Journal of Computer-Aided Molecular Design最新文献_第4页

A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat 深度神经网络：预测大鼠药代动力学的机理混合模型。

IF 3 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2024-01-31 DOI: 10.1007/s10822-023-00547-9

Florian Führer, Andrea Gruber, Holger Diedam, Andreas H. Göller, Stephan Menz, Sebastian Schneckener

{"title":"A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat","authors":"Florian Führer, Andrea Gruber, Holger Diedam, Andreas H. Göller, Stephan Menz, Sebastian Schneckener","doi":"10.1007/s10822-023-00547-9","DOIUrl":"10.1007/s10822-023-00547-9","url":null,"abstract":"<div><p>An important aspect in the development of small molecules as drugs or agrochemicals is their systemic availability after intravenous and oral administration. The prediction of the systemic availability from the chemical structure of a potential candidate is highly desirable, as it allows to focus the drug or agrochemical development on compounds with a favorable kinetic profile. However, such predictions are challenging as the availability is the result of the complex interplay between molecular properties, biology and physiology and training data is rare. In this work we improve the hybrid model developed earlier (Schneckener in J Chem Inf Model 59:4893–4905, 2019). We reduce the median fold change error for the total oral exposure from 2.85 to 2.35 and for intravenous administration from 1.95 to 1.62. This is achieved by training on a larger data set, improving the neural network architecture as well as the parametrization of mechanistic model. Further, we extend our approach to predict additional endpoints and to handle different covariates, like sex and dosage form. In contrast to a pure machine learning model, our model is able to predict new end points on which it has not been trained. We demonstrate this feature by predicting the exposure over the first 24 h, while the model has only been trained on the total exposure.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139641396","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

Identification of a druggable site on GRP78 at the GRP78-SARS-CoV-2 interface and virtual screening of compounds to disrupt that interface 在 GRP78-SARS-CoV-2 界面确定 GRP78 上的一个可用药位点，并虚拟筛选破坏该界面的化合物。

IF 3 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2024-01-24 DOI: 10.1007/s10822-023-00546-w

Maria Lazou, Jonathan R. Hutton, Arijit Chakravarty, Diane Joseph-McCarthy

{"title":"Identification of a druggable site on GRP78 at the GRP78-SARS-CoV-2 interface and virtual screening of compounds to disrupt that interface","authors":"Maria Lazou, Jonathan R. Hutton, Arijit Chakravarty, Diane Joseph-McCarthy","doi":"10.1007/s10822-023-00546-w","DOIUrl":"10.1007/s10822-023-00546-w","url":null,"abstract":"<div><p>SARS-CoV-2, the virus that causes COVID-19, led to a global health emergency that claimed the lives of millions. Despite the widespread availability of vaccines, the virus continues to exist in the population in an endemic state which allows for the continued emergence of new variants. Most of the current vaccines target the spike glycoprotein interface of SARS-CoV-2, creating a selection pressure favoring viral immune evasion. Antivirals targeting other molecular interactions of SARS-CoV-2 can help slow viral evolution by providing orthogonal selection pressures on the virus. GRP78 is a host auxiliary factor that mediates binding of the SARS-CoV-2 spike protein to human cellular ACE2, the primary pathway of cell infection. As GRP78 forms a ternary complex with SARS-CoV-2 spike protein and ACE2, disrupting the formation of this complex is expected to hinder viral entry into host cells. Here, we developed a model of the GRP78-Spike RBD-ACE2 complex. We then used that model together with hot spot mapping of the GRP78 structure to identify the putative binding site for spike protein on GRP78. Next, we performed structure-based virtual screening of known drug/candidate drug libraries to identify binders to GRP78 that are expected to disrupt spike protein binding to the GRP78, and thereby preventing viral entry to the host cell. A subset of these compounds has previously been shown to have some activity against SARS-CoV-2. The identified hits are starting points for the further development of novel SARS-CoV-2 therapeutics, potentially serving as proof-of-concept for GRP78 as a potential drug target for other viruses.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139540966","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

Molecular dynamics study on micelle-small molecule interactions: developing a strategy for an extensive comparison 胶束-小分子相互作用的分子动力学研究：制定广泛比较的策略

IF 3.5 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2023-12-16 DOI: 10.1007/s10822-023-00541-1

Aleksei Kabedev, Christel A. S. Bergström, Per Larsson

{"title":"Molecular dynamics study on micelle-small molecule interactions: developing a strategy for an extensive comparison","authors":"Aleksei Kabedev, Christel A. S. Bergström, Per Larsson","doi":"10.1007/s10822-023-00541-1","DOIUrl":"10.1007/s10822-023-00541-1","url":null,"abstract":"<div><p>Theoretical predictions of the solubilizing capacity of micelles and vesicles present in intestinal fluid are important for the development of new delivery techniques and bioavailability improvement. A balance between accuracy and computational cost is a key factor for an extensive study of numerous compounds in diverse environments. In this study, we aimed to determine an optimal molecular dynamics (MD) protocol to evaluate small-molecule interactions with micelles composed of bile salts and phospholipids. MD simulations were used to produce free energy profiles for three drug molecules (danazol, probucol, and prednisolone) and one surfactant molecule (sodium caprate) as a function of the distance from the colloid center of mass. To address the challenges associated with such tasks, we compared different simulation setups, including freely assembled colloids versus pre-organized spherical micelles, full free energy profiles versus only a few points of interest, and a coarse-grained model versus an all-atom model. Our findings demonstrate that combining these techniques is advantageous for achieving optimal performance and accuracy when evaluating the solubilization capacity of micelles.</p><h3>Graphical abstract</h3><p>All-atom (AA) and coarse-grained (CG) umbrella sampling (US) simulations and point-wise free energy (FE) calculations were compared to their efficiency to computationally analyze the solubilization of active pharmaceutical ingredients in intestinal fluid colloids.</p>\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":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-023-00541-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138689379","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

QM assisted ML for 19F NMR chemical shift prediction 用于 19F NMR 化学位移预测的 QM 辅助 ML

IF 3.5 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2023-12-12 DOI: 10.1007/s10822-023-00542-0

Patrick Penner, Anna Vulpetti

{"title":"QM assisted ML for 19F NMR chemical shift prediction","authors":"Patrick Penner, Anna Vulpetti","doi":"10.1007/s10822-023-00542-0","DOIUrl":"10.1007/s10822-023-00542-0","url":null,"abstract":"<div><div><h3>Background</h3><p>Ligand-observed 19F NMR detection is an efficient method for screening libraries of fluorinated molecules in fragment-based drug design campaigns. Screening fluorinated molecules in large mixtures makes 19F NMR a high-throughput method. Typically, these mixtures are generated from pools of well-characterized fragments. By predicting 19F NMR chemical shift, mixtures could be generated for arbitrary fluorinated molecules facilitating for example focused screens.</p><h3>Methods</h3><p>In a previous publication, we introduced a method to predict 19F NMR chemical shift using rooted fluorine fingerprints and machine learning (ML) methods. Having observed that the quality of the prediction depends on similarity to the training set, we here propose to assist the prediction with quantum mechanics (QM) based methods in cases where compounds are not well covered by a training set.</p><h3>Results</h3><p>Beyond similarity, the performance of ML methods could be associated with individual features in compounds. A combination of both could be used as a procedure to split input data sets into those that could be predicted by ML and those that required QM processing. We could show on a proprietary fluorinated fragment library, known as LEF (Local Environment of Fluorine), and a public Enamine data set of 19F NMR chemical shifts that ML and QM methods could synergize to outperform either method individually. Models built on Enamine data, as well as model building and QM workflow tools, can be found at https://github.com/PatrickPenner/lefshift and https://github.com/PatrickPenner/lefqm.</p></div></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138570832","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

Open-ComBind: harnessing unlabeled data for improved binding pose prediction Open-ComBind：利用无标记数据改进结合姿态预测

IF 3.5 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2023-12-08 DOI: 10.1007/s10822-023-00544-y

Andrew T. McNutt, David Ryan Koes

{"title":"Open-ComBind: harnessing unlabeled data for improved binding pose prediction","authors":"Andrew T. McNutt, David Ryan Koes","doi":"10.1007/s10822-023-00544-y","DOIUrl":"10.1007/s10822-023-00544-y","url":null,"abstract":"<div><p>Determination of the bound pose of a ligand is a critical first step in many in silico drug discovery tasks. Molecular docking is the main tool for the prediction of non-covalent binding of a protein and ligand system. Molecular docking pipelines often only utilize the information of one ligand binding to the protein despite the commonly held hypothesis that different ligands share binding interactions when bound to the same receptor. Here we describe Open-ComBind, an easy-to-use, open-source version of the ComBind molecular docking pipeline that leverages information from multiple ligands without known bound structures to enhance pose selection. We first create distributions of feature similarities between ligand pose pairs, comparing near-native poses with all sampled docked poses. These distributions capture the likelihood of observing similar features, such as hydrogen bonds or hydrophobic contacts, in different pose configurations. These similarity distributions are then combined with a per-ligand docking score to enhance overall pose selection by 5% and 4.5% for high-affinity and congeneric series helper ligands, respectively. Open-ComBind reduces the average RMSD of ligands in our benchmark dataset by 9.0%. We provide Open-ComBind as an easy-to-use command line and Python API to increase pose prediction performance at www.github.com/drewnutt/open_combind.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-023-00544-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138552568","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: Exploring DrugCentral: from molecular structures to clinical effects 更正:探索DrugCentral:从分子结构到临床效果。

IF 3.5 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2023-12-02 DOI: 10.1007/s10822-023-00545-x

Liliana Halip, Sorin Avram, Ramona Curpan, Ana Borota, Alina Bora, Cristian Bologa, Tudor I. Oprea

引用次数: 0

Binding of small molecule inhibitors to RNA polymerase-Spt5 complex impacts RNA and DNA stability 小分子抑制剂与RNA聚合酶- spt5复合物的结合影响RNA和DNA的稳定性。

IF 3.5 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2023-11-21 DOI: 10.1007/s10822-023-00543-z

Adan Gallardo, Bercem Dutagaci

{"title":"Binding of small molecule inhibitors to RNA polymerase-Spt5 complex impacts RNA and DNA stability","authors":"Adan Gallardo, Bercem Dutagaci","doi":"10.1007/s10822-023-00543-z","DOIUrl":"10.1007/s10822-023-00543-z","url":null,"abstract":"<div><p>Spt5 is an elongation factor that associates with RNA polymerase II (Pol II) during transcription and has important functions in promoter-proximal pausing and elongation processivity. Spt5 was also recognized for its roles in the transcription of expanded-repeat genes that are related to neurodegenerative diseases. Recently, a set of Spt5-Pol II small molecule inhibitors (SPIs) were reported, which selectively inhibit mutant huntingtin gene transcription. Inhibition mechanisms as well as interaction sites of these SPIs with Pol II and Spt5 are not entirely known. In this study, we predicted the binding sites of three selected SPIs at the Pol II-Spt5 interface by docking and molecular dynamics simulations. Two molecules out of three demonstrated strong binding with Spt5 and Pol II, while the other molecule was more loosely bound and sampled multiple binding sites. Strongly bound SPIs indirectly affected RNA and DNA dynamics at the exit site as DNA became more flexible while RNA was stabilized by increased interactions with Spt5. Our results suggest that the transcription inhibition mechanism induced by SPIs can be related to Spt5-nucleic acid interactions, which were altered to some extent with strong binding of SPIs.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10663202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138175193","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

Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state 通过应用人工液态的机器学习模型作为固态的代理来预测水的绝对溶解度。

IF 3.5 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2023-10-25 DOI: 10.1007/s10822-023-00538-w

Sadra Kashef Ol Gheta, Anne Bonin, Thomas Gerlach, Andreas H. Göller

{"title":"Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state","authors":"Sadra Kashef Ol Gheta, Anne Bonin, Thomas Gerlach, Andreas H. Göller","doi":"10.1007/s10822-023-00538-w","DOIUrl":"10.1007/s10822-023-00538-w","url":null,"abstract":"<div><p>In this study, we use machine learning algorithms with QM-derived COSMO-RS descriptors, along with Morgan fingerprints, to predict the absolute solubility of drug-like compounds. The QM-derived descriptors account for the molecular properties of the solute, i.e., the solute–solute interactions in an artificial-liquid-state (super-cooled liquid), and the solute–solvent interactions in solution. We employ two main approaches to predict solubility: (i) a hypothetical pathway that involves melting the solute at room temperature T = T¯ (<span>({Delta }_{fus}{G}_{A}^{ominus })</span>) and mixing the artificially liquid solute into the solvent (<span>({Delta }_{m}{G}_{left(A:Bright)}^{ominus })</span>). In this approach <span>({Delta }_{fus}{G}_{A}^{ominus })</span> is predicted using machine learning models, and the <span>({Delta }_{m}{G}_{left(A:Bright)}^{ominus })</span> is obtained from COSMO-RS calculations; (ii) direct solubility prediction using machine learning algorithms. The models were trained on a large number of Bayer in-house compounds for which water solubility data is available at physiological pH of 6.5 and ambient temperature. We also evaluated our models using external datasets from a solubility challenge. Our models present great improvements compared to the absolute solubility prediction with the QSAR model for the artificial liquid state as implemented in the COSMO<i>therm</i> software, for both in-house and external datasets. We are furthermore able to demonstrate the superiority of QM-derived descriptors compared to cheminformatics descriptors. We finally present low-cost alternative models using fragment-based COSMO<i>quick</i> calculations with only marginal reduction in the quality of predicted solubility.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50156780","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

Correction to: Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study 更正：通过整合分子动力学模拟、药效团建模和机器学习，发现浅结合位点的小分子结合物的计算工作流程：STAT3作为案例研究。

IF 3.5 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2023-10-19 DOI: 10.1007/s10822-023-00540-2

Nour Jamal Jaradat, Mamon Hatmal, Dana Alqudah, Mutasem Omar Taha

引用次数: 0

Artificial intelligence for prediction of biological activities and generation of molecular hits using stereochemical information 利用立体化学信息预测生物活性和生成分子命中率的人工智能。

IF 3.5 3区生物学

Journal of Computer-Aided Molecular Design Pub Date : 2023-10-17 DOI: 10.1007/s10822-023-00539-9

Tiago O. Pereira, Maryam Abbasi, Rita I. Oliveira, Romina A. Guedes, Jorge A. R. Salvador, Joel P. Arrais

{"title":"Artificial intelligence for prediction of biological activities and generation of molecular hits using stereochemical information","authors":"Tiago O. Pereira, Maryam Abbasi, Rita I. Oliveira, Romina A. Guedes, Jorge A. R. Salvador, Joel P. Arrais","doi":"10.1007/s10822-023-00539-9","DOIUrl":"10.1007/s10822-023-00539-9","url":null,"abstract":"<div><p>In this work, we develop a method for generating targeted hit compounds by applying deep reinforcement learning and attention mechanisms to predict binding affinity against a biological target while considering stereochemical information. The novelty of this work is a deep model Predictor that can establish the relationship between chemical structures and their corresponding <span>(pIC_{50})</span> values. We thoroughly study the effect of different molecular descriptors such as ECFP4, ECFP6, SMILES and RDKFingerprint. Also, we demonstrated the importance of attention mechanisms to capture long-range dependencies in molecular sequences. Due to the importance of stereochemical information for the binding mechanism, this information was employed both in the prediction and generation processes. To identify the most promising hits, we apply the self-adaptive multi-objective optimization strategy. Moreover, to ensure the existence of stereochemical information, we consider all the possible enumerated stereoisomers to provide the most appropriate 3D structures. We evaluated this approach against the Ubiquitin-Specific Protease 7 (USP7) by generating putative inhibitors for this target. The predictor with SMILES notations as descriptor plus bidirectional recurrent neural network using attention mechanism has the best performance. Additionally, our methodology identify the regions of the generated molecules that are important for the interaction with the receptor’s active site. Also, the obtained results demonstrate that it is possible to discover synthesizable molecules with high biological affinity for the target, containing the indication of their optimal stereochemical conformation.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41231479","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