Artificial intelligence chemistry最新文献

{"title":"Advances in machine-learning approaches to RNA-targeted drug design","authors":"Yuanzhe Zhou ,&nbsp;Shi-Jie Chen","doi":"10.1016/j.aichem.2024.100053","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100053","url":null,"abstract":"<div><p>RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI’s potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100053"},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000113/pdfft?md5=300db5aa459794dcdbc0972a40d0ca02&pid=1-s2.0-S2949747724000113-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139714050","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 prediction of state-to-state rate constants for astrochemistry","authors":"Duncan Bossion ,&nbsp;Gunnar Nyman ,&nbsp;Yohann Scribano","doi":"10.1016/j.aichem.2024.100052","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100052","url":null,"abstract":"<div><p>In this work, we investigate the possibility to use an artificial neural network to predict a large number of accurate state-to-state rate constants for atom-diatom collisions, from available rates obtained at two different accuracy levels, using a few accurate rates and many low-accuracy rates. The H + H₂ → H₂ + H chemical reaction is used to benchmark our neural network, as both low and high accuracy state-to-state rates are available in the literature. Our artificial neural network is a multilayer perceptron, using 8 input neurons including the low-accuracy rate constants, with the high accuracy rate constants as the output neuron. The use of machine learning to predict rate constants is very encouraged, as the rates obtained are accurate, even using as low as 1% of the full dataset to train the neural network, and improve greatly the low accuracy rates previously available. This approach can be used to generate full rate constant datasets with a consistent accuracy, from sparse rates obtained with various methods of different accuracies.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100052"},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000101/pdfft?md5=be9d938fa5886a1544bcda53427c4f6f&pid=1-s2.0-S2949747724000101-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139714051","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":"Size dependent lithium-ion conductivity of solid electrolytes in machine learning molecular dynamics simulations","authors":"Yixi Zhang ,&nbsp;Jin-Da Luo ,&nbsp;Hong-Bin Yao ,&nbsp;Bin Jiang","doi":"10.1016/j.aichem.2024.100051","DOIUrl":"10.1016/j.aichem.2024.100051","url":null,"abstract":"<div><p>Solid-state electrolytes are key ingredients in next-generation devices for energy storage and release. Machine learning molecular dynamics (MLMD) has shown great promise in studying the diffusivity of mobile ions in solid-state electrolytes, with much higher efficiency than conventional ab initio molecular dynamics (AIMD). In this work, we combine an efficient embedded atom neural network (EANN) approach and an uncertainty-driven active learning algorithm that optimally selects data points from high-temperature AIMD trajectories to construct ML potentials for solid-state electrolytes and validate this strategy in a benchmark system, Li₃YCl₆, for which several controversy theoretical results exist. Through systematic MLMD simulations, we find that a typically used small supercell in AIMD simulations fails to predict the supersonic transition at a critical temperature, leading to a significant overestimation of the Li⁺ conductivity in Li₃YCl₆ at room temperature. Fortunately, thanks to the scalability of the EANN potential, extended MLMD simulations in a sufficiently large cell does yield a notable change of temperature-dependence in conductivity at ∼420 K and a much lower room-temperature conductivity in excellent with experiment. Interestingly, our results are all based on a semi-local PBE density functional, which was argued unable to predict the superionic transition. We analyze possible reasons of the seemingly inconsistent MLMD results reported in literature with different ML potentials. This work paves the way of simply using high-temperature AIMD data to generate more reliable MLMD results of low-temperature ionic conductivities in solid-state electrolytes.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100051"},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000095/pdfft?md5=ff9758425c151a024cd1c50e2503eb45&pid=1-s2.0-S2949747724000095-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139635555","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 advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry","authors":"Rizvi Syed Aal E Ali ,&nbsp;Jiaolong Meng ,&nbsp;Muhammad Ehtisham Ibraheem Khan ,&nbsp;Xuefeng Jiang","doi":"10.1016/j.aichem.2024.100049","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100049","url":null,"abstract":"<div><p>Artificial intelligence (AI) is driving a revolution in chemistry, reshaping the landscape of molecular design. This review explores AI’s pivotal roles in the field of organic synthesis applications. AI accurately predicts reaction outcomes, controls chemical selectivity, simplifies synthesis planning, accelerates catalyst discovery, and fuels material innovation and so on. It seamlessly integrates data-driven algorithms with chemical intuition to redefine molecular design. As AI chemistry advances, it promises accelerated research, sustainability, and innovative solutions to chemistry’s pressing challenges. The fusion of AI and chemistry is poised to shape the field’s future profoundly, offering new horizons in precision and efficiency. This review encapsulates the transformation of AI in chemistry, marking a pivotal moment where algorithms and data converge to revolutionize the world of molecules.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100049"},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000071/pdfft?md5=ca6a79f1c6ae5ed3980ec0ff3589b022&pid=1-s2.0-S2949747724000071-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139549589","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":"Applying graph neural network models to molecular property prediction using high-quality experimental data","authors":"Chen Qu,&nbsp;Barry I. Schneider,&nbsp;Anthony J. Kearsley,&nbsp;Walid Keyrouz,&nbsp;Thomas C. Allison","doi":"10.1016/j.aichem.2024.100050","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100050","url":null,"abstract":"<div><p>Graph neural networks have been successfully applied to machine learning models related to molecules and crystals, due to the similarity between a molecule/crystal and a graph. In this paper, we present three models that are trained with high-quality experimental data to predict three molecular properties (Kováts retention index, normal boiling point, and mass spectrum), using the same GNN architecture. We show that graph representations of molecules, combined with deep learning methodologies and high-quality data sets, lead to accurate machine learning models to predict molecular properties.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100050"},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000083/pdfft?md5=d755fd2f616c83e07982edec2890d06c&pid=1-s2.0-S2949747724000083-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139549590","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":"Exploring the energy landscape of graphynes for noble gas adsorption using swarm intelligence","authors":"Megha Rajeevan,&nbsp;Rotti Srinivasamurthy Swathi","doi":"10.1016/j.aichem.2024.100048","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100048","url":null,"abstract":"<div><p>Gas adsorption on one-atom-thick membranes is a growing technology for separation applications owing to its excellent energy efficiency. Herein, we investigate the adsorption of the noble gases, Ne, Ar and Kr on graphynes (GYs), a novel class of one-atom-thick carbon membranes using a swarm intelligence technique, namely particle swarm optimization (PSO). Modeling the adsorption of noble gas clusters on two-dimensional substrates requires a thorough examination of the energy landscape. The high dimensionality of the problem makes it tricky to employ ab initio methods for such studies, necessitating the use of a metaheuristic global optimization technique such as PSO. We explored the adsorption of 1–30 atoms of Ne, Ar and Kr on α-, β-, γ- and rhombic-GYs to predict the most suitable form of GY for the adsorption of each of the gases. Employing the dispersion-corrected density functional theory (DFT-D) data for the adsorption of single gas atoms as the reference data, we parametrized two empirical pairwise potentials, namely, Lennard-Jones (LJ) and improved Lennard-Jones (ILJ) potentials. We then analyzed the growth pattern as well as the energetics of adsorption using the parametrized potentials, in combination with the PSO technique, which enabled us to predict the best possible membrane for the adsorption of the noble gases: α-GY for Ne and γ-GY for Ar and Kr. The accuracy of our modeling approach is further validated against DFT-D computations thereby establishing that PSO, when combined with the ILJ potential, can serve as a computationally feasible approach for modeling gas adsorption on GYs.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100048"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294974772400006X/pdfft?md5=13e8fd3ef313b8180bab9c56f7c85352&pid=1-s2.0-S294974772400006X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504245","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 general strategy for improving the performance of PINNs -- Analytical gradients and advanced optimizers in the NeuralSchrödinger framework","authors":"Jakob Gamper ,&nbsp;Hans Georg Gallmetzer ,&nbsp;Alexander K.H. Weiss ,&nbsp;Thomas S. Hofer","doi":"10.1016/j.aichem.2024.100047","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100047","url":null,"abstract":"<div><p>In this work, the previously introduced NeuralSchrödinger PINN is extended towards the use of analytical gradient expressions of the loss function. It is shown that the analytical gradients derived in this work increase the convergence properties for both the BFGS and ADAM optimizers compared to the previously employed numerical gradient implementation. In addition, the use of parallelised GPU computations <em>via</em> CUDA greatly increased the computational performance over the previous implementation using single-core CPU computations. As a consequence, an extension of the NeuralSchrödinger PINN towards two-dimensional quantum systems became feasible as also demonstrated in this work.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100047"},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000058/pdfft?md5=a75b7d5a2a3dee17cd82180444493827&pid=1-s2.0-S2949747724000058-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139434123","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":"Adsorption kinetics of H2O on graphene surface based on a new potential energy surface","authors":"Jun Chen ,&nbsp;Tan Jin ,&nbsp;Zhe-Ning Chen ,&nbsp;Chong Liu ,&nbsp;Wei Zhuang","doi":"10.1016/j.aichem.2024.100046","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100046","url":null,"abstract":"<div><p>The interaction between water and graphene is important for understanding the thermodynamic and kinetic properties of water on hydrophobic surfaces. In this study, we constructed a high-dimensional potential energy surface (PES) for the water-graphene system using the many-body expansion scheme and neural network fitting. By analyzing the landscape of the PES, we found that the water molecule exhibits a weak physisorption behavior with a binding energy of about − 1000 cm⁻¹ and a very low diffusion barrier. Furthermore, extensive molecular dynamics were performed to investigate the adsorption and diffusion dynamics of a single water on a graphene surface at temperatures ranging from 50 to 300 K. Potential-of-mean-forces were computed from the trajectories, providing a comprehensive and accurate description of the water-graphene interaction kinetics.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100046"},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000046/pdfft?md5=3509ee6529315b58646877b33b98b477&pid=1-s2.0-S2949747724000046-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139433815","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":"Synchrotron radiation data-driven artificial intelligence approaches in materials discovery","authors":"Qingmeng Li ,&nbsp;Rongchang Xing ,&nbsp;Linshan Li ,&nbsp;Haodong Yao ,&nbsp;Liyuan Wu ,&nbsp;Lina Zhao","doi":"10.1016/j.aichem.2024.100045","DOIUrl":"10.1016/j.aichem.2024.100045","url":null,"abstract":"<div><p>Synchrotron radiation technology provides high-resolution and high-sensitivity information for many fields such as material science, life science, and energy research. Synchrotron radiation data-driven methods have significantly accelerated the development of materials discovery and analysis. However, synchrotron radiation data is complex and large, requiring artificial intelligence for analysis. Artificial intelligence can efficiently process complex high-dimensional data, automate the analysis process, discover hidden patterns and associations, and build predictive models. This review provides an overview of the application and development of combining synchrotron radiation data-driven methods with artificial intelligence in the field of materials discovery. The application of the method in science is still limited by the problems of large and complex synchrotron radiation data, valuable experimental machine time, and uninterpretable artificial intelligence models. To address these problems, this review correspondingly proposes solutions for synchrotron radiation artificial intelligence data banks, standardized experiment records systems, and interpretable artificial intelligence predictive models.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100045"},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000034/pdfft?md5=ce78a666fc4929f98343bbd4a363d8f9&pid=1-s2.0-S2949747724000034-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139458271","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":"2024-01-05","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}