Artificial intelligence chemistry最新文献

{"title":"Combining state-of-the-art quantum chemistry and machine learning make gold standard potential energy surfaces accessible for medium-sized molecules","authors":"Apurba Nandi ,&nbsp;Péter R. Nagy","doi":"10.1016/j.aichem.2023.100036","DOIUrl":"10.1016/j.aichem.2023.100036","url":null,"abstract":"<div><p>Developing full-dimensional machine-learned potentials with the current “gold-standard” coupled-cluster (CC) level is challenging for medium-sized molecules due to the high computational cost. Consequently, researchers are often bound to use lower-level electronic structure methods such as density functional theory or second-order Møller–Plesset perturbation theory (MP2). Here, we demonstrate on a representative example that gold-standard potentials can now be effectively constructed for molecules of 15 atoms using off-the-shelf hardware. This is achieved by accelerating the CCSD(T) computations via the accurate and cost-effective frozen natural orbital (FNO) approach. The Δ-machine learning (Δ-ML) approach is employed with the use of permutationally invariant polynomials to fit a full-dimensional potential energy surface of the acetylacetone molecule, but any other effective descriptor and ML approach can similarly benefit from the accelerated data generation proposed here. Our benchmarks for the global minima, H-transfer TS, and many high-lying configurations show the excellent agreement of FNO-CCSD(T) results with conventional CCSD(T) while achieving a significant time advantage of about a factor of 30–40. The obtained Δ-ML PES shows high fidelity from multiple perspectives including energetic, structural, and vibrational properties. We obtain the symmetric double well H-transfer barrier of 3.15 kcal/mol in excellent agreement with the direct FNO-CCSD(T) barrier of 3.11 kcal/mol as well as with the benchmark CCSD(F12*)(T+)/CBS value of 3.21 kcal/mol. Furthermore, the tunneling splitting due to H-atom transfer is calculated using a 1D double-well potential, providing improved estimates over previous ones obtained using an MP2-based PES. The methodology introduced here represents a significant advancement in the efficient and precise construction of potentials at the CCSD(T) level for molecules above the current limit of 15 atoms.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100036"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000362/pdfft?md5=c6666f5fcbc3a2bf27c6aae23a604aaf&pid=1-s2.0-S2949747723000362-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138991782","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":"Effects of aleatoric and epistemic errors in reference data on the learnability and quality of NN-based potential energy surfaces","authors":"Sugata Goswami ,&nbsp;Silvan Käser ,&nbsp;Raymond J. Bemish ,&nbsp;Markus Meuwly","doi":"10.1016/j.aichem.2023.100033","DOIUrl":"10.1016/j.aichem.2023.100033","url":null,"abstract":"<div><p>The effect of noise in the input data for learning potential energy surfaces (PESs) based on neural networks for chemical applications is assessed. Noise in energies and forces can result from aleatoric and epistemic errors in the quantum chemical reference calculations. Statistical (aleatoric) noise arises for example due to the need to set convergence thresholds in the self consistent field (SCF) iterations whereas systematic (epistemic) noise is due to, <em>i</em>nter alia, particular choices of basis sets in the calculations. The two molecules considered here as proxies are H₂CO and HONO which are examples for single- and multi-reference problems, respectively, for geometries around the minimum energy structure. For H₂CO it is found that adding noise to energies and forces with magnitudes representative of single-point calculations does not deteriorate the quality of the final PESs whereas increasing the noise level commensurate with electronic structure calculations for more complicated, e.g. metal-containing, systems is expected to have a more notable effect. On the other hand, for HONO which requires a multi-reference treatment, a clear correlation between model quality and the degree of multi-reference character as measured by the <em>T</em>₁ amplitude is found. It is concluded that for chemically “simple” cases the effect of aleatoric and epistemic errors is manageable without evident deterioration of the trained model, but more care needs to be exercised for situations in which multi-reference effects are present.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100033"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000337/pdfft?md5=391098ccf3759b129948054b61d9af08&pid=1-s2.0-S2949747723000337-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138611496","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":"Drug discovery and development in the era of artificial intelligence: From machine learning to large language models","authors":"Shenghui Guan ,&nbsp;Guanyu Wang","doi":"10.1016/j.aichem.2024.100070","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100070","url":null,"abstract":"<div><p>Drug Research and Development (R&amp;D) is a complex and difficult process, and current drug R&amp;D faces the challenges of long time span, high investment, and high failure rate. Machine learning, with its powerful learning ability to characterize big data and complex networks, is increasingly effective to improve the efficiency and success rate of drug R&amp;D. Here we review some recent examples of the application of machine learning methods in six areas: disease gene prediction, virtual screening, drug molecule generation, molecular attribute prediction, and prediction of drug combination synergism. We also discuss the advantages of integrative learning in multi-attribute prediction. Integrative models based on base learners constructed from data of different dimensions on the one hand fully utilize the information contained in these data, and on the other hand improve the average prediction performance. Finally, we envision a new paradigm for drug discovery and development: a large language model acts as a central hub to organize public resources into a knowledge base, validating the knowledge with computational software and smaller predictive models, as well as high-throughput automated screening platforms based on organoidal technologies, to speed up development and reduce the differences in efficacy between disease models and humans to improve the success rate of a drug.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100070"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000289/pdfft?md5=3f360afc13a60f15f7dab8b6a1dd740b&pid=1-s2.0-S2949747724000289-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140906077","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":"Data mining of stable, low-cost metal oxides as potential electrocatalysts","authors":"Xue Jia,&nbsp;Hao Li","doi":"10.1016/j.aichem.2024.100065","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100065","url":null,"abstract":"<div><p>Metal oxides (MOs) are a class of electrocatalysts which could be the low-cost alternatives to precious metals. However, many MOs suffer from poor stability under electrochemical operating conditions. The <em>Materials Project</em> stands out as one of the largest computational materials databases to date, where the bulk Pourbaix diagrams are essential in assessing the aqueous stability of potential electrocatalysts. Herein, we performed data mining from the <em>Materials Project</em> database to identify potentially stable MOs for industrially important electrocatalytic reactions including oxygen reduction reaction (ORR), oxygen evolution reaction (OER), chlorine evolution reaction (CER), hydrogen evolution reaction (HER), and nitrogen reduction reaction (NRR). We found that many MOs can be potentially stable under electrocatalytic conditions, especially in neutral and alkaline medium. Finally, we summarized those MOs that had been previously experimentally synthesized but haven’t been explored as electrocatalysts. This comprehensive assessment effectively narrows down the exploration scope and facilitates the evaluation of material stability.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100065"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294974772400023X/pdfft?md5=b8129e021daa42ae50521062f842ed41&pid=1-s2.0-S294974772400023X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140645362","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 models to predict ligand binding affinity for the orexin 1 receptor","authors":"Vanessa Y. Zhang ,&nbsp;Shayna L. O’Connor ,&nbsp;William J. Welsh ,&nbsp;Morgan H. James","doi":"10.1016/j.aichem.2023.100040","DOIUrl":"10.1016/j.aichem.2023.100040","url":null,"abstract":"<div><p>The orexin 1 receptor (OX1R) is a G-protein coupled receptor that regulates a variety of physiological processes through interactions with the neuropeptides orexin A and B. Selective OX1R antagonists exhibit therapeutic effects in preclinical models of several behavioral disorders, including drug seeking and overeating. However, currently there are no selective OX1R antagonists approved for clinical use, fueling demand for novel compounds that act at this target. In this study, we meticulously curated a dataset comprising over 1300 OX1R ligands using a stringent filter and criteria cascade. Subsequently, we developed highly predictive quantitative structure-activity relationship (QSAR) models employing the optimized hyper-parameters for the random forest machine learning algorithm and twelve 2D molecular descriptors selected by recursive feature elimination with a 5-fold cross-validation process. The predictive capacity of the QSAR model was further assessed using an external test set and enrichment study, confirming its high predictivity. The practical applicability of our final QSAR model was demonstrated through virtual screening of the DrugBank database. This revealed two FDA-approved drugs (isavuconazole and cabozantinib) as potential OX1R ligands, confirmed by radiolabeled OX1R binding assays. To our best knowledge, this study represents the first report of highly predictive QSAR models on a large comprehensive dataset of diverse OX1R ligands, which should prove useful for the discovery and design of new compounds targeting this receptor.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100040"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000404/pdfft?md5=43fcbc13b8cafbb292e0ad47efa38a38&pid=1-s2.0-S2949747723000404-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139024429","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":"Prediction of 19F NMR chemical shift by machine learning","authors":"Yao Li ,&nbsp;Wen-Shuo Huang ,&nbsp;Li Zhang ,&nbsp;Dan Su ,&nbsp;Haoran Xu ,&nbsp;Xiao-Song Xue","doi":"10.1016/j.aichem.2024.100043","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100043","url":null,"abstract":"<div><p>Fluorine-19 (¹⁹F) is a nucleus of great importance in the field of Nuclear Magnetic Resonance (NMR) spectroscopy due to its high receptivity and wide chemical shift dispersion. ¹⁹F NMR plays crucial roles in both organic synthesis and biomedicine. Herein, a machine learning-based comprehensive ¹⁹F NMR chemical shift prediction model was established based on the experimental ¹⁹F NMR dataset from the book by Dolbier and the open NMR database nmrshiftdb2. Fluorine radical SMILES (Fr-SMILES) that reflected the fluorine chemical equivalence, was designed as the representation of fluorine in the molecule. Model trained with the graph convolution network (GCN) algorithm gave a low mean absolute error (MAE) of 3.636 ppm on the testing set. This model exhibits broad applicability and can effectively predict ¹⁹F NMR shifts for a wide range of organic fluorine molecules. We believe that the current work will provide a powerful tool for not only predicting ¹⁹F NMR shifts but also aiding in the analysis and identification of these shifts in diverse organic fluorine compounds. An online prediction platform was constructed based on the current model, which can be found at <span>https://fluobase.cstspace.cn/fnmr</span><svg><path></path></svg>.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100043"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000010/pdfft?md5=264d1a1fb39301258e870d87dfd75fce&pid=1-s2.0-S2949747724000010-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139107524","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":"2024-06-01","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":"Emerging technologies for drug repurposing: Harnessing the potential of text and graph embedding approaches","authors":"Xialan Dong,&nbsp;Weifan Zheng","doi":"10.1016/j.aichem.2024.100060","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100060","url":null,"abstract":"<div><p>Drug repurposing is an approach to identifying new uses for existing drugs, where advanced computational methods, such as text and graph embedding techniques, are playing an ever-increasing role. This review provides a timely overview of these embedding methods for drug repurposing and discusses their integration with machine learning. Text embedding techniques, such as Word2Vec, FastText, BERT, and Doc2Vec, enable the analysis of biomedical literature and clinical data to discover potential drug-disease relationships. These methods convert textual data into numerical representations, allowing for similarity calculations and predictive modeling. Several successful applications of text embedding for drug repurposing are highlighted. In addition, graph embedding methods, such as Node2Vec and GraphSAGE, are being employed to convert complex biological knowledge graphs into vector representations. These representations facilitate various network analysis tasks, including predicting drug-target interactions and identifying hidden associations between drugs and diseases. Case studies in both technologies demonstrate their effectiveness in drug repurposing. The advantages and limitations of both text and graph embedding technologies, and their complementarity with traditional structure-based approaches have been discussed. Finally, text and graph embedding methods can be employed in conjunction with traditional approaches of computational methods, which can offer a promising path to identifying novel drug repurposing opportunities, particularly for rare diseases.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100060"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000186/pdfft?md5=181c1bd52e69ed59bfcbf5f5691c8a0d&pid=1-s2.0-S2949747724000186-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140181159","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":"QuantumBound – Interactive protein generation with one-shot learning and hybrid quantum neural networks","authors":"Eric Paquet ,&nbsp;Farzan Soleymani ,&nbsp;Gabriel St-Pierre-Lemieux ,&nbsp;Herna Lydia Viktor ,&nbsp;Wojtek Michalowski","doi":"10.1016/j.aichem.2023.100030","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100030","url":null,"abstract":"<div><p>This paper presents a new approach for protein generation based on one-shot learning and hybrid quantum neural networks. Given a single protein complex, the system learns how to predict the remaining unknown properties, without resorting to autoregression, from the physicochemical properties of the receptor and a prior on the physicochemical properties of the ligand. In contrast with other approaches, QuantumBound learns from a single instance, not from a large dataset, as is common in deep learning. By knowing half of the properties of the ligand, the system can predict the remaining half with an average relative error of 1.43% for a dataset consisting of one hundred and twenty Covid-19 spikes complexes. To the best of our knowledge, this is the first time that one-shot learning and hybrid quantum computing have been applied to protein generation.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100030"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000301/pdfft?md5=7d7af911816c956f9e7248de8a335e1a&pid=1-s2.0-S2949747723000301-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138489573","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":"AI's role in pharmaceuticals: Assisting drug design from protein interactions to drug development","authors":"Solene Bechelli ,&nbsp;Jerome Delhommelle","doi":"10.1016/j.aichem.2023.100038","DOIUrl":"10.1016/j.aichem.2023.100038","url":null,"abstract":"<div><p>Developing new pharmaceutical compounds is a lengthy, costly, and intensive process. In recent years, the development of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) models has drawn considerable interest in drug discovery. In this review, we discuss recent advances in the field and show how these methods can be leveraged to assist each stage of the drug discovery process. After discussing recent technical progress in the encoding of chemical information via fingerprinting and the emergence of graph-based and generative models, we examine all types of interactions, including drug-target interactions, protein-protein interactions, protein-peptide interactions, and nucleic acid-based interactions. Furthermore, we discuss recent advances enabled by DL models for the prediction of ADMET (Absorption, Distribution, Metabolism, Elimination, Toxicity) properties and of solubility. We also review applications that have emerged in the past two years with the development of models, for instance, on SARS-CoV-2 inhibitors and highlight outstanding challenges.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100038"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000386/pdfft?md5=62dd3ca63edeb03fc522f745a7dce425&pid=1-s2.0-S2949747723000386-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139014049","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}