to bind to multiple targets,88 the initial multitarget seeds were extracted from known COX-2 and LTA4H inhibitors automatically by LigBuilder 3 and validated experimentally. We determined all potential binding conformations of these fragments using a thorough docking process and used those conformations as the starting positions for the growing process. During the first round of de novo multitarget optimization, several criteria, such as minimum modification and common frameworks, as well as the binding-related scoring, were applied to avoid combinatorial explosion. Once the distribution of all starting fragments among top-ranked ligands and their synthetic accessibilities were considered, several ligands were selected for synthesis and a bioassay. One ligand showed dualtarget inhibition activity. After a second round of optimization and potency improvement, a compound with improved activity for both targets was obtained. This research shows that optimization of multitarget leads with a multiobjective balance is far more complicated than that of single-target leads when explicit target structural information is included. The traditional stepwise group-activity achievement approach often emphasizes local minima. This approach could be feasibly implemented by systematically and automatically starting from an extensive global structure sampling and properly optimizing the ligand without rushing. Hastiness would produce a ligand that only partially achieved the final objective. As shown in this work, the selection of multiple potential starting points greatly reduces the searching space queried during the growing process. We conducted a simple retrospective binding-site comparison of the proteins in Shang et al.83 (COX-2 and LTA4H), Wei et al.59(LTA4H and hnps-PLA2), and Yang et al.89 (acetylcholinesterase [AChE] and histone deacetylase 7 [HDAC7]) using the Apoc program.54 When the targets were processed using the common pharmacophore-aided method, they showed relatively higher PS-scores than those of targets processed using pure docking-based or de novo design-based methods. This demonstrates that common pharmacophore approaches can be used when the similarity of the binding site is high; otherwise, docking-based or de novo design should be used. This hypothesis needs further validation after a sufficient number of successful multitarget design studies have been reported.
6. CHALLENGES AND PERSPECTIVES FOR COMPUTATIONAL MULTITARGET DRUG DESIGN Over the past decade, the field of computational multitarget drug design has progressed rapidly. Several methods have been developed and examples of successful applications have been reported. However, many challenges remain. For example, the selection of target combinations is hindered because quantitative data for network dynamic studies are sparse and the molecular network for the disease cannot be constructed.
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*Fax: (+86)10-62751725. E-mail:
[email protected] (J. Pei). 409
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[email protected] (L. Lai).
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ORCID
Luhua Lai: 0000-0002-8343-7587 Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This work was supported in part by the Ministry of Science and Technology of China (Grant Nos. 2016YFA0502303 and 2015CB910302) and the National Natural Science Foundation of China (Nos. 81273436 and 21633001). The authors thank all the former and current members of the Lai Laboratory for their contributions to the multitarget drug design projects in the laboratory.
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