Artificial intelligence chemistry最新文献_第10页

Evaluating point-prediction uncertainties in neural networks for protein-ligand binding prediction 评估神经网络在蛋白质配体结合预测中的点预测不确定性

Artificial intelligence chemistry Pub Date : 2023-06-01 DOI: 10.1016/j.aichem.2023.100004

Ya Ju Fan , Jonathan E. Allen , Kevin S. McLoughlin , Da Shi , Brian J. Bennion , Xiaohua Zhang , Felice C. Lightstone

{"title":"Evaluating point-prediction uncertainties in neural networks for protein-ligand binding prediction","authors":"Ya Ju Fan , Jonathan E. Allen , Kevin S. McLoughlin , Da Shi , Brian J. Bennion , Xiaohua Zhang , Felice C. Lightstone","doi":"10.1016/j.aichem.2023.100004","DOIUrl":"10.1016/j.aichem.2023.100004","url":null,"abstract":"<div><p>Neural Network (NN) models provide potential to speed up the drug discovery process and reduce its failure rates. The success of NN models requires uncertainty quantification (UQ) as drug discovery explores chemical space beyond the training data distribution. Standard NN models do not provide uncertainty information. Some methods require changing the NN architecture or training procedure, limiting the selection of NN models. Moreover, predictive uncertainty can come from different sources. It is important to have the ability to separately model different types of predictive uncertainty, as the model can take assorted actions depending on the source of uncertainty. In this paper, we examine UQ methods that estimate different sources of predictive uncertainty for NN models aiming at protein-ligand binding prediction. We use our prior knowledge on chemical compounds to design the experiments. By utilizing a visualization method we create non-overlapping and chemically diverse partitions from a collection of chemical compounds. These partitions are used as training and test set splits to explore NN model uncertainty. We demonstrate how the uncertainties estimated by the selected methods describe different sources of uncertainty under different partitions and featurization schemes and the relationship to prediction error.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 1","pages":"Article 100004"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/9f/25/nihms-1912151.PMC10426331.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10019861","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}

引用次数: 0

Reproducing the color with reformulated recipe 用重新配方再现颜色

Artificial intelligence chemistry Pub Date : 2023-06-01 DOI: 10.1016/j.aichem.2023.100003

Jinming Fan , Chao Qian , Shaodong Zhou

引用次数: 0

Machine learning modeling of the absorption properties of azobenzene molecules 偶氮苯分子吸收特性的机器学习建模

Artificial intelligence chemistry Pub Date : 2023-06-01 DOI: 10.1016/j.aichem.2023.100002

Valentin Stanev , Ryota Maehashi , Yoshimi Ohta , Ichiro Takeuchi

引用次数: 0

Starting the new journal of “Artificial Intelligence Chemistry” 创办新期刊《人工智能化学》

Artificial intelligence chemistry Pub Date : 2023-06-01 DOI: 10.1016/j.aichem.2023.100001

Jun Jiang , Song Wang , Shaul Mukamel

引用次数: 0

Probing the origin of higher efficiency of terphenyl phosphine over the biaryl framework in Pd-catalyzed C-N coupling: A combined DFT and machine learning study 在钯催化的C-N偶联中，三苯基膦在双芳基骨架上的高效性的来源：DFT和机器学习的结合研究

Artificial intelligence chemistry Pub Date : 2023-06-01 DOI: 10.1016/j.aichem.2023.100005

Qingfu Ye , Yu Zhao , Jun Zhu

{"title":"Probing the origin of higher efficiency of terphenyl phosphine over the biaryl framework in Pd-catalyzed C-N coupling: A combined DFT and machine learning study","authors":"Qingfu Ye , Yu Zhao , Jun Zhu","doi":"10.1016/j.aichem.2023.100005","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100005","url":null,"abstract":"<div><p>The Pd-catalyzed Buchwald–Hartwig coupling reaction is important in the construction of the C-N bond due to various applications in organic synthesis. Quantum chemical calculations are widely used in understanding reaction mechanisms whereas the machine learning method is extremely popular in recognizing the relationships of data. Here, we combine density functional theory calculations with the support vector regression method to probe the origin of the higher efficiency of terphenyl phosphine ligand over the biaryl counterpart in the Buchwald–Hartwig C-N coupling reaction. By quantum chemical calculations, the turnover frequency-determining transition states are located and ligand features are calculated with high accuracy. By machine learning, the relationship between the reaction barrier and ligand features has been examined. It is found that the interplay of the charge on the metal center, the cone angle of the ligands, and the Sterimol L parameters of the ligand determines the catalytic performance of the palladium catalysts with different phosphine ligands. Our findings could help experimental chemists to design the ligands for Pd-catalyzed C-N coupling reactions with high efficiency.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 1","pages":"Article 100005"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49817020","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}

引用次数: 0