The three-dimensional structure of a biomolecule rather than its one-dimensionM sequence determines its biological function. At present, the most accurate structures are derived from experimental data measured mainly ...The three-dimensional structure of a biomolecule rather than its one-dimensionM sequence determines its biological function. At present, the most accurate structures are derived from experimental data measured mainly by two techniques： X-ray crystallog- raphy and nuclear magnetic resonance （NMR） spec- troscopy. Because neither X-ray crystallography nor NMR spectroscopy could directly measure the positions of atoms in a biomolecule, algorithms must be designed to compute atom coordinates from the data. One salient feature of most NMR structure computation algorithms is their reliance on stochastic search to find the lowest energy conformations that satisfy the experimentally- derived geometric restraints. However, neither the cor- rectness of the stochastic search has been established nor the errors in the output structures could be quantified. Though there exist exact algorithms to compute struc- tures from angular restraints, similar algorithms that use distance restraints remain to be developed. An important application of structures is rational drug design where protein-ligand docking plays a crit- ical role. In fact, various docking programs that place a compound into the binding site of a target protein have been used routinely by medicinal chemists for both lead identification and optimization. Unfortunately, de- spite ongoing methodological advances and some success stories, the performance of current docking algorithms is still data-dependent. These algorithms formulate the docking problem as a match of two sets of feature points. Both the selection of feature points and the search for the best poses with the minimum scores are accomplished through some stochastic search methods. Both the un- certainty in the scoring function and the limited sam- pling space attained by the stochastic search contribute to their failures. Recently, we have developed two novel docking algorithms： a data-driven docking algorithm and a general docking algorithm that does not rely on experimental data. Our algorithms search the pose space exhaustively with the pose space itself being limited to a set of hierarchical manifolds that represent, respectively, surfaces, curves and points with unique geometric and energetic properties. These algorithms promise to be es- pecially valuable for the docking of fragments and small compounds as well as for virtual screening.展开更多
Based upon the crystal structure of a previously reported fragment hit that binds to Corresponding author. β-secretase, a novel series of non-peptidic small-molecule β-secretase inhibitors, namely hexahydropyrimidin...Based upon the crystal structure of a previously reported fragment hit that binds to Corresponding author. β-secretase, a novel series of non-peptidic small-molecule β-secretase inhibitors, namely hexahydropyrimidin-5-ols, along with two series of their analogues, were rationally designed through structural modification. The CADD study was performed and revealed good expectation. Inhibitory activities of the corresponding structural cores were tested, which provided further support for our design approach.展开更多
Mycobacterium tuberculosis FabH, an essential enzyme in mycolic acids biosynthetic pathway, is an attractive target for novel anti-tuberculosis agents. Structure-based design, synthesis of novel inhibitors of mtFabH w...Mycobacterium tuberculosis FabH, an essential enzyme in mycolic acids biosynthetic pathway, is an attractive target for novel anti-tuberculosis agents. Structure-based design, synthesis of novel inhibitors of mtFabH was reported in this paper. A novel scaffold structure was designed, and 12 candidate compounds that displayed favorable binding with the active site were identified and synthesized. 2009 Song Li. Published by Elsevier B.V. on behalf of Chinese Chemical Society. All rights reserved.展开更多
Dental caries,a chronic disease characterized by tooth decay,occupies the second position in terms of disease burden and is primarily caused by cariogenic bacteria,especially Streptococcus mutans,because of its acidog...Dental caries,a chronic disease characterized by tooth decay,occupies the second position in terms of disease burden and is primarily caused by cariogenic bacteria,especially Streptococcus mutans,because of its acidogenic,aciduric,and biofilm-forming capabilities.Developing novel targeted anti-virulence agents is always a focal point in caries control to overcome the limitations of conventional anti-virulence agents.The current study represents an up-to-date review of in silico approaches of drug design against dental caries,which have emerged more and more powerful complementary to biochemical attempts.Firstly,we categorize the in silico approaches into computer-aided drug design(CADD)and AI-assisted drug design(AIDD)and highlight the specific methods and models they contain respectively.Subsequently,we detail the design of anti-virulence drugs targeting single or multiple cariogenic virulence targets of S.mutans,such as glucosyltransferases(Gtfs),antigen I/II(AgI/II),sortase A(SrtA),the VicRK signal transduction system and superoxide dismutases(SODs).Finally,we outline the current opportunities and challenges encountered in this field to aid future endeavors and applications of CADD and AIDD in anti-virulence drug design.展开更多
The evolution of cancer therapies has highlighted the limitations of traditional chemotherapy,particularly its lack of specificity and off-target toxicities,driving the development of targeted treatments like small mo...The evolution of cancer therapies has highlighted the limitations of traditional chemotherapy,particularly its lack of specificity and off-target toxicities,driving the development of targeted treatments like small molecule-drug conjugates(SMDCs).SMDCs offer distinct advantages over antibody-drug conjugates(ADCs),including simpler synthesis,lower production costs,and improved solid tumor penetration due to their smaller size.However,challenges remain,such as a limited variety of targeting ligands and the complexity of optimizing selectivity and efficacy within the tumor microenvironment.This review focuses on key aspects such as mechanisms of action,biomarker selection,and the optimization of each component of SMDCs.It also covers SMDCs that have been approved or are currently under active clinical trials,while providing insights into future developments in this promising field of targeted cancer therapies.展开更多
In the year 1971,the world’s biggest structural biology collaboration name—The Research Collaboratory for Structural Bioinformatics(RCSB),was formed to gather all the structural biologists at a single platform and t...In the year 1971,the world’s biggest structural biology collaboration name—The Research Collaboratory for Structural Bioinformatics(RCSB),was formed to gather all the structural biologists at a single platform and then extended out to be the world’s most extensive structural data repository named RCSB-Protein Data Bank(PDB)(https://www.rcsb.org/)that has provided the service for more than 50 years and continues its legacy for the discoveries and repositories for structural data.The RCSB has evolved from being a collaboratory network to a full-fledged database and tool with a huge list of protein structures,nucleic acid-containing structures,ModelArchive,and AlphaFold structures,and the best is that it is expanding day by day with computational advancement with tools and visual experiences.In this review article,we have discussed how RCSB has been a successful collaboratory network,its expansion in each decade,and how it has helped the ground-breaking research.The PDB tools that are helping the researchers,yearly data deposition,validation,processing,and suggestions that can help the developer improve for upcoming years are also discussed.This review will help future researchers understand the complete history of RCSB and its developments in each decade and how various future collaborative networks can be developed in various scientific areas and can be successful by keeping RCSB as a case study.展开更多
The mammalian target of rapamycin(mTOR) is a critical component of the PI3K-AKT signaling pathway. It is highly activated in cervical cancer, which continues to pose an important clinical challenge with an urgent need...The mammalian target of rapamycin(mTOR) is a critical component of the PI3K-AKT signaling pathway. It is highly activated in cervical cancer, which continues to pose an important clinical challenge with an urgent need for new and improved therapeutic approaches. Herein, we describe the structure-based drug discovery and biological evaluation of a series of m TOR kinase inhibitors as potential anti-cervical cancer agents. The results of enzymatic activity assays supported C3 as a potential m TOR inhibitor, which exhibited high inhibitory activity with an IC50 of 1.57 μM. Molecular docking and dynamics simulation were conducted to predict the binding patterns, suggesting relationships between structure and activity. The anti-proliferative assay against diverse cancer cell lines was displayed subsequently, revealing that C3 exhibited significant proliferation inhibition against cervical cancer cell He La(IC50=0.38μM) compared with other cell lines. Moreover, C3 could effectively reduce the expression of phospho-ribosomal S6 protein(p-S6) in He La cells in a dose-dependent manner. Noteworthily, m TOR signaling and other cellular pathways might contribute to the significant effect of C3 against cervical cancer simultaneously. These data indicated that C3 represented a good lead molecule for further development as a therapeutic agent for cervical cancer treatment.展开更多
Membrane transporters mediate the influx and efflux of various drugs,and play essential roles in drug absorption,distribution,metabolism and excretion（ADME）.The unique characteristics of membranes transporters poten...Membrane transporters mediate the influx and efflux of various drugs,and play essential roles in drug absorption,distribution,metabolism and excretion（ADME）.The unique characteristics of membranes transporters potentiate them as targets for developing drugs with ideal pharmacokinetics profiles,including targeted distribution,improved clinical efficacy and low adverse reaction.In this review,we summarize the tissue-specific expression,transport functions and substrates profiles of the major influx and efflux transporters,including solute carrier（SLC） superfamily and adenosine triphosphate（ATP）-binding cassette（ABC） superfamily.Moreover,we describe examples of successful drug or prodrug design based on the function of transporters that yielded drugs with excellent ADME properties.Lastly,we discuss the in vitro and in vivo methods that are broadly applied in the drug designing process to study the interactions between the drugs and the transporters.展开更多
Attributing to their broad pharmacological effects encompassing anti-inflammation,antitoxin,and immunosuppression,glucocorticoids(GCs)are extensively utilized in the clinic for the treatment of diverse diseases such a...Attributing to their broad pharmacological effects encompassing anti-inflammation,antitoxin,and immunosuppression,glucocorticoids(GCs)are extensively utilized in the clinic for the treatment of diverse diseases such as lupus erythematosus,nephritis,arthritis,ulcerative colitis,asthma,keratitis,macular edema,and leukemia.However,longterm use often causes undesirable side effects,including metabolic disorders-induced Cushing's syndrome(buffalo back,full moon face,hyperglycemia,etc.),osteoporosis,aggravated infection,psychosis,glaucoma,and cataract.These notorious side effects seriously compromise patients'quality of life,especially in patients with chronic diseases.Therefore,glucocorticoid-based advanced drug delivery systems for reducing adverse effects have received extensive attention.Among them,prodrugs have the advantages of low investment,low risk,and high success rate,making them a promising strategy.In this review,we propose the strategies for the design and summarize current research progress of glucocorticoid-based prodrugs in recent decades,including polymer-based prodrugs,dendrimer-based prodrugs,antibody-drug conjugates,peptide-drug conjugates,carbohydrate-based prodrugs,aliphatic acid-based prodrugs and so on.Besides,we also raise issues that need to be focused on during the development of glucocorticoid-based prodrugs.This review is expected to be helpful for the research and development of novel GCs and prodrugs.展开更多
Computer-aided drug design (CADD) is an interdisciplinary subject, playing a pivotal role during new drug research and development, especially the discovery and optimization of lead compounds. Traditional Chinese Medi...Computer-aided drug design (CADD) is an interdisciplinary subject, playing a pivotal role during new drug research and development, especially the discovery and optimization of lead compounds. Traditional Chinese Medicine (TCM) modernization is the only way of TCM development and also an effective approach to the development of new drugs and the discovery of potential drug targets (PDTs). Discovery and validation of PTDs has become the “bottle-neck” restricted new drug research and development and is urgently solved. Innovative drug research is of great significance and bright prospects. This paper mainly discusses the “druggability” and specificity of PTDs, the “druglikeness” of drug candidates, the methods and technologies of the discovery and validation of PTDs and their application. It is very important to achieve the invention and innovation strategy “from gene to drug”. In virtue of modern high-new technology, especially CADD, combined with TCM theory, research and develop TCM and initiate an innovating way fitting our country progress. This paper mainly discusses CADD and their application to drug research, especially TCM modernization.展开更多
The development of self-nanoemulsifying drug delivery systems(SNEDDS) to enhance the oral bioavailability of lipophilic drugs is usually based on traditional one-factor-at-a-time approaches. These approaches may be in...The development of self-nanoemulsifying drug delivery systems(SNEDDS) to enhance the oral bioavailability of lipophilic drugs is usually based on traditional one-factor-at-a-time approaches. These approaches may be inadequate to analyse the effect of each excipient and their potential interactions on the emulsion droplet size formed when dispersing the SNEDDS in an aqueous environment. The current study investigates the emulsion droplet sizes formed from SNEDDS containing different levels of the natural surfactant monoacyl phosphatidylcholine to reduce the concentration of the synthetic surfactant polyoxyl 40 hydrogenated castor oil(Kolliphor ~? RH40). Monoacyl phosphatidylcholine was used in the form of Lipoid S LPC 80(LPC, containing approximately 80% monoacyl phosphatidylcholine, 13% phosphatidylcholine and 4% concomitant components). The investigated SNEDDS comprised of long-chain or medium-chain glycerides(40% to 75%), Kolliphor ~? RH40(5% to 55%), LPC(0 to 40%) and ethanol(0 to 10%). D-optimal design, multiple linear regression, and partial least square regression were used to screen different SNEDDS within the investigated excipient ranges and to analyse the effect of each excipient on the resulting droplet size of the dispersed SNEDDS measured by dynamic light scattering. All investigated formulations formed nano-emulsions with droplet sizes from about 20 to 200 nm. The use of mediumchain glycerides was more likely to result in smaller and more monodisperse droplet sizes compared to the use of long-chain glycerides. Kolliphor~? RH40 exhibited the most significant effect on reducing the emulsion droplet sizes. Increasing LPC concentration increased the emulsion droplet sizes, possibly because of the reduction of Kolliphor~? RH40 concentration. A higher concentration of ethanol resulted in an insignificant reduction of the emulsion droplet size. The study provides different ternary diagrams of SNEDDS containing LPC and Kolliphor ~? RH40 as a reference for formulation developers.展开更多
Deamination is a crucial step in the transformation of 6-cyclopropylamino guanosine prodrug to its active form. A convenient method using capillary electrophoresis （CE） without sample labeling was developed to analy...Deamination is a crucial step in the transformation of 6-cyclopropylamino guanosine prodrug to its active form. A convenient method using capillary electrophoresis （CE） without sample labeling was developed to analyze the deamination of a series of D-/L-6-cyclopropylamino guanosine analogs by mouse liver homogenate, mouse liver microsome, and adenosine deaminase （ADA）. A two-step process involving a 6-amino guanosine intermediate formed by oxidative N-dealkylation was demonstrated in the metabolism of 6-cyclopropylamino guanosine to 6-hydroxy guanosine. The results indicated that the transformation rates of different prodrugs to the active form varied greatly, which were closely correlated with the configuration of nucleosides and the structure of glycosyl groups. Most importantly, D-form analogs were metabolized much faster than their L-counterparts, thus clearly pointed out that compared to guanine, modification of glycosyl part might be a better choice for the development of L-Kuanosine analogs for the treatment of HIV,展开更多
Many recent advances in biomedical research are related to the combination of biology and microengineering. Microfluidic devices, such as organ-on-a-chip systems, integrate with living cells to allow for the detailed ...Many recent advances in biomedical research are related to the combination of biology and microengineering. Microfluidic devices, such as organ-on-a-chip systems, integrate with living cells to allow for the detailed in vitro study of human physiology and pathophysiology. With the poor translation from animal models to human models, the organ-on-a-chip technology has become a promising substitute for animal testing, and their small scale enables precise control of culture conditions and high-throughput experiments, which would not be an economically sound model on a macroscopic level. These devices are becoming more and more common in research centers, clinics, and hospitals, and are contributing to more accurate studies and therapies, making them a staple technology for future drug design.展开更多
In the last few years, there have been important new insights into the structural biology of G-protein coupled receptors. It is now known that allosteric binding sites are involved in the affinity and selec- tivity of...In the last few years, there have been important new insights into the structural biology of G-protein coupled receptors. It is now known that allosteric binding sites are involved in the affinity and selec- tivity of ligands for G-protein coupled receptors, and that signaling by these receptors involves both G-protein dependent and independent pathways. The present review outlines the physiological and pharmacological implications of this perspective for the design of new drugs to treat disorders of the central nervous system. Specifically, new possibilities are explored in relation to allosteric and or- thosteric binding sites on dopamine receptors for the treatment of Parkinson＇s disease, and on muscarinic receptors for Alzheimer＇s disease. Future research can seek to identify ligands that can bind to more than one site on the same receptor, or simultaneously bind to two receptors and form a dimer. For example, the design of bivalent drugs that can reach homo/hetero-dimers of D2 dopa- mine receptor holds promise as a relevant therapeutic strategy for Parkinson＇s disease. Regarding the treatment of Alzheimer＇s disease, the design of dualsteric ligands for mono-oligomeric mus- carinic receptors could increase therapeutic effectiveness by generating potent compounds that could activate more than one signaling pathway.展开更多
Over the last decade,deep learning(DL)methods have been extremely successful and widely used in almost every domain.Researchers are now focusing on the convergence of medical imaging and drug design using deep learnin...Over the last decade,deep learning(DL)methods have been extremely successful and widely used in almost every domain.Researchers are now focusing on the convergence of medical imaging and drug design using deep learning to revolutionize medical diagnostic and improvement in the monitoring from response to therapy.DL a new machine learning paradigm that focuses on learning with deep hierarchical models of data.Medical imaging has transformed healthcare science,it was thought of as a diagnostic tool for disease,but now it is also used in drug design.Advances in medical imaging technology have enabled scientists to detect events at the cellular level.The role of medical imaging in drug design includes identification of likely responders,detection,diagnosis,evaluation,therapy monitoring,and follow-up.A qualitative medical image is transformed into a quantitative biomarker or surrogate endpoint useful in drug design decision-making.For this,a parameter needs to be identified that characterizes the disease baseline and its subsequent response to treatment.The result is a quantifiable improvement in healthcare quality in most therapeutic areas,resulting in improvements in quality and life duration.This paper provides an overview of recent studies on applying the deep learning method in medical imaging and drug design.We briefly discuss the fields related to the history of deep learning,medical imaging,and drug design.展开更多
Objectives： Computational study will help us in reducing the experimental work. The process of drug discovery involves the designing of molecules with appropriate pharmacophores with the help of various soft wares. T...Objectives： Computational study will help us in reducing the experimental work. The process of drug discovery involves the designing of molecules with appropriate pharmacophores with the help of various soft wares. The purpose of this paper is to study the probable binding modes of fatty acids on fatty acids after enzymatic hydrolysis of the FAAH （fatty acid amide hydrolase） in different extracts of flowers, leaves, stem bark, root bark and nuts of Semecarpus anacardiurn L. f. by using molecular modeling study and computer assisted drug designing. Nuts yielded 20 fatty acids including saturated, ω-3 unsaturated, ω-6 unsaturated, ω-7 unsaturated and ω-9 unsaturated fatty acids. Based on IR, IH NMR, 13C NMR, MS （mass） spectrometry, GC analysis, the structural elucidation of these isolated fatty acids was established. Methods： A dataset comprising of 20 fatty acids were drawn in ChemDraw and converted into 3D-molecules with all possible tautomers and chiral centers. The minimization of molecules was carried out using PRCG （Polak-Ribiere Conjugate Gradient） method with maximum of 5000 iterations. The minimized compounds were used for protein preparation. The crystal structure of human FAAH （PDB ID： 3K84） is prepared and selected for the docking studies of 20 fatty acids using Schr6dinger docking program module.. Conclusions： In this study, we carried out the molecular docking studies in order to understand the probable binding mode of 20 fatty acids in FAAH from which we identified key active site residues for FAAH, thereby it can be used to design the novel compounds for FAAH targets.展开更多
Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the...Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.展开更多
In the realm of drug discovery,recent advancements have paved the way for innovative approaches and methodologies.This comprehensive review encapsulates six distinct yet interrelated mini-reviews,each shedding light o...In the realm of drug discovery,recent advancements have paved the way for innovative approaches and methodologies.This comprehensive review encapsulates six distinct yet interrelated mini-reviews,each shedding light on novel strategies in drug development.(a)The resurgence of covalent drugs is highlighted,focusing on the targeted covalent inhibitors(TCIs)and their role in enhancing selectivity and affinity.(b)The potential of the quantum mechanics-based computational aid drug design(CADD)tool,Cov_DOX,is introduced for predicting protein-covalent ligand binding structures and affinities.(c)The scaffolding function of proteins is proposed as a new avenue for drug design,with a focus on modulating protein-protein interactions through small molecules and proteolysis targeting chimeras(PROTACs).(d)The concept of pro-PROTACs is explored as a promising strategy for cancer therapy,combining the principles of prodrugs and PROTACs to enhance specificity and reduce toxicity.(e)The design of prodrugs through carbon-carbon bond cleavage is discussed,offering a new perspective for the activation of drugs with limited modifiable functional groups.(f)The targeting of programmed cell death pathways in cancer therapies with small molecules is reviewed,emphasizing the induction of autophagy-dependent cell death,ferroptosis,and cuproptosis.These insights collectively contribute to a deeper understanding of the dynamic landscape of drug discovery.展开更多
Introduction Since the 21st century,the biomedical field has gained increasing attention.The biomedical field mainly encompasses biology,materials science,pharmacology,and drug delivery,etc.These areas hold significan...Introduction Since the 21st century,the biomedical field has gained increasing attention.The biomedical field mainly encompasses biology,materials science,pharmacology,and drug delivery,etc.These areas hold significant importance for human society in terms of health protection,disease diagnosis and treatment,via medical technology innovation and drug development.Consequently,scientists place great emphasis on research in this domain.It must be noted that the research process in biomedicine mainly includes topic selection,experimentation,analysis,and summary.Among these,topic selection is a critical step that affects the entire process.This topic selection not only clarifies the direction and objectives of the study but also provides a clear framework for subsequent research,thereby ensuring scientific rigor and effectiveness while laying a solid foundation for the result analysis.Thus,how to approach topic selection is a crucial issue that requires careful consideration.展开更多
文摘The three-dimensional structure of a biomolecule rather than its one-dimensionM sequence determines its biological function. At present, the most accurate structures are derived from experimental data measured mainly by two techniques： X-ray crystallog- raphy and nuclear magnetic resonance （NMR） spec- troscopy. Because neither X-ray crystallography nor NMR spectroscopy could directly measure the positions of atoms in a biomolecule, algorithms must be designed to compute atom coordinates from the data. One salient feature of most NMR structure computation algorithms is their reliance on stochastic search to find the lowest energy conformations that satisfy the experimentally- derived geometric restraints. However, neither the cor- rectness of the stochastic search has been established nor the errors in the output structures could be quantified. Though there exist exact algorithms to compute struc- tures from angular restraints, similar algorithms that use distance restraints remain to be developed. An important application of structures is rational drug design where protein-ligand docking plays a crit- ical role. In fact, various docking programs that place a compound into the binding site of a target protein have been used routinely by medicinal chemists for both lead identification and optimization. Unfortunately, de- spite ongoing methodological advances and some success stories, the performance of current docking algorithms is still data-dependent. These algorithms formulate the docking problem as a match of two sets of feature points. Both the selection of feature points and the search for the best poses with the minimum scores are accomplished through some stochastic search methods. Both the un- certainty in the scoring function and the limited sam- pling space attained by the stochastic search contribute to their failures. Recently, we have developed two novel docking algorithms： a data-driven docking algorithm and a general docking algorithm that does not rely on experimental data. Our algorithms search the pose space exhaustively with the pose space itself being limited to a set of hierarchical manifolds that represent, respectively, surfaces, curves and points with unique geometric and energetic properties. These algorithms promise to be es- pecially valuable for the docking of fragments and small compounds as well as for virtual screening.
基金National Natural Science Foundation of China (Grant No.20772008 and 30772650)
文摘Based upon the crystal structure of a previously reported fragment hit that binds to Corresponding author. β-secretase, a novel series of non-peptidic small-molecule β-secretase inhibitors, namely hexahydropyrimidin-5-ols, along with two series of their analogues, were rationally designed through structural modification. The CADD study was performed and revealed good expectation. Inhibitory activities of the corresponding structural cores were tested, which provided further support for our design approach.
基金supported by the National Basic Research Program of China(No.2004CB518908)the National High Technology Research and Development Program of China(No.2006AA020601)
文摘Mycobacterium tuberculosis FabH, an essential enzyme in mycolic acids biosynthetic pathway, is an attractive target for novel anti-tuberculosis agents. Structure-based design, synthesis of novel inhibitors of mtFabH was reported in this paper. A novel scaffold structure was designed, and 12 candidate compounds that displayed favorable binding with the active site were identified and synthesized. 2009 Song Li. Published by Elsevier B.V. on behalf of Chinese Chemical Society. All rights reserved.
基金supported by the Sichuan Science and Technology Program,China(Grant Nos.:2023ZYD0105 and 2023YFS0343)。
文摘Dental caries,a chronic disease characterized by tooth decay,occupies the second position in terms of disease burden and is primarily caused by cariogenic bacteria,especially Streptococcus mutans,because of its acidogenic,aciduric,and biofilm-forming capabilities.Developing novel targeted anti-virulence agents is always a focal point in caries control to overcome the limitations of conventional anti-virulence agents.The current study represents an up-to-date review of in silico approaches of drug design against dental caries,which have emerged more and more powerful complementary to biochemical attempts.Firstly,we categorize the in silico approaches into computer-aided drug design(CADD)and AI-assisted drug design(AIDD)and highlight the specific methods and models they contain respectively.Subsequently,we detail the design of anti-virulence drugs targeting single or multiple cariogenic virulence targets of S.mutans,such as glucosyltransferases(Gtfs),antigen I/II(AgI/II),sortase A(SrtA),the VicRK signal transduction system and superoxide dismutases(SODs).Finally,we outline the current opportunities and challenges encountered in this field to aid future endeavors and applications of CADD and AIDD in anti-virulence drug design.
基金the financial support from the National Natural Science Foundation of China(Nos.82473781,82173652 and 81872728)the Natural Science Foundation of Jiangsu Province(No.BK20221522)Support from Jiangsu“333 High Level Talents Cultivation”Leading Talents(No.2022–3–16–203)。
文摘The evolution of cancer therapies has highlighted the limitations of traditional chemotherapy,particularly its lack of specificity and off-target toxicities,driving the development of targeted treatments like small molecule-drug conjugates(SMDCs).SMDCs offer distinct advantages over antibody-drug conjugates(ADCs),including simpler synthesis,lower production costs,and improved solid tumor penetration due to their smaller size.However,challenges remain,such as a limited variety of targeting ligands and the complexity of optimizing selectivity and efficacy within the tumor microenvironment.This review focuses on key aspects such as mechanisms of action,biomarker selection,and the optimization of each component of SMDCs.It also covers SMDCs that have been approved or are currently under active clinical trials,while providing insights into future developments in this promising field of targeted cancer therapies.
文摘In the year 1971,the world’s biggest structural biology collaboration name—The Research Collaboratory for Structural Bioinformatics(RCSB),was formed to gather all the structural biologists at a single platform and then extended out to be the world’s most extensive structural data repository named RCSB-Protein Data Bank(PDB)(https://www.rcsb.org/)that has provided the service for more than 50 years and continues its legacy for the discoveries and repositories for structural data.The RCSB has evolved from being a collaboratory network to a full-fledged database and tool with a huge list of protein structures,nucleic acid-containing structures,ModelArchive,and AlphaFold structures,and the best is that it is expanding day by day with computational advancement with tools and visual experiences.In this review article,we have discussed how RCSB has been a successful collaboratory network,its expansion in each decade,and how it has helped the ground-breaking research.The PDB tools that are helping the researchers,yearly data deposition,validation,processing,and suggestions that can help the developer improve for upcoming years are also discussed.This review will help future researchers understand the complete history of RCSB and its developments in each decade and how various future collaborative networks can be developed in various scientific areas and can be successful by keeping RCSB as a case study.
基金National Natural Science Foundation of China(Grant No.21772005,81872730)the Beijing Natural Science Foundation(Grant No.7202088,7172118)。
文摘The mammalian target of rapamycin(mTOR) is a critical component of the PI3K-AKT signaling pathway. It is highly activated in cervical cancer, which continues to pose an important clinical challenge with an urgent need for new and improved therapeutic approaches. Herein, we describe the structure-based drug discovery and biological evaluation of a series of m TOR kinase inhibitors as potential anti-cervical cancer agents. The results of enzymatic activity assays supported C3 as a potential m TOR inhibitor, which exhibited high inhibitory activity with an IC50 of 1.57 μM. Molecular docking and dynamics simulation were conducted to predict the binding patterns, suggesting relationships between structure and activity. The anti-proliferative assay against diverse cancer cell lines was displayed subsequently, revealing that C3 exhibited significant proliferation inhibition against cervical cancer cell He La(IC50=0.38μM) compared with other cell lines. Moreover, C3 could effectively reduce the expression of phospho-ribosomal S6 protein(p-S6) in He La cells in a dose-dependent manner. Noteworthily, m TOR signaling and other cellular pathways might contribute to the significant effect of C3 against cervical cancer simultaneously. These data indicated that C3 represented a good lead molecule for further development as a therapeutic agent for cervical cancer treatment.
基金National Science and Technology Major Project(Grant No. 2012ZX09506001-004)
文摘Membrane transporters mediate the influx and efflux of various drugs,and play essential roles in drug absorption,distribution,metabolism and excretion（ADME）.The unique characteristics of membranes transporters potentiate them as targets for developing drugs with ideal pharmacokinetics profiles,including targeted distribution,improved clinical efficacy and low adverse reaction.In this review,we summarize the tissue-specific expression,transport functions and substrates profiles of the major influx and efflux transporters,including solute carrier（SLC） superfamily and adenosine triphosphate（ATP）-binding cassette（ABC） superfamily.Moreover,we describe examples of successful drug or prodrug design based on the function of transporters that yielded drugs with excellent ADME properties.Lastly,we discuss the in vitro and in vivo methods that are broadly applied in the drug designing process to study the interactions between the drugs and the transporters.
基金supported by the National Natural Science Foundation of China[82172086]National Key R&D Program of China[2020YFE0201700]+2 种基金Shenyang Science and Technology Talent Support Program[RC210447]Career Development Program for Young and Middle-aged Teachers of Shenyang Pharmaceutical University[ZQN2019004]“Dual Service”Program of University in Shenyang。
文摘Attributing to their broad pharmacological effects encompassing anti-inflammation,antitoxin,and immunosuppression,glucocorticoids(GCs)are extensively utilized in the clinic for the treatment of diverse diseases such as lupus erythematosus,nephritis,arthritis,ulcerative colitis,asthma,keratitis,macular edema,and leukemia.However,longterm use often causes undesirable side effects,including metabolic disorders-induced Cushing's syndrome(buffalo back,full moon face,hyperglycemia,etc.),osteoporosis,aggravated infection,psychosis,glaucoma,and cataract.These notorious side effects seriously compromise patients'quality of life,especially in patients with chronic diseases.Therefore,glucocorticoid-based advanced drug delivery systems for reducing adverse effects have received extensive attention.Among them,prodrugs have the advantages of low investment,low risk,and high success rate,making them a promising strategy.In this review,we propose the strategies for the design and summarize current research progress of glucocorticoid-based prodrugs in recent decades,including polymer-based prodrugs,dendrimer-based prodrugs,antibody-drug conjugates,peptide-drug conjugates,carbohydrate-based prodrugs,aliphatic acid-based prodrugs and so on.Besides,we also raise issues that need to be focused on during the development of glucocorticoid-based prodrugs.This review is expected to be helpful for the research and development of novel GCs and prodrugs.
文摘Computer-aided drug design (CADD) is an interdisciplinary subject, playing a pivotal role during new drug research and development, especially the discovery and optimization of lead compounds. Traditional Chinese Medicine (TCM) modernization is the only way of TCM development and also an effective approach to the development of new drugs and the discovery of potential drug targets (PDTs). Discovery and validation of PTDs has become the “bottle-neck” restricted new drug research and development and is urgently solved. Innovative drug research is of great significance and bright prospects. This paper mainly discusses the “druggability” and specificity of PTDs, the “druglikeness” of drug candidates, the methods and technologies of the discovery and validation of PTDs and their application. It is very important to achieve the invention and innovation strategy “from gene to drug”. In virtue of modern high-new technology, especially CADD, combined with TCM theory, research and develop TCM and initiate an innovating way fitting our country progress. This paper mainly discusses CADD and their application to drug research, especially TCM modernization.
基金Financial support from the University of Copenhagen and the Phospholipid Research Center(Heidelberg,Germany)is kindly acknowledged
文摘The development of self-nanoemulsifying drug delivery systems(SNEDDS) to enhance the oral bioavailability of lipophilic drugs is usually based on traditional one-factor-at-a-time approaches. These approaches may be inadequate to analyse the effect of each excipient and their potential interactions on the emulsion droplet size formed when dispersing the SNEDDS in an aqueous environment. The current study investigates the emulsion droplet sizes formed from SNEDDS containing different levels of the natural surfactant monoacyl phosphatidylcholine to reduce the concentration of the synthetic surfactant polyoxyl 40 hydrogenated castor oil(Kolliphor ~? RH40). Monoacyl phosphatidylcholine was used in the form of Lipoid S LPC 80(LPC, containing approximately 80% monoacyl phosphatidylcholine, 13% phosphatidylcholine and 4% concomitant components). The investigated SNEDDS comprised of long-chain or medium-chain glycerides(40% to 75%), Kolliphor ~? RH40(5% to 55%), LPC(0 to 40%) and ethanol(0 to 10%). D-optimal design, multiple linear regression, and partial least square regression were used to screen different SNEDDS within the investigated excipient ranges and to analyse the effect of each excipient on the resulting droplet size of the dispersed SNEDDS measured by dynamic light scattering. All investigated formulations formed nano-emulsions with droplet sizes from about 20 to 200 nm. The use of mediumchain glycerides was more likely to result in smaller and more monodisperse droplet sizes compared to the use of long-chain glycerides. Kolliphor~? RH40 exhibited the most significant effect on reducing the emulsion droplet sizes. Increasing LPC concentration increased the emulsion droplet sizes, possibly because of the reduction of Kolliphor~? RH40 concentration. A higher concentration of ethanol resulted in an insignificant reduction of the emulsion droplet size. The study provides different ternary diagrams of SNEDDS containing LPC and Kolliphor ~? RH40 as a reference for formulation developers.
基金supported by National Natural Science Foundation of China (NSFC) (Nos.21172010,21002004)
文摘Deamination is a crucial step in the transformation of 6-cyclopropylamino guanosine prodrug to its active form. A convenient method using capillary electrophoresis （CE） without sample labeling was developed to analyze the deamination of a series of D-/L-6-cyclopropylamino guanosine analogs by mouse liver homogenate, mouse liver microsome, and adenosine deaminase （ADA）. A two-step process involving a 6-amino guanosine intermediate formed by oxidative N-dealkylation was demonstrated in the metabolism of 6-cyclopropylamino guanosine to 6-hydroxy guanosine. The results indicated that the transformation rates of different prodrugs to the active form varied greatly, which were closely correlated with the configuration of nucleosides and the structure of glycosyl groups. Most importantly, D-form analogs were metabolized much faster than their L-counterparts, thus clearly pointed out that compared to guanine, modification of glycosyl part might be a better choice for the development of L-Kuanosine analogs for the treatment of HIV,
文摘Many recent advances in biomedical research are related to the combination of biology and microengineering. Microfluidic devices, such as organ-on-a-chip systems, integrate with living cells to allow for the detailed in vitro study of human physiology and pathophysiology. With the poor translation from animal models to human models, the organ-on-a-chip technology has become a promising substitute for animal testing, and their small scale enables precise control of culture conditions and high-throughput experiments, which would not be an economically sound model on a macroscopic level. These devices are becoming more and more common in research centers, clinics, and hospitals, and are contributing to more accurate studies and therapies, making them a staple technology for future drug design.
基金supported by SIP-IPN,CONACYT (CB-168116)FIS/IMSS (FIS/IMSS/PROT/G11-2/1013)
文摘In the last few years, there have been important new insights into the structural biology of G-protein coupled receptors. It is now known that allosteric binding sites are involved in the affinity and selec- tivity of ligands for G-protein coupled receptors, and that signaling by these receptors involves both G-protein dependent and independent pathways. The present review outlines the physiological and pharmacological implications of this perspective for the design of new drugs to treat disorders of the central nervous system. Specifically, new possibilities are explored in relation to allosteric and or- thosteric binding sites on dopamine receptors for the treatment of Parkinson＇s disease, and on muscarinic receptors for Alzheimer＇s disease. Future research can seek to identify ligands that can bind to more than one site on the same receptor, or simultaneously bind to two receptors and form a dimer. For example, the design of bivalent drugs that can reach homo/hetero-dimers of D2 dopa- mine receptor holds promise as a relevant therapeutic strategy for Parkinson＇s disease. Regarding the treatment of Alzheimer＇s disease, the design of dualsteric ligands for mono-oligomeric mus- carinic receptors could increase therapeutic effectiveness by generating potent compounds that could activate more than one signaling pathway.
文摘Over the last decade,deep learning(DL)methods have been extremely successful and widely used in almost every domain.Researchers are now focusing on the convergence of medical imaging and drug design using deep learning to revolutionize medical diagnostic and improvement in the monitoring from response to therapy.DL a new machine learning paradigm that focuses on learning with deep hierarchical models of data.Medical imaging has transformed healthcare science,it was thought of as a diagnostic tool for disease,but now it is also used in drug design.Advances in medical imaging technology have enabled scientists to detect events at the cellular level.The role of medical imaging in drug design includes identification of likely responders,detection,diagnosis,evaluation,therapy monitoring,and follow-up.A qualitative medical image is transformed into a quantitative biomarker or surrogate endpoint useful in drug design decision-making.For this,a parameter needs to be identified that characterizes the disease baseline and its subsequent response to treatment.The result is a quantifiable improvement in healthcare quality in most therapeutic areas,resulting in improvements in quality and life duration.This paper provides an overview of recent studies on applying the deep learning method in medical imaging and drug design.We briefly discuss the fields related to the history of deep learning,medical imaging,and drug design.
文摘Objectives： Computational study will help us in reducing the experimental work. The process of drug discovery involves the designing of molecules with appropriate pharmacophores with the help of various soft wares. The purpose of this paper is to study the probable binding modes of fatty acids on fatty acids after enzymatic hydrolysis of the FAAH （fatty acid amide hydrolase） in different extracts of flowers, leaves, stem bark, root bark and nuts of Semecarpus anacardiurn L. f. by using molecular modeling study and computer assisted drug designing. Nuts yielded 20 fatty acids including saturated, ω-3 unsaturated, ω-6 unsaturated, ω-7 unsaturated and ω-9 unsaturated fatty acids. Based on IR, IH NMR, 13C NMR, MS （mass） spectrometry, GC analysis, the structural elucidation of these isolated fatty acids was established. Methods： A dataset comprising of 20 fatty acids were drawn in ChemDraw and converted into 3D-molecules with all possible tautomers and chiral centers. The minimization of molecules was carried out using PRCG （Polak-Ribiere Conjugate Gradient） method with maximum of 5000 iterations. The minimized compounds were used for protein preparation. The crystal structure of human FAAH （PDB ID： 3K84） is prepared and selected for the docking studies of 20 fatty acids using Schr6dinger docking program module.. Conclusions： In this study, we carried out the molecular docking studies in order to understand the probable binding mode of 20 fatty acids in FAAH from which we identified key active site residues for FAAH, thereby it can be used to design the novel compounds for FAAH targets.
基金supported in part by National Institute of Health(NIH),USA(Grant Nos.:R01GM126189,R01AI164266,and R35GM148196)the National Science Foundation,USA(Grant Nos.DMS2052983,DMS-1761320,and IIS-1900473)+3 种基金National Aero-nautics and Space Administration(NASA),USA(Grant No.:80NSSC21M0023)Michigan State University(MSU)Foundation,USA,Bristol-Myers Squibb(Grant No.:65109)USA,and Pfizer,USAsupported by the National Natural Science Foundation of China(Grant Nos.:11971367,12271416,and 11972266).
文摘Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.
基金supported by grants from the National Natural Science Foundation of China(No.82273770)the Foundation for Innovative Research Groups of the National Natural Science Foundation of Sichuan Province(No.24NSFTD0051).
文摘In the realm of drug discovery,recent advancements have paved the way for innovative approaches and methodologies.This comprehensive review encapsulates six distinct yet interrelated mini-reviews,each shedding light on novel strategies in drug development.(a)The resurgence of covalent drugs is highlighted,focusing on the targeted covalent inhibitors(TCIs)and their role in enhancing selectivity and affinity.(b)The potential of the quantum mechanics-based computational aid drug design(CADD)tool,Cov_DOX,is introduced for predicting protein-covalent ligand binding structures and affinities.(c)The scaffolding function of proteins is proposed as a new avenue for drug design,with a focus on modulating protein-protein interactions through small molecules and proteolysis targeting chimeras(PROTACs).(d)The concept of pro-PROTACs is explored as a promising strategy for cancer therapy,combining the principles of prodrugs and PROTACs to enhance specificity and reduce toxicity.(e)The design of prodrugs through carbon-carbon bond cleavage is discussed,offering a new perspective for the activation of drugs with limited modifiable functional groups.(f)The targeting of programmed cell death pathways in cancer therapies with small molecules is reviewed,emphasizing the induction of autophagy-dependent cell death,ferroptosis,and cuproptosis.These insights collectively contribute to a deeper understanding of the dynamic landscape of drug discovery.
文摘Introduction Since the 21st century,the biomedical field has gained increasing attention.The biomedical field mainly encompasses biology,materials science,pharmacology,and drug delivery,etc.These areas hold significant importance for human society in terms of health protection,disease diagnosis and treatment,via medical technology innovation and drug development.Consequently,scientists place great emphasis on research in this domain.It must be noted that the research process in biomedicine mainly includes topic selection,experimentation,analysis,and summary.Among these,topic selection is a critical step that affects the entire process.This topic selection not only clarifies the direction and objectives of the study but also provides a clear framework for subsequent research,thereby ensuring scientific rigor and effectiveness while laying a solid foundation for the result analysis.Thus,how to approach topic selection is a crucial issue that requires careful consideration.
