Artificial intelligence chemistry最新文献_第10页

Machine learning approaches for the identification of ligands of the autophagy marker LC3 自噬标记LC3配体识别的机器学习方法

Artificial intelligence chemistry Pub Date : 2023-12-01 Epub Date: 2023-10-24 DOI: 10.1016/j.aichem.2023.100022

Laurent Soulère, Yves Queneau

{"title":"Machine learning approaches for the identification of ligands of the autophagy marker LC3","authors":"Laurent Soulère, Yves Queneau","doi":"10.1016/j.aichem.2023.100022","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100022","url":null,"abstract":"<div><p>The LC3 proteins play a crucial role in autophagy by participating to the formation of the autophagosome. Modulation of autophagy by molecular interference with LC3 proteins could help to understand this complex fundamental biological process and how it is involved in several pathologies. Identifying new LC3 ligands is a useful contribution to this aim. In the present study, we created a PubChem library of 749 compounds having a structure based on the central scaffold of novobiocin, a reported LC3A ligand. A robust, rapid and exhaustive algorithm was used for docking each compound of this database as ligands within the dihydronovobiocin binding site, providing a docking score. Remarkable reliability and consistency between docking scores and the reported binding efficiencies of known ligands was observed, validating the machine leaning protocol used in this study. Investigation of the binding mode of the ligands having the best docking score provides additional insights in possible mode of actions of the LC3 identified ligands.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100022"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000222/pdfft?md5=535de2ec95e92e677368af743f018ee2&pid=1-s2.0-S2949747723000222-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91987333","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

ML meets MLn: Machine learning in ligand promoted homogeneous catalysis ML与MLn相遇:配体促进均相催化的机器学习

Artificial intelligence chemistry Pub Date : 2023-12-01 Epub Date: 2023-07-11 DOI: 10.1016/j.aichem.2023.100006

Jonathan D. Hirst , Samuel Boobier , Jennifer Coughlan , Jessica Streets , Philippa L. Jacob , Oska Pugh , Ender Özcan , Simon Woodward

引用次数: 2

Development and application of in silico models to design new antibacterial 5-amino-4-cyano-1,3-oxazoles against colistin-resistant E. coli strains 5-氨基-4-氰基-1,3-恶唑抗粘菌素耐药大肠杆菌模型的建立及应用

Artificial intelligence chemistry Pub Date : 2023-12-01 Epub Date: 2023-11-09 DOI: 10.1016/j.aichem.2023.100024

Ivan Semenyuta, Diana Hodyna, Vasyl Kovalishyn, Bohdan Demydchuk, Maryna Kachaeva, Stepan Pilyo, Volodymyr Brovarets, Larysa Metelytsia

{"title":"Development and application of in silico models to design new antibacterial 5-amino-4-cyano-1,3-oxazoles against colistin-resistant E. coli strains","authors":"Ivan Semenyuta, Diana Hodyna, Vasyl Kovalishyn, Bohdan Demydchuk, Maryna Kachaeva, Stepan Pilyo, Volodymyr Brovarets, Larysa Metelytsia","doi":"10.1016/j.aichem.2023.100024","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100024","url":null,"abstract":"<div><p>Here we describe the results of QSAR analysis based on artificial neural networks, synthesis, activity evaluation and molecular docking of a number of 1,3-oxazole derivatives as anti-E. coli antibacterials. All developed QSAR models showed excellent statistics on training (with determination coefficient q<sup>2</sup> as 0.76 ± 0.01) and test samples (with q<sup>2</sup> as 0.78 ± 0.01). The models were successfully used to identify nine novel 5-amino-4-cyano-1,3-oxazoles with potential anti-E. coli activity. All nine 1,3-oxazoles with predicted high antibacterial potential showed different levels of anti- E. coli in vitro activity. 5-amino-4-cyano-1,3-oxazoles <strong>1</strong> and <strong>3</strong> showed the highest antibacterial activity on average from 17 to 27 mm against MDR, hemolytic MDR and ATCC 25922 <em>E. coli</em> colistin-resistant strains, respectively. The comparative docking analysis demonstrated a possible mechanism of the antibacterial action of the studied 1, 3-oxazoles <strong>1</strong> and <strong>3</strong> through inhibition of <em>E. coli</em> enoyl-ACP reductase (ENR) involved in the biosynthesis of bacterial fatty acids. The localization type is shown of 5-amino-4-cyano-1,3-oxazoles <strong>1</strong> and <strong>3</strong> into the <em>E. coli</em> ENR active site with estimated binding energy from − 10.1 to − 9.5 kcal/mol and hydrogen bonds formation with key amino acids similar to Triclosan. These facts confirm the validity of the hypothesis put forward about the potential antibacterial mechanism of 5-amino-4- cyano-1,3-oxazoles.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100024"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000246/pdfft?md5=c9085bc34142109bacab7efa22188c7f&pid=1-s2.0-S2949747723000246-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91987332","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

Metaheuristic optimisation of Gaussian process regression model hyperparameters: Insights from FEREBUS 高斯过程回归模型超参数的元启发式优化:来自FEREBUS的见解

Artificial intelligence chemistry Pub Date : 2023-12-01 Epub Date: 2023-10-29 DOI: 10.1016/j.aichem.2023.100021

Bienfait K. Isamura, Paul L.A. Popelier

{"title":"Metaheuristic optimisation of Gaussian process regression model hyperparameters: Insights from FEREBUS","authors":"Bienfait K. Isamura, Paul L.A. Popelier","doi":"10.1016/j.aichem.2023.100021","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100021","url":null,"abstract":"<div><p>FEREBUS is a Gaussian process regression (GPR) engine embedded in the large machinery of FFLUX, a novel machine learnt force field developed from scratch through several well-documented proof-of-concept studies. This package relies on the exploration and exploitation capabilities of metaheuristic algorithms (MAs) to carry out the global optimisation of GPR model hyperparameters (<span><math><mi>θ</mi></math></span>). However, because MAs employ different search mechanisms to scrutinise the hyperparameter space, their performance on a specific optimisation task can vary a lot from one technique to another. Herein, we report a series of carefully designed experiments aimed at evaluating the ability of ten metaheuristic algorithms to locate the optimal set of <span><math><mi>θ</mi></math></span> values. Selected optimisation techniques belong to four popular families of MAs, namely particle swarm optimisation (4), grey wolf optimisation (2), bat (2) and firefly (2) algorithms. Our calculations suggest that grey wolf optimisers (GWOs) achieve the best results on average. Furthermore, the RMSE(<span><math><mi>θ</mi></math></span>) cost function is confirmed to be an excellent guide for the selection of atomic GPR models. This work also briefly introduces an enhanced grey wolf optimiser called GWO-RUHL (Random Update of the Hierarchy Ladder), which accounts for the (so far omitted) natural desire of non-leader wolves to occupy high-ranked leadership positions in the pack. We demonstrate that GWO-RUHL achieves better results than the standard GWO in terms of both convergence speed and quality of solutions.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100021"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000210/pdfft?md5=b3d2985c50bf91347418f158a01005cc&pid=1-s2.0-S2949747723000210-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92061992","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

Discovery of novel CaMK-II inhibitor for the possible mitigation of arrhythmia through pharmacophore modelling, virtual screening, molecular docking, and toxicity prediction 通过药效团建模、虚拟筛选、分子对接和毒性预测，发现可能缓解心律失常的新型CaMK-II抑制剂

Artificial intelligence chemistry Pub Date : 2023-12-01 Epub Date: 2023-07-20 DOI: 10.1016/j.aichem.2023.100009

Niyati Parekh , Sarthak Lakhani , Ayushi Patel , Dhyanesh Oza , Bhumika Patel , Ruchi Yadav , Udit Chaube

{"title":"Discovery of novel CaMK-II inhibitor for the possible mitigation of arrhythmia through pharmacophore modelling, virtual screening, molecular docking, and toxicity prediction","authors":"Niyati Parekh , Sarthak Lakhani , Ayushi Patel , Dhyanesh Oza , Bhumika Patel , Ruchi Yadav , Udit Chaube","doi":"10.1016/j.aichem.2023.100009","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100009","url":null,"abstract":"<div><p>In the present research, a few well-known artificial intelligence tools were explored for efficient hit selection which could be further utilized for the discovery of CaMK-II inhibitors for the Treatment of arrhythmia. To achieve the desired goals pharmacophore modelling, database retrieval, molecular docking studies, and toxicity prediction were performed. Pharmacophore modelling was performed with the Pharmit open-source database which gave the features viz. Hydrogen Bond Donor, Hydrogen Bond Acceptor, and Hydrophobic. This pharmacophore is generated with the aid of the protein of CaMK-II (PDB ID: 2WEL) and co-crystallized ligand K88. Further, this generated pharmacophore was screened through the various Pharmit databases which include CHEMBL30, ChemDiv, ChemSpace, MCULE, MolPort, NCI Open Chemical Repository, Lab Network, and ZINC. Further, the top two hits from each database that has maximum similarity with the pharmacophore have been selected for the molecular docking and ADMET studies. Among, all the hits CHEMBL 1952032 showed good binding interactions with CaMK-II. Also, it was found to be non-toxic upon evaluation through the OSIRIS property explorer. In the future, it can be explored against the CaMK-II for the development of novel CaMK-II inhibitors which can be used for the mitigation of arrhythmia.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100009"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49764056","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: 1

Intelligent vision for the detection of chemistry glassware toward AI robotic chemists 面向人工智能机器人化学家的化学玻璃器皿检测智能视觉

Artificial intelligence chemistry Pub Date : 2023-12-01 Epub Date: 2023-09-20 DOI: 10.1016/j.aichem.2023.100016

Xiaogang Cheng , Shiyuan Zhu , Zhaocheng Wang , Chenxin Wang , Xin Chen , Qin Zhu , Linghai Xie

{"title":"Intelligent vision for the detection of chemistry glassware toward AI robotic chemists","authors":"Xiaogang Cheng , Shiyuan Zhu , Zhaocheng Wang , Chenxin Wang , Xin Chen , Qin Zhu , 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":"2023-12-01","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}

引用次数: 0

Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization 用最优神经元激活函数实现单神经网络的耦合阶数表示，无需非线性参数优化

Artificial intelligence chemistry Pub Date : 2023-12-01 Epub Date: 2023-08-15 DOI: 10.1016/j.aichem.2023.100013

Sergei Manzhos, Manabu Ihara

{"title":"Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization","authors":"Sergei Manzhos, 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":"2023-12-01","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}

引用次数: 3

Machine learning and robot-assisted synthesis of diverse gold nanorods via seedless approach 机器学习和机器人辅助合成各种金纳米棒的无籽方法

Artificial intelligence chemistry Pub Date : 2023-12-01 Epub Date: 2023-11-19 DOI: 10.1016/j.aichem.2023.100028

Oyawale Adetunji Moses , Mukhtar Lawan Adam , Zijian Chen , Collins Izuchukwu Ezeh , Hao Huang , Zhuo Wang , Zixuan Wang , Boyuan Wang , Wentao Li , Chensu Wang , Zongyou Yin , Yang Lu , Xue-Feng Yu , Haitao Zhao

{"title":"Machine learning and robot-assisted synthesis of diverse gold nanorods via seedless approach","authors":"Oyawale Adetunji Moses , Mukhtar Lawan Adam , Zijian Chen , Collins Izuchukwu Ezeh , Hao Huang , Zhuo Wang , Zixuan Wang , Boyuan Wang , Wentao Li , Chensu Wang , Zongyou Yin , Yang Lu , Xue-Feng Yu , Haitao Zhao","doi":"10.1016/j.aichem.2023.100028","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100028","url":null,"abstract":"<div><p>The challenge of data-driven synthesis of advanced nanomaterials can be minimized by using machine learning algorithms to optimize synthesis parameters and expedite the innovation process. In this study, a high-throughput robotic platform was employed to synthesize over 1356 gold nanorods with varying aspect ratios via a seedless approach. The developed models guided us in synthesizing gold nanorods with customized morphology, resulting in highly repeatable morphological yield with quantifiable structure-modulating precursor adjustments. The study provides insight into the dynamic relationships between key structure-modulating precursors and the structural morphology of gold nanorods based on the expected aspect ratio. The high-throughput robotic platform-fabricated gold nanorods demonstrated precise aspect ratio control when spectrophotometrically investigated and further validated with the transmission electron microscopy characterization. These findings demonstrate the potential of high-throughput robot-assisted synthesis and machine learning in the synthesis optimization of gold nanorods and aided in the development of models that can aid such synthesis of as-desired gold nanorods.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100028"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000283/pdfft?md5=8511642b616c7b56dec42d00c89c3ede&pid=1-s2.0-S2949747723000283-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138448082","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

Machine-learning-based virtual screening and ligand docking identify potent HIV-1 protease inhibitors 基于机器学习的虚拟筛选和配体对接识别有效的HIV-1蛋白酶抑制剂

Artificial intelligence chemistry Pub Date : 2023-12-01 Epub Date: 2023-09-01 DOI: 10.1016/j.aichem.2023.100014

Andrew K. Gao , Trevor B. Chen , Valentina L. Kouznetsova , Igor F. Tsigelny

{"title":"Machine-learning-based virtual screening and ligand docking identify potent HIV-1 protease inhibitors","authors":"Andrew K. Gao , Trevor B. Chen , Valentina L. Kouznetsova , 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 pIC<sub>50</sub> 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 pIC<sub>50</sub> 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 pIC<sub>50</sub> of at least 7.301 (IC<sub>50</sub> <= 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 IC<sub>50</sub> 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":"2023-12-01","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}

引用次数: 0

Robust design strategy using a scaffold based Turing machine model--- Application to PDI based dyes 基于支架的图灵机模型鲁棒设计策略——在PDI染料中的应用

Artificial intelligence chemistry Pub Date : 2023-12-01 Epub Date: 2023-11-07 DOI: 10.1016/j.aichem.2023.100023

Feng Wang , Vladislav Vasilyev

{"title":"Robust design strategy using a scaffold based Turing machine model--- Application to PDI based dyes","authors":"Feng Wang , Vladislav Vasilyev","doi":"10.1016/j.aichem.2023.100023","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100023","url":null,"abstract":"<div><p>This study turns the design and screen of new compounds into a computer integer crunch of the control arrays using a scaffold based Turing machine model. If small organic fragments are stored in a fragment database (FDB) in which each fragment is labelled by an integer in an array, the position and frequency of the integer control how the fragment clicks on a scaffold (template compound). This method can robustly screen a large number of candidate fragments for solar cells and other applications such as drug design with minimal human assistance. As a proof of concept, we consider terminal imide substituents on the core perylene diimide (PDI) to develop PDI derivatives capable of absorbing UV–vis light for solar cell applications. Time dependent-density functional theory (TD-DFT) method was employed in the calculations. When the imide substituents are electron donors such as azobenzene (DPI-7), they produce a larger bathochromic shift (Δλ<sub>max</sub>) from the core DPI band position. The UV–vis absorption transitions of these DPI derivatives have more charge transfer (CT) character, as the highest occupied molecular orbitals (HOMO) are located on the fragments rather than the core DPI region. Our study presents a robust and efficient high-performance organic dye screen design strategy, and further research in DPI-based solar cell design will focus on promoting the HOMO to LUMO transitions of the optical spectra.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100023"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000234/pdfft?md5=b6b1b440208372f0df0d3764b52bd55d&pid=1-s2.0-S2949747723000234-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134657401","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