Journal of Computer-Aided Molecular Design最新文献_第3页

Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design 丙烯酰胺弹头对半胱氨酸目标的反应活性：共价抑制剂设计的 QM/ML 方法

IF 3 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2024-05-01 DOI: 10.1007/s10822-024-00560-6

Aaron D. Danilack, Callum J. Dickson, Cihan Soylu, Mike Fortunato, Stephane Rodde, Hagen Munkler, Viktor Hornak, Jose S. Duca

{"title":"Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design","authors":"Aaron D. Danilack, Callum J. Dickson, Cihan Soylu, Mike Fortunato, Stephane Rodde, Hagen Munkler, Viktor Hornak, Jose S. Duca","doi":"10.1007/s10822-024-00560-6","DOIUrl":"10.1007/s10822-024-00560-6","url":null,"abstract":"<div><p>Covalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain potency while avoiding unwanted side effects. While warhead reactivities are commonly measured with assays, a computational model to predict warhead reactivities could be useful for several aspects of the covalent inhibitor design process. Studies have shown correlations between covalent warhead reactivities and quantum mechanic (QM) properties that describe important aspects of the covalent reaction mechanism. However, the models from these studies are often linear regression equations and can have limitations associated with their usage. Applications of machine learning (ML) models to predict covalent warhead reactivities with QM descriptors are not extensively seen in the literature. This study uses QM descriptors, calculated at different levels of theory, to train ML models to predict reactivities of covalent acrylamide warheads. The QM/ML models are compared with linear regression models built upon the same QM descriptors and with ML models trained on structure-based features like Morgan fingerprints and RDKit descriptors. Experiments show that the QM/ML models outperform the linear regression models and the structure-based ML models, and literature test sets demonstrate the power of the QM/ML models to predict reactivities of unseen acrylamide warhead scaffolds. Ultimately, these QM/ML models are effective, computationally feasible tools that can expedite the design of new covalent inhibitors.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"38 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140836933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

De novo drug design as GPT language modeling: large chemistry models with supervised and reinforcement learning 作为 GPT 语言建模的新药设计：采用监督和强化学习的大型化学模型

IF 3 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2024-04-22 DOI: 10.1007/s10822-024-00559-z

Gavin Ye

{"title":"De novo drug design as GPT language modeling: large chemistry models with supervised and reinforcement learning","authors":"Gavin Ye","doi":"10.1007/s10822-024-00559-z","DOIUrl":"10.1007/s10822-024-00559-z","url":null,"abstract":"<div><p>In recent years, generative machine learning algorithms have been successful in designing innovative drug-like molecules. SMILES is a sequence-like language used in most effective drug design models. Due to data’s sequential structure, models such as recurrent neural networks and transformers can design pharmacological compounds with optimized efficacy. Large language models have advanced recently, but their implications on drug design have not yet been explored. Although one study successfully pre-trained a <i>large chemistry model</i> (LCM), its application to specific tasks in drug discovery is unknown. In this study, the drug design task is modeled as a causal language modeling problem. Thus, the procedure of reward modeling, supervised fine-tuning, and proximal policy optimization was used to transfer the LCM to drug design, similar to Open AI’s ChatGPT and InstructGPT procedures. By combining the SMILES sequence with chemical descriptors, the novel efficacy evaluation model exceeded its performance compared to previous studies. After proximal policy optimization, the drug design model generated molecules with 99.2% having efficacy pIC<sub>50</sub> > 7 towards the amyloid precursor protein, with 100% of the generated molecules being valid and novel. This demonstrated the applicability of LCMs in drug discovery, with benefits including less data consumption while fine-tuning. The applicability of LCMs to drug discovery opens the door for larger studies involving reinforcement-learning with human feedback, where chemists provide feedback to LCMs and generate higher-quality molecules. LCMs’ ability to design similar molecules from datasets paves the way for more accessible, non-patented alternatives to drug molecules.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"38 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-024-00559-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140673020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From UK-2A to florylpicoxamid: Active learning to identify a mimic of a macrocyclic natural product 从UK-2A到氟啶虫酰胺：主动学习识别大环天然产物的模拟物

IF 3 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2024-04-17 DOI: 10.1007/s10822-024-00555-3

Ann E. Cleves, Ajay N. Jain, David A. Demeter, Zachary A. Buchan, Jeremy Wilmot, Erin N. Hancock

{"title":"From UK-2A to florylpicoxamid: Active learning to identify a mimic of a macrocyclic natural product","authors":"Ann E. Cleves, Ajay N. Jain, David A. Demeter, Zachary A. Buchan, Jeremy Wilmot, Erin N. Hancock","doi":"10.1007/s10822-024-00555-3","DOIUrl":"10.1007/s10822-024-00555-3","url":null,"abstract":"<div><p>Scaffold replacement as part of an optimization process that requires maintenance of potency, desirable biodistribution, metabolic stability, and considerations of synthesis at very large scale is a complex challenge. Here, we consider a set of over 1000 time-stamped compounds, beginning with a macrocyclic natural-product lead and ending with a broad-spectrum crop anti-fungal. We demonstrate the application of the QuanSA 3D-QSAR method employing an active learning procedure that combines two types of molecular selection. The first identifies compounds predicted to be most active of those most likely to be well-covered by the model. The second identifies compounds predicted to be most <i>informative</i> based on exhibiting low predicted activity but showing high 3D similarity to a highly active nearest-neighbor training molecule. Beginning with just 100 compounds, using a deterministic and automatic procedure, five rounds of 20-compound selection and model refinement identifies the binding metabolic form of florylpicoxamid. We show how iterative refinement broadens the domain of applicability of the successive models while also enhancing predictive accuracy. We also demonstrate how a simple method requiring very sparse data can be used to generate relevant ideas for synthetic candidates.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"38 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-024-00555-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140614475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the relevance of query definition in the performance of 3D ligand-based virtual screening 基于三维配体的虚拟筛选性能中查询定义的相关性

IF 3 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2024-04-04 DOI: 10.1007/s10822-024-00561-5

Javier Vázquez, Ricardo García, Paula Llinares, F. Javier Luque, Enric Herrero

{"title":"On the relevance of query definition in the performance of 3D ligand-based virtual screening","authors":"Javier Vázquez, Ricardo García, Paula Llinares, F. Javier Luque, Enric Herrero","doi":"10.1007/s10822-024-00561-5","DOIUrl":"10.1007/s10822-024-00561-5","url":null,"abstract":"<div><p>Ligand-based virtual screening (LBVS) methods are widely used to explore the vast chemical space in the search of novel compounds resorting to a variety of properties encoded in 1D, 2D or 3D descriptors. The success of 3D-LBVS is affected by the overlay of molecular pairs, thus making selection of the template compound, search of accessible conformational space and choice of the query conformation to be potential factors that modulate the successful retrieval of actives. This study examines the impact of adopting different choices for the query conformation of the template, paying also attention to the influence exerted by the structural similarity between templates and actives. The analysis is performed using PharmScreen, a 3D LBVS tool that relies on similarity measurements of the hydrophobic/philic pattern of molecules, and Phase Shape, which is based on the alignment of atom triplets followed by refinement of the volume overlap. The study is performed for the original DUD-E<sup>+</sup> database and a Morgan Fingerprint filtered version (denoted DUD-E<sup>+</sup>-Diverse; available in https://github.com/Pharmacelera/Query-models-to-3DLBVS), which was prepared to minimize the 2D resemblance between template and actives. Although in most cases the query conformation exhibits a mild influence on the overall performance, a critical analysis is made to disclose factors, such as the content of structural features between template and actives and the induction of conformational strain in the template, that underlie the drastic impact of the query definition in the recovery of actives for certain targets. The findings of this research also provide valuable guidance for assisting the selection of the query definition in 3D LBVS campaigns.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"38 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-024-00561-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140568082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational peptide discovery with a genetic programming approach 利用遗传编程方法计算肽的发现

IF 3 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2024-04-03 DOI: 10.1007/s10822-024-00558-0

Nicolas Scalzitti, Iliya Miralavy, David E. Korenchan, Christian T. Farrar, Assaf A. Gilad, Wolfgang Banzhaf

{"title":"Computational peptide discovery with a genetic programming approach","authors":"Nicolas Scalzitti, Iliya Miralavy, David E. Korenchan, Christian T. Farrar, Assaf A. Gilad, Wolfgang Banzhaf","doi":"10.1007/s10822-024-00558-0","DOIUrl":"10.1007/s10822-024-00558-0","url":null,"abstract":"<div><p>The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search spaces that need to be considered. <i>In silico</i> methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and can facilitate the discovery of new peptides. This study presents the development and use of a new variant of the genetic-programming-based POET algorithm, called POET<sub><i>Regex</i></sub>, where individuals are represented by a list of regular expressions. This algorithm was trained on a small curated dataset and employed to generate new peptides improving the sensitivity of peptides in magnetic resonance imaging with chemical exchange saturation transfer (CEST). The resulting model achieves a performance gain of 20% over the initial POET models and is able to predict a candidate peptide with a 58% performance increase compared to the gold-standard peptide. By combining the power of genetic programming with the flexibility of regular expressions, new peptide targets were identified that improve the sensitivity of detection by CEST. This approach provides a promising research direction for the efficient identification of peptides with therapeutic or diagnostic potential.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"38 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-024-00558-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140568079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying and characterising promising small molecule inhibitors of kinesin spindle protein using ligand-based virtual screening, molecular docking, molecular dynamics and MM‑GBSA calculations 利用基于配体的虚拟筛选、分子对接、分子动力学和 MM-GBSA 计算，确定并表征有前途的驱动蛋白纺锤体小分子抑制剂。

IF 3 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2024-04-01 DOI: 10.1007/s10822-024-00553-5

Samia A. Elseginy

{"title":"Identifying and characterising promising small molecule inhibitors of kinesin spindle protein using ligand-based virtual screening, molecular docking, molecular dynamics and MM‑GBSA calculations","authors":"Samia A. Elseginy","doi":"10.1007/s10822-024-00553-5","DOIUrl":"10.1007/s10822-024-00553-5","url":null,"abstract":"<div><p>The kinesin spindle protein (Eg5) is a mitotic protein that plays an essential role in the formation of the bipolar spindles during the mitotic phase. Eg5 protein controls the segregation of the chromosomes in mitosis which renders it a vital target for cancer treatment. In this study our approach to identifying novel scaffold for Eg5 inhibitors is based on targeting the novel allosteric pocket (α4/α6/L11). Extensive computational techniques were applied using ligand-based virtual screening and molecular docking by two approaches, MOE and AutoDock, to screen a library of commercial compounds. We identified compound 8-(3-(1H-imidazol-1-ylpropylamino)-3-methyl-7-((naphthalen-3-yl)methyl)-1H-purine-2, 6 (3H,7H)-dione (compound 5) as a novel scaffold for Eg5 inhibitors. This compound inhibited cancer cell Eg5 ATPase at 2.37 ± 0.15 µM. The molecular dynamics simulations revealed that the identified compound formed stable interactions in the allosteric pocket (α4/α6/L11) of the receptor, indicating its potential as a novel Eg5 inhibitor.</p><h3>Graphical Abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"38 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10982093/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140331462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Benchmarking ANI potentials as a rescoring function and screening FDA drugs for SARS-CoV-2 Mpro 将 ANI 电位作为重构函数的基准，并筛选用于 SARS-CoV-2 Mpro 的 FDA 药物。

IF 3 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2024-03-27 DOI: 10.1007/s10822-024-00554-4

Irem N. Zengin, M. Serdar Koca, Omer Tayfuroglu, Muslum Yildiz, Abdulkadir Kocak

{"title":"Benchmarking ANI potentials as a rescoring function and screening FDA drugs for SARS-CoV-2 Mpro","authors":"Irem N. Zengin, M. Serdar Koca, Omer Tayfuroglu, Muslum Yildiz, Abdulkadir Kocak","doi":"10.1007/s10822-024-00554-4","DOIUrl":"10.1007/s10822-024-00554-4","url":null,"abstract":"<div><p>Here, we introduce the use of ANI-ML potentials as a rescoring function in the host–guest interaction in molecular docking. Our results show that the “docking power” of ANI potentials can compete with the current scoring functions at the same level of computational cost. Benchmarking studies on CASF-2016 dataset showed that ANI is ranked in the top 5 scoring functions among the other 34 tested. In particular, the ANI predicted interaction energies when used in conjunction with GOLD-PLP scoring function can boost the top ranked solution to be the closest to the x-ray structure. Rapid and accurate calculation of interaction energies between ligand and protein also enables screening of millions of drug candidates/docking poses. Using a unique protocol in which docking by GOLD-PLP, rescoring by ANI-ML potentials and extensive MD simulations along with end state free energy methods are combined, we have screened FDA approved drugs against the SARS-CoV-2 main protease (M<sup>pro</sup>). The top six drug molecules suggested by the consensus of these free energy methods have already been in clinical trials or proposed as potential drug molecules in previous theoretical and experimental studies, approving the validity and the power of accuracy in our screening method.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"38 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10965596/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140292429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The AI-driven Drug Design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations 人工智能驱动的药物设计（AIDD）平台：一个交互式多参数优化系统，将分子进化与基于生理学的药代动力学模拟融为一体。

IF 3 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2024-03-19 DOI: 10.1007/s10822-024-00552-6

Jeremy Jones, Robert D. Clark, Michael S. Lawless, David W. Miller, Marvin Waldman

{"title":"The AI-driven Drug Design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations","authors":"Jeremy Jones, Robert D. Clark, Michael S. Lawless, David W. Miller, Marvin Waldman","doi":"10.1007/s10822-024-00552-6","DOIUrl":"10.1007/s10822-024-00552-6","url":null,"abstract":"<div><p>Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have demonstrated the contribution of this field to medicine. Properly designed and implemented platforms can drastically reduce drug development timelines and costs. While such efforts were initially focused primarily on target affinity/activity, it is now appreciated that other parameters are equally important in the successful development of a drug and its progression to the clinic, including pharmacokinetic properties as well as absorption, distribution, metabolic, excretion and toxicological (ADMET) properties. In the last decade, several programs have been developed that incorporate these properties into the drug design and optimization process and to varying degrees, allowing for multi-parameter optimization. Here, we introduce the Artificial Intelligence-driven Drug Design (AIDD) platform, which automates the drug design process by integrating high-throughput physiologically-based pharmacokinetic simulations (powered by GastroPlus) and ADMET predictions (powered by ADMET Predictor) with an advanced evolutionary algorithm that is quite different than current generative models. AIDD uses these and other estimates in iteratively performing multi-objective optimizations to produce novel molecules that are active and lead-like. Here we describe the AIDD workflow and details of the methodologies involved therein. We use a dataset of triazolopyrimidine inhibitors of the dihydroorotate dehydrogenase from <i>Plasmodium falciparum</i> to illustrate how AIDD generates novel sets of molecules.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"38 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140157358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SpaceGrow: efficient shape-based virtual screening of billion-sized combinatorial fragment spaces SpaceGrow：基于形状的亿万级组合片段空间高效虚拟筛选。

IF 3 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2024-03-17 DOI: 10.1007/s10822-024-00551-7

Sophia M. N. Hönig, Florian Flachsenberg, Christiane Ehrt, Alexander Neumann, Robert Schmidt, Christian Lemmen, Matthias Rarey

{"title":"SpaceGrow: efficient shape-based virtual screening of billion-sized combinatorial fragment spaces","authors":"Sophia M. N. Hönig, Florian Flachsenberg, Christiane Ehrt, Alexander Neumann, Robert Schmidt, Christian Lemmen, Matthias Rarey","doi":"10.1007/s10822-024-00551-7","DOIUrl":"10.1007/s10822-024-00551-7","url":null,"abstract":"<p>The growing size of make-on-demand chemical libraries is posing new challenges to cheminformatics. These ultra-large chemical libraries became too large for exhaustive enumeration. Using a combinatorial approach instead, the resource requirement scales approximately with the number of synthons instead of the number of molecules. This gives access to billions or trillions of compounds as so-called chemical spaces with moderate hardware and in a reasonable time frame. While extremely performant ligand-based 2D methods exist in this context, 3D methods still largely rely on exhaustive enumeration and therefore fail to apply. Here, we present SpaceGrow: a novel shape-based 3D approach for ligand-based virtual screening of billions of compounds within hours on a single CPU. Compared to a conventional superposition tool, SpaceGrow shows comparable pose reproduction capacity based on RMSD and superior ranking performance while being orders of magnitude faster. Result assessment of two differently sized subsets of the eXplore space reveals a higher probability of finding superior results in larger spaces highlighting the potential of searching in ultra-large spaces. Furthermore, the application of SpaceGrow in a drug discovery workflow was investigated in four examples involving G protein-coupled receptors (GPCRs) with the aim to identify compounds with similar binding capabilities and molecular novelty.</p><p>SpaceGrow descriptor comparison for an example cut in the molecule of interest. Scoring scheme is implied for one fragment of this cut. </p>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"38 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10944417/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140139700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction: Complex peptide macrocycle optimization: combining NMR restraints with conformational analysis to guide structure-based and ligand-based design 更正：复杂多肽大环优化：将核磁共振约束与构象分析相结合，指导基于结构和配体的设计。

IF 3 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2024-03-13 DOI: 10.1007/s10822-024-00556-2

Ajay N. Jain, Alexander C. Brueckner, Christine Jorge, Ann E. Cleves, Purnima Khandelwal, Janet Caceres Cortes, Luciano Mueller

引用次数: 0