Artificial intelligence chemistry最新文献

{"title":"An accurate full-dimensional interaction potential energy surface of CO2+N2 incorporating ∆-machine learning approach via permutation invariant polynomial-neural network","authors":"Jia Li,&nbsp;Jun Li","doi":"10.1016/j.aichem.2023.100019","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100019","url":null,"abstract":"<div><p>The interaction between CO₂ and N₂, both as essential components of the Earth’s atmosphere, plays a crucial role in investigating the greenhouse effect. In this work, we sampled 40,930 data points within the full-dimensional configuration space of CO₂ and N₂ and performed calculations at the level of explicitly correlated coupled cluster single, double, and perturbative triple level with the augmented correlation corrected valence triple-ζ basis set (CCSD(T)-F12a/AVTZ). To ensure computational accuracy while reducing computational costs, we employed the recently proposed Δ-machine learning (Δ-ML) method based on Permutation Invariant Polynomial-Neural Network (PIP-NN) for basis set superposition error (BSSE) correction. By leveraging the limited extrapolation capability of NN, efficient sampling was performed within the existing dataset, enabling the construction of the potential energy surface (PES) incorporating BSSE correction with only a small number of data points for BSSE calculations. A total of approximately 1100 data points were selected from the initial dataset to construct a BSSE correction PES. Utilizing this correction PES, BSSE predictions were carried out for all remaining data points, resulting in the successful development of a high-precision full-dimensional PES with BSSE correction for the CO₂ + N₂ system. The PIP-NN based Δ-ML method significantly reduced the required BSSE calculations by approximately 97.2%, resulting in a final PES with a fitting error of merely 0.026 kcal/mol.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100019"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000192/pdfft?md5=4f0503b66010517c20f46da9e39da648&pid=1-s2.0-S2949747723000192-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92061993","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":"Balancing Wigner sampling and geometry interpolation for deep neural networks learning photochemical reactions","authors":"Li Wang,&nbsp;Zhendong Li,&nbsp;Jingbai Li","doi":"10.1016/j.aichem.2023.100018","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100018","url":null,"abstract":"<div><p>Machine learning photodynamics simulations are revolutionary tools to resolve elusive photochemical reaction mechanisms with time-dependent high-fidelity structure information. Besides the recent advances in neural networks (NNs) potentials, it still lacks a general rule for designing training data for learning photochemical reaction mechanisms with Wigner sampling and geometry interpolation. We present an in-depth investigation of the relationship between the accuracy of the multiple layer NNs and the combinations of training data based on the Wigner sampling and geometry interpolation using model photochemical reactions of the [3]-ladderdiene systems. The NNs trained with Wigner sampling data show underfitting, where the NN errors increase with the structural complexity and diversity. The NNs trained with composite Wigner sampling and geometry interpolation data show one magnitude reduced errors, suggesting an essential role of geometry interpolation in facilitating NNs learning the potential energy surfaces. However, increasing the interpolation steps results in overfitting if the Wigner sampled configuration space is narrowed. Correlating the mean absolute errors (MAE) of the NN predicted energies for the sampled and out-of-sample structures shows an optimal combination ratio of 100:10 between the Wigner sampling structures and geometry interpolation steps for 1000 training data, where the MAE of the sampled structures achieve chemical accuracy while the MAE of the out-of-sample structures is minimized. The NNs trained with the optimally combined data can detect the out-of-sample structures in adaptive sampling with a positive correlation between the maximum standard deviation and MAE of the predicted energies. Collectively, our findings suggest a general rule for designing the training data for ML photodynamics.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100018"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000180/pdfft?md5=2cdb8ecc2616508d396111c8c149852d&pid=1-s2.0-S2949747723000180-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92047094","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":"An improved artificial neural network fit of the ab initio potential energy surface points for HeH+ + H2 and its ensuing rigid rotors quantum dynamics","authors":"R. Biswas ,&nbsp;F.A. Gianturco ,&nbsp;K. Giri ,&nbsp;L. González-Sánchez ,&nbsp;U. Lourderaj ,&nbsp;N. Sathyamurthy ,&nbsp;E. Yurtsever","doi":"10.1016/j.aichem.2023.100017","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100017","url":null,"abstract":"<div><p>Artificial neural networks (ANN) have been shown for the last several years to be a versatile tool for fitting <em>ab initio</em> potential energy surfaces. We have demonstrated recently how a 60-neuron ANN could successfully fit a four-dimensional <em>ab initio</em> potential energy surface for the rigid rotor HeH⁺ - rigid rotor H₂ system with a root-mean-squared deviation (RMSD) of 35 cm⁻¹. We show in the present study how a (40, 40) neural network with two hidden layers could achieve a better fit with an RMSD of 5 cm⁻¹. Through a follow-up quantum dynamical study of HeH⁺(<span><math><msub><mrow><mi>j</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>)-H₂(<span><math><msub><mrow><mi>j</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>) collisions, it is shown that the two fits lead to slightly different rotational excitation and de-excitation cross sections but are comparable to each other in terms of magnitude and dependence on the relative translational energy of the collision partners. When averaged over relative translational energy, the two sets of results lead to rate coefficients that are nearly indistinguishable at higher temperatures thus demonstrating the reliability of the ANN method for fitting <em>ab initio</em> potential energy surfaces. On the other hand, we also find that the de-excitation rate coefficients obtained using the two different ANN fits differ significantly from each other at low temperatures. The consequences of these findings are discussed in our conclusions.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100017"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49744254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent vision for the detection of chemistry glassware toward AI robotic chemists","authors":"Xiaogang Cheng ,&nbsp;Shiyuan Zhu ,&nbsp;Zhaocheng Wang ,&nbsp;Chenxin Wang ,&nbsp;Xin Chen ,&nbsp;Qin Zhu ,&nbsp;Linghai Xie","doi":"10.1016/j.aichem.2023.100016","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100016","url":null,"abstract":"<div><p>One of the key steps to make an artificially intelligent (AI) and robotic chemist is the introduction of machine vision for guiding the experiment operation in the AI-redefined laboratory. In order to realize the targets, the prerequisites are to innovate/implement the intelligent vision for the detection of chemistry glassware. Here, we reported a computer vision method based on You only look once (YOLO) with a self-developed Chemical Vessel Identification Dataset (CViG) for the improvement of classification and recognition performance. The training dataset has been collected that includes 4072 images in real-time chemical laboratory. Three models, YOLOv5s, Slim-YOLOv5s and YOLOv7, have been exploited for the recognition of seven types of glassware in the condition of different scenarios (recognition distance, light and dark, stationary and moving). The improved Slim-YOLOv5s exhibited better recognition ability in various scenes, and the recognition accuracy of chemical vessels is improved by 1.51 % compared with YOLOv5s, and the size of the model is reduced from 14.4 MB to 11.0 MB. Slim-YOLOv5s's mAP is similar to YOLOv7's ability with a disadvantage of large volume, suggested that the improved Slim-YOLOv5s clearly has more advantages in terms of embedded requirements. This vision-assisted system capable of classifying chemical containers accurately in the scenarios of real-time chemical experiments will provide a good vision solution in the frontier fields of automated machine chemistry.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100016"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49744252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of potential antiviral lead inhibitors against SARS-CoV-2 main protease: Structure-guided virtual screening, docking, ADME, and MD Simulation based approach","authors":"Goverdhan Lanka ,&nbsp;Revanth Bathula ,&nbsp;Balaram Ghosh ,&nbsp;Sarita Rajender Potlapally","doi":"10.1016/j.aichem.2023.100015","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100015","url":null,"abstract":"<div><p>The novel coronavirus disease (COVID-19) was caused by a new strain of the virus SARS-CoV-2 in December 2019 emerged as deadly pandemic that affected millions of people worldwide. Factors such as lack of effective drugs, vaccine resistance, gene mutations, and cost of repurposed drugs demand new potential inhibitors. The main protease (Mpro) of SARS-CoV-2 has a key role in viral replication and transcription and is considered as drug target for new lead identification. In this present work, structure-based virtual screening, docking, MM/GBSA, AutoDock, ADME, and MD simulations-based optimization was proposed for the identification of new potential inhibitors against Mpro of SARS-CoV-2. The ligand molecules M1, M3, and M6 were identified as potential leads from lead optimization. Induced fit docking was performed for the identification of the best poses of lead molecules. The best docked poses of potential leads M1 and M3 were subject to 100 ns MD simulations for the evaluation of stability and interaction analysis into Mpro active site. The structures of the top two leads M1 and M3 were optimized based on MD simulation conformational changes and isoster scanning, designed as new leads M7 and M8. The MD simulation trajectories RMSD, RMSF, protein-ligand, ligand-protein interaction plots, and ligand torsion profiles were analyzed for stability interpretation. The docked complexes of M7 and M8 of Mpro exhibited equilibrated and converged plots in 100 ns simulation. The lead molecules M1, M3, M7, and M8 were identified as potential SARS-CoV-2 inhibitors for COVID-19 disease. A comparative docking study was carried out using FDA-approved drugs to support the potential binding affinities of newly identified lead inhibitors.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100015"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49744906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of potential antiviral lead inhibitors against SARS-CoV-2 main protease: Structure-guided virtual screening, docking, ADME, and MD Simulation based approach","authors":"G. Lanka, R. Bathula, B. Ghosh, S. R. Potlapally","doi":"10.2139/ssrn.4457340","DOIUrl":"https://doi.org/10.2139/ssrn.4457340","url":null,"abstract":"","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139344913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine-learning-based virtual screening and ligand docking identify potent HIV-1 protease inhibitors","authors":"Andrew K. Gao ,&nbsp;Trevor B. Chen ,&nbsp;Valentina L. Kouznetsova ,&nbsp;Igor F. Tsigelny","doi":"10.1016/j.aichem.2023.100014","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100014","url":null,"abstract":"<div><p>The human immunodeficiency virus type 1 (HIV-1) is a retrovirus that can cause acquired immunodeficiency syndrome (AIDS), severely weakening the immune system. The United Nations estimates that there are 37.7 million people with HIV worldwide. HIV-1 protease (PR) cleaves polyproteins to create the individual proteins that comprise an HIV virion. Inhibiting PR prevents the creation of new virions, rendering PR an attractive antiviral target. In the present study, a machine-learning regression model was constructed to predict pIC₅₀ bioactivity concentrations using data from 2547 experimentally characterized PR inhibitors. The model achieved Pearson correlation coefficient of 0.88, R-squared of 0.78, and a RMSE of 0.717 in pIC₅₀ units on unseen data using 199 high-variance PubChem substructure fingerprints. The SWEETLEAD database of approximately 4300 traditional medicine compounds and drugs from around the world was screened using the model. Fifty molecules were identified as highly potent, with pIC₅₀ of at least 7.301 (IC₅₀ &lt;= 50 nM). Nine of these molecules, such as lopinavir and ritonavir, are known antiviral drugs. The highly potent molecules were ligand-docked to the 3D structure of HIV protease at the active site. Dihydroergotamine mesylate (daechu alkaloids) had a very strong binding affinity of −13.2, outperforming all known antiviral drugs that were tested. It was also predicted by the model to have an IC₅₀ of 9.16 nM, which is considered very low and desirable. Overall, this study demonstrates the use of machine-learning regression models for virtual screening and highlights several drugs with significant promise for repurposing against HIV-1. Future steps include testing dihydroergotamine mesylate and other candidates in vitro.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100014"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49744902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization","authors":"Sergei Manzhos,&nbsp;Manabu Ihara","doi":"10.1016/j.aichem.2023.100013","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100013","url":null,"abstract":"<div><p>Orders-of-coupling representations (representations of multivariate functions with low-dimensional functions that depend on subsets of original coordinates corresponding to different orders of coupling) are useful in many applications, for example, in computational chemistry and other applications, especially where integration is needed. Examples include N-mode approximations and many-body expansions. Such representations can be conveniently built with machine learning methods, and previously, methods building the lower-dimensional terms of such representations with neural networks [e.g. Comput. Phys. Commun. 180 (2009) 2002] and Gaussian process regressions [e.g. Mach. Learn. Sci. Technol. 3 (2022) 01LT02] were proposed. Here, we show that neural network models of orders-of-coupling representations can be easily built by using a recently proposed neural network with optimal neuron activation functions computed with a first-order additive Gaussian process regression [arXiv:2301.05567] and avoiding non-linear parameter optimization. Examples are given of representations of molecular potential energy surfaces.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100013"},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49744706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chemical space navigation by machine learning models for discovering selective MAO-B enzyme inhibitors for Parkinson’s disease","authors":"P. Catherene Tomy,&nbsp;C. Gopi Mohan","doi":"10.1016/j.aichem.2023.100012","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100012","url":null,"abstract":"<div><p>Monoamine Oxidase-B (MAO-B) is a key neuroprotective target that breaks neurotransmitters such as dopamine and releases highly reactive free radicals as the by-product. Its over-expression in the brain observed due to ageing and neurodegenerative diseases contributes to worsening neuronal degeneration. Being the primary enzyme for dopamine metabolism in <em>the substantia nigra</em> of the brain and due to the lack of efficient drug candidates, MAO-B selective, reversible inhibition is hot topic of research in Parkinson’s disease (PD). This study developed machine learning (ML) models that predict the activity of experimentally tested indole and indazole derivatives against MAO-B using linear genetic function approximation (GFA) and two non-linear support vector machine (SVM) and artificial neural network (ANN) techniques. ANN model with an R² of 0.9704 for the training dataset, <span><math><mrow><msup><mrow><mi>q</mi></mrow><mrow><mn>2</mn></mrow></msup><mspace></mspace></mrow></math></span>of 0.9436 for cross-validation and <span><math><mrow><msup><mrow><mi>r</mi></mrow><mrow><mn>2</mn></mrow></msup><mspace></mspace></mrow></math></span>of 0.9025 for the test dataset were identified as the best-performing ML model with the seven significant molecular descriptors CATS2D_04_DA, CATS2D_05_DA, CATS3D_06_LL, Mor04u, Mor25m, P_VSA_v_2 and nO. The robust ML model was then employed to design novel MAO-B inhibitors with similar core scaffolds and their biological activity prediction. ANN model was further employed in the virtual screening of 4356 molecules from the ChEMBL database. Applicability domain analysis and pharmacokinetic and toxicity profiles predicted three newly designed molecules (22 N, 23 N and 24 N) and two virtually screened best ChEMBL molecules as potential drug candidates using the ANN ML model. Molecular docking studies of the best-identified compounds were performed to understand the molecular mechanism of interactions having high binding energy and selectivity with the MAO-B enzyme. The current study shortlisted 5 potential lead compounds as potent and selective MAO-B inhibitors, which could further be carried forward for in vitro and in vivo studies to discover small molecules against neurodegenerative disease.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100012"},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49744704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of artificial intelligence and machine learning in early detection of adverse drug reactions (ADRs) and drug-induced toxicity","authors":"Siyun Yang,&nbsp;Supratik Kar","doi":"10.1016/j.aichem.2023.100011","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100011","url":null,"abstract":"<div><p>Adverse drug reactions (ADRs) and drug-induced toxicity are major challenges in drug discovery, threatening patient safety and dramatically increasing healthcare expenditures. Since ADRs and toxicity are not as visible as infectious diseases, the potential consequences are considerable. Early detection of ADRs and drug-induced toxicity is an essential indicator of a drug's viability and safety profile. The introduction of artificial intelligence (AI) and machine learning (ML) approaches has resulted in a paradigm shift in the field of early ADR and toxicity detection. The application of these modern computational methods allows for the rapid, thorough, and precise prediction of probable ADRs and toxicity even before the drug’s practical synthesis as well as preclinical and clinical trials, resulting in more efficient and safer medications with a lesser chance of drug’s withdrawal. This present review offers an in-depth examination of the role of AI and ML in the early detection of ADRs and toxicity, incorporating a wide range of methodologies ranging from data mining to deep learning followed by a list of important databases, modeling algorithms, and software that could be used in modeling and predicting a series of ADRs and toxicity. This review also provides a complete reference to what has been performed and what might be accomplished in the field of AI and ML-based early identification of ADRs and drug-induced toxicity. By shedding light on the capabilities of these technologies, it highlights their enormous potential for revolutionizing drug discovery and improving patient safety.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100011"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49757852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}