Artificial intelligence chemistry最新文献

{"title":"Building a DFT+U machine learning interatomic potential for uranium dioxide","authors":"Elizabeth Stippell ,&nbsp;Lorena Alzate-Vargas ,&nbsp;Kashi N. Subedi ,&nbsp;Roxanne M. Tutchton ,&nbsp;Michael W.D. Cooper ,&nbsp;Sergei Tretiak ,&nbsp;Tammie Gibson ,&nbsp;Richard A. Messerly","doi":"10.1016/j.aichem.2023.100042","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100042","url":null,"abstract":"<div><p>Despite uranium dioxide (UO₂) being a widely used nuclear fuel, fuel performance models rely extensively on empirical correlations of material behavior, leveraging the historical operating experience of UO₂. Mechanistic models that consider an atomistic understanding of the processes governing fuel performance (such as fission gas release and creep) will enable a better description of fuel behavior under non-prototypical conditions such as in new reactor concepts or for modified UO₂ fuel compositions. To this end, molecular dynamics simulation is a powerful tool for rapidly predicting physical properties of proposed fuel candidates. However, the reliability of these simulations depends largely on the accuracy of the atomic forces. Traditionally, these forces are computed using either a classical force field (FF) or density functional theory (DFT). While DFT is relatively accurate, the computational cost is burdensome, especially for <em>f</em>-electron elements, such as actinides. By contrast, classical FFs are computationally efficient but are less accurate. For these reasons, we report a new accurate machine learning interatomic potential (MLIP) for UO₂ that provides high-fidelity reproduction of DFT forces at a similar low cost to classical FFs. We employ an active learning approach that autonomously augments the DFT training data set to iteratively refine the MLIP. To further improve the quality of our predictions, we utilize transfer learning to retrain our MLIP to higher-accuracy DFT+U data. We validate our MLIPs by comparing predicted physical properties (e.g., thermal expansion and elastic properties) with those from existing classical FFs and DFT/DFT+U calculations, as well as with experimental data when available.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100042"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000428/pdfft?md5=b4a181e648f961d53d4c25f1bedd0f01&pid=1-s2.0-S2949747723000428-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139100192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploration of SAM-I riboswitch inhibitors: In-Silico discovery of ligands to a new target employing multistage CADD approaches","authors":"Nada Elkholy ,&nbsp;Reham Hassan ,&nbsp;Loay Bedda ,&nbsp;Mohamed A. Elrefaiy ,&nbsp;Reem K. Arafa","doi":"10.1016/j.aichem.2024.100044","DOIUrl":"10.1016/j.aichem.2024.100044","url":null,"abstract":"<div><p>Targeting riboswitches, regulatory elements responsible for the expression of essential genes, is taking central stage in the new era of antibacterial medications discovery due to the emergence of antibiotic resistance. The S-Adenosyl methionine-I (SAM-I) riboswitch works through transcription termination in a negative feedback manner modulated by the natural ligand SAM. SAM-I riboswitch is specific to bacteria and found mainly in gram-positive bacteria such as <em>Bacillus anthracis</em>. Analyzing the interactions of the co-crystallized structure of SAM-I riboswitch aptamer with its native ligand SAM clarified the needed chemical structural features to achieve binding. Acknowledging those features, structure-based and ligand-based pharmacophore models were built for filtration use in screening the OTAVA Chemical library and the Pubchem database. For further filtration enhancement, the physicochemical properties of SAM were used as a second filtration criterion. Compounds obtained as output from previous steps were energy minimized, and the lowest energy conformer structures were docked to SAM-I using MOE, v.2019.01. S-score and ligand interactions were used to assess the best hits. This yielded eight promising compounds to which molecular dynamics (MD) simulations with SAM-I aptamer were applied using GROMACS 2020.3 package affirming stable binding interactions and binding energetics similar to SAM. Moreover, pharmacokinetic and drug-like properties of those eight hits were assessed using SWISS-ADME. According to the combined computational methods and PK/Tox assessment, compound <strong>20</strong> was the most promising and thus can be considered a lead for future evaluation and optimization as a candidate new antibacterial agent targeting a new biomolecule eliciting a new mechanism of action.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100044"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000022/pdfft?md5=8917be98aed350d86d15c16d637e45f1&pid=1-s2.0-S2949747724000022-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139392339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated learning data-driven potential models for spectroscopic characterization of astrophysical interest noble gas-containing NgH2+ molecules","authors":"María Judit Montes de Oca-Estévez ,&nbsp;Rita Prosmiti","doi":"10.1016/j.aichem.2024.100059","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100059","url":null,"abstract":"<div><p>The choice of a proper machine learning (ML) algorithm for constructing potential energy surface (PES) models has become a crucial tool in the fields of quantum chemistry and computational modeling. These algorithms offer the ability to make reliable and accurate predictions at a reasonable computational cost, and thus they can be then used in various molecular dynamics and spectroscopic studies. For that, it is not surprising that much of the current research focuses on the development of software that generates machine learning models using precalculated <em>ab initio</em> data points. This study is primarily dedicated to the application and assessment of various automated learning models. These models are trained and tested using datasets derived from CCSD(T)/CBS[56] calculations, aiming to represent intermolecular interactions in small molecules, such as the NgH<span><math><msubsup><mrow></mrow><mrow><mn>2</mn></mrow><mrow><mo>+</mo></mrow></msubsup></math></span> complexes, where Ng represents helium (He), neon (Ne), and argon (Ar) atoms. These noble gas-containing molecules have gained increasing significance in the field of molecular astrochemistry, due to the recent discovery of HeH⁺ and ArH⁺ molecular cations in the interstellar medium (ISM), thereby opening up a wide range of possibilities in this scientific area. Consequently, the ML-generated PESs are employed to compute vibrational bound states for these molecular cations, with the goal of characterizing all their known isotopologues. Furthermore, the results are compared with spectroscopic data, when available, from previous studies in the literature. Our findings have the potential to provide valuable guidance for future ML-PES development and benchmarking studies involving noble gas-containing cations of astrophysical importance.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100059"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000174/pdfft?md5=b79fa9d5a2a5cca40b8a1c916bfa7bf5&pid=1-s2.0-S2949747724000174-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent advances of machine learning applications in the development of experimental homogeneous catalysis","authors":"Nil Sanosa ,&nbsp;David Dalmau ,&nbsp;Diego Sampedro ,&nbsp;Juan V. Alegre-Requena ,&nbsp;Ignacio Funes-Ardoiz","doi":"10.1016/j.aichem.2024.100068","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100068","url":null,"abstract":"<div><p>Machine Learning (ML) stands as a disruptive technology, finding application across a diverse array of scientific disciplines. When applied to homogeneous catalysis, this technology accelerates catalyst discovery through virtual screening, which not only reduces experimental iterations but also yields significant savings in time, resources, and waste generation. ML algorithms, often integrated with cheminformatic tools and quantum mechanics featurization, excel in predicting reaction outcomes that guide the engineering of catalysts for desired reactivity and selectivity. This minireview presents recent studies regarding databases as well as supervised and unsupervised problems, offering a general yet insightful perspective on the current ML-driven progress in homogeneous catalysis.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100068"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000265/pdfft?md5=2dd0fc25216808ebfca4936d94919c60&pid=1-s2.0-S2949747724000265-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140825120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RedPred, a machine learning model for the prediction of redox reaction energies of the aqueous organic electrolytes","authors":"Murat Cihan Sorkun ,&nbsp;Elham Nour Ghassemi ,&nbsp;Cihan Yatbaz ,&nbsp;J.M. Vianney A. Koelman ,&nbsp;Süleyman Er","doi":"10.1016/j.aichem.2024.100064","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100064","url":null,"abstract":"<div><p>Aqueous Organic Redox Flow Batteries (AORFBs) are considered as one of the most appealing technologies for large-scale energy storage due to their electroactive organic materials, which are abundant, easy to produce, and recyclable. A prevailing challenge for the redox chemistries applied in AORFBs is to achieve high power and energy density. The chemical design and molecular engineering of the electroactive compounds is an effective approach for the optimization of their physicochemical properties. Among them, the reaction energy of redox couples is often used as a proxy for the measured potentials. In this study, we present RedPred, a machine learning (ML) model that predicts the one-step two-electron two-proton redox reaction energy of redox-active molecule pairs. RedPred comprises an ensemble of Artificial Neural Networks, Random Forests, and Graph Convolutional Networks, trained using the RedDB database, which contains over 15,000 reactant-product pairs for AORFBs. We evaluated RedPred’s performance using six different molecular encoders and five prominent ML algorithms applied in chemical science. The predictive capability of RedPred was tested on both its training chemical space and the chemical space outside its training domain using two separate test datasets. We released a user-friendly web tool with open-source code to promote software sustainability and broad use.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100064"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000228/pdfft?md5=eb00d9969ca643436121b54e6f5cf72b&pid=1-s2.0-S2949747724000228-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140539637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated kinetics measurement for homogeneous photocatalytic reactions in continuous microflow","authors":"Yujie Wang ,&nbsp;Jian Li ,&nbsp;Xuze Chen ,&nbsp;Weiping Zhu ,&nbsp;Xuhong Guo ,&nbsp;Fang Zhao","doi":"10.1016/j.aichem.2024.100066","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100066","url":null,"abstract":"<div><p>Photocatalytic reactions, achieving chemical synthesis in a more sustainable manner than thermal reactions, have been demonstrated to become more efficient, greener and easier to scale up when combined with continuous microflow technology. Nevertheless, the report on the kinetics measurement for photocatalytic reactions in continuous microflow, especially in a fully automated way, is very rare. In this work, two challenging parameters, i.e., the reaction order with respect to oxygen (2.48) and photoreaction activation energy (-16.83 kJ/mol) of the photocatalytic oxidation of 9,10-diphenylanthracene, were acquired in an automated continuous flow platform using the Steady-state Method. Moreover, the Ramping Method was also successfully implemented in the automated continuous flow photoreaction platform, exhibiting a predictive accuracy of 4.42 %, with 64.3 % less time and 58.0 % less material consumption than the Steady-state Method. And it was found that the improvement in the residence time distribution of the microreactor could improve the accuracy of the Ramping Method. The automated continuous flow process developed in this work could offer an efficient and accurate way to attain the reaction kinetics information for homogeneous photocatalytic reactions.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100066"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000241/pdfft?md5=acff6c610e496d29876c4bc9832b2989&pid=1-s2.0-S2949747724000241-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140644835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of dimensionality reduction techniques for the visualisation of chemical space in organometallic catalysis","authors":"Mario Villares ,&nbsp;Carla M. Saunders ,&nbsp;Natalie Fey","doi":"10.1016/j.aichem.2024.100055","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100055","url":null,"abstract":"<div><p>We have used a Ligand Knowledge Base for bidentate P,P-donor ligands of potential interest to homogeneous catalysis to compare three dimensionality reduction techniques, namely Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP) and t-distributed Stochastic Neighbor Embedding (t-SNE). While our previous work on Ligand Knowledge Bases has focused on PCA, here we compare this approach with more recently-published approaches and assess the information retention, visualization, clustering and interpretability which can be achieved for each approach. We find that potential advantages of t-SNE are not realized with a database of the current size (275 entries), and that there is a degree of complementarity between PCA and UMAP. The statistics underlying PCA rely on linear relationships, making interpretation of the resulting plots comparatively straightforward. Since much of chemistry relies on linear structure-property relationships and low-dimensional visualization, the explainability and information retention achieved is attractive. UMAP proved more challenging to interpret, but achieved clear clustering which was often chemically meaningful, and it would be a useful approach for ensuring that distinct subsets of compounds are sampled in a machine-learning context. This analysis also highlighted that the tunability of catalysis achieved through ligand exchange maps well onto some areas of chemical space where closely related ligands cluster, while others represent outliers; these arise from different combinations of steric and electronic effects which chemists will find intuitive.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100055"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000137/pdfft?md5=d22dd66b98e698544ad12f66b7d830c4&pid=1-s2.0-S2949747724000137-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139943030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning assisted analysis and prediction of rubber formulation using existing databases","authors":"Wei Deng ,&nbsp;Yuehua Zhao ,&nbsp;Yafang Zheng ,&nbsp;Yuan Yin ,&nbsp;Yan Huan ,&nbsp;Lijun Liu ,&nbsp;Dapeng Wang","doi":"10.1016/j.aichem.2024.100054","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100054","url":null,"abstract":"<div><p>Designing rubber formulations can greatly benefit from using a database that stores the formulations and corresponding property data of rubber composites. Such a database can expedite the decision-making process by swiftly identifying the most suitable formulations for specific applications. However, the management of a rubber formulation database encounters various issues, including missing formulation and property data, as well as data entry errors. These issues can impede the decision-making processes and even result in incorrect decisions being made. In this study, machine learning (ML) algorithms were applied to analyze rubber formulation databases. Our findings highlight the success of the ML algorithm in effectively filling in missing data and identifying erroneous data. Furthermore, it demonstrates the accurate prediction of properties for untested formulations within the pre-determined database space. The results underline the outstanding performance of ML algorithms in expediting the rubber formulation design process and emphasize their immense potential to play a prominent role in the advancement of rubber composites.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100054"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000125/pdfft?md5=c058446a90f81b469ca59bff1d08c2a1&pid=1-s2.0-S2949747724000125-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139749640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning-based high-precision density functional method for drug-like molecules","authors":"Jin Xiao ,&nbsp;YiXiao Chen ,&nbsp;LinFeng Zhang ,&nbsp;Han Wang ,&nbsp;Tong Zhu","doi":"10.1016/j.aichem.2023.100037","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100037","url":null,"abstract":"<div><p>In computer-aided drug discovery, accurately determining the structure and properties of drug-like molecules is of utmost importance. This necessitates the use of precise and efficient electronic structure methods. Here, we developed two deep learning-based density functional methods, namely DeePHF and DeePKS, specifically tailored for drug-like molecules. Notably, DeePKS incorporates self-consistency into its framework. With a limited dataset labelled at the CCSD(T)/def2-TZVP level, both models have been able to achieve chemical accuracy in calculating molecular energies and have demonstrated excellent transferability. We anticipate that further advancements in this field will lead to the development of high-quality density functional methods designed specifically for drug discovery purposes. This research showcases the capabilities of deep learning approaches in simplifying the construction complexity associated with traditional DFT methods.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100037"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000374/pdfft?md5=75400cd611ac51291405e572faae390a&pid=1-s2.0-S2949747723000374-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138769494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning approach for predicting the reactivity power of hypervalent iodine compounds","authors":"Vaneet Saini ,&nbsp;Ramesh Kataria,&nbsp;Shruti Rajput","doi":"10.1016/j.aichem.2023.100032","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100032","url":null,"abstract":"<div><p>The knowledge of chemical reactivity of substrates is a prerequisite to accurately design a chemical reaction; however, it has been a challenging task due to the slow trial-and-error experimental approaches and the high computational cost associated with in silico investigations. Artificial intelligence techniques could serve as an alternative to efficiently determine the relative reactivity of chemical entities. In the context of this research, we propose an artificial neural network model to predict the bond dissociation energies of hypervalent iodine reagents. An open-source cheminformatics package, namely, Mordred, was employed for calculating various 1D, 2D and topological descriptors. The approach utilizes a dataset of more than 1000 hypervalent iodine reagents, and the bond dissociation energies can be predicted with a remarkable accuracy, as suggested by an R² score of 0.97 and a mean absolute error of 1.96 kcal/mol. Owing to the low cost and high efficiency, this machine learning approach can provide an alternative to the theoretical/experimental approaches to rationally design a chemical reaction and without having to go through the hassle of high-throughput experimentation to reach the desired reaction outcome. In an effort to make the model interpretable, a feature importance algorithm was applied, which identified descriptors contributing most to the development of the model. Features describing electronegativity and polarizability are some of the important contributors to the model’s training.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100032"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000325/pdfft?md5=a1dd6d2ca6039f146d3c2a643cbb05b8&pid=1-s2.0-S2949747723000325-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138489574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}