Digital discovery最新文献

{"title":"Artificial intelligence approaches for anti-addiction drug discovery.","authors":"Dong Chen, Jian Jiang, Nicole Hayes, Zhe Su, Guo-Wei Wei","doi":"10.1039/d5dd00032g","DOIUrl":"10.1039/d5dd00032g","url":null,"abstract":"<p><p>Drug addiction remains a complex global public health challenge, with traditional anti-addiction drug discovery hindered by limited efficacy and slow progress in targeting intricate neurochemical systems. Advanced algorithms within artificial intelligence (AI) present a transformative solution that boosts both speed and precision in therapeutic development. This review examines how artificial intelligence serves as a crucial element in developing anti-addiction medications by targeting the opioid system along with dopaminergic and GABAergic systems, which are essential in addiction pathology. It identifies upcoming trends promising in studying less-researched addiction-linked systems through innovative general-purpose drug discovery techniques. AI holds the potential to transform anti-addiction research by breaking down conventional limitations, which will enable the development of superior treatment methods.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" ","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121519","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":"MOFChecker: a package for validating and correcting metal-organic framework (MOF) structures.","authors":"Xin Jin, Kevin Maik Jablonka, Elias Moubarak, Yutao Li, Berend Smit","doi":"10.1039/d5dd00109a","DOIUrl":"10.1039/d5dd00109a","url":null,"abstract":"<p><p>Metal-organic frameworks are promising porous materials for applications like gas adsorption, separation, transportation, and photocatalysis, but their large-scale computational screening requires high-quality, computation-ready structural data. Existing databases often contain errors due to experimental limitations, including inaccurately determined hydrogen positions, atomic overlaps, and missing components. We introduce MOFChecker to address these issues, providing tools for duplicate detection, geometric and charge error checking, and structure correction. Some errors can be systematically corrected through atomic adjustments on structures in the database, including deleting duplicated structures and adding missing hydrogen atoms, counterions, and linkers. Evaluation of established MOF databases, like the CoRE2014 database, indicates that 38% of structures contain significant errors, highlighting the importance of MOFChecker in ensuring accurate structural data for subsequent density functional theory (DFT) optimizations and computational studies. This work aims to enhance the reliability of MOF databases for high-throughput screening and practical applications.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" ","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12091091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129560","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":"Feature vectorization of microphase-separated structures in polymeric materials using dissipative particle dynamics and persistent homology for machine learning applications†","authors":"Yukito Higashi, Koji Okuwaki, Yuji Mochizuki, Tsuyohiko Fujigaya and Koichiro Kato","doi":"10.1039/D4DD00376D","DOIUrl":"https://doi.org/10.1039/D4DD00376D","url":null,"abstract":"<p >Recently, materials informatics (MI) has gained attention as an efficient approach for materials development. However, its application to polymers has been limited owing to the complexity and significance of the higher-order structures unique to these materials. This study focuses on microphase-separated structures, among the higher-order structures, as they influence many functional polymeric materials that support modern society. To implement MI that accounts for specific higher-order structures, such as microphase-separated structures, these structures must be quantified and converted into features. This approach addresses a gap in current materials informatics, in which traditional methods do not adequately account for the complex structures of polymers. Persistent homology (PH), a topological data analysis method, was used to extract features from the microphase-separated structures of polymeric materials. A coarse-grained simulation method known as dissipative particle dynamics (DPD) was used to generate the microphase-separated structures for PH analysis. The method was validated using electrolyte membranes for fuel cells, where microphase-separated structures are critical. Topological feature extraction was successfully performed on Nafion™ and its analogs, Aquivion® and Flemion™. Additionally, the correlation between the extracted features and proton conductivity was analyzed using unsupervised machine learning, which indicated that these features can be used to predict proton conductivity. The combination of DPD and PH can effectively convert microphase-separated structures into features. This method may be applicable to a wide range of polymeric materials influenced by microphase-separated structures, as it is not limited to proton exchange membranes or proton conductivity. This research marks a significant step toward advancing polymer informatics by incorporating the microphase-separated structures of polymers.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 5","pages":" 1339-1351"},"PeriodicalIF":6.2,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d4dd00376d?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944075","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":"Building workflows for an interactive human-in-the-loop automated experiment (hAE) in STEM-EELS†","authors":"Utkarsh Pratiush, Kevin M. Roccapriore, Yongtao Liu, Gerd Duscher, Maxim Ziatdinov and Sergei V. Kalinin","doi":"10.1039/D5DD00033E","DOIUrl":"https://doi.org/10.1039/D5DD00033E","url":null,"abstract":"<p >Exploring the structural, chemical, and physical properties of matter on the nano- and atomic scales has become possible with the recent advances in aberration-corrected electron energy-loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). However, the current paradigm of STEM-EELS relies on the classical rectangular grid sampling, in which all surface regions are assumed to be of equal <em>a priori</em> interest. However, this is typically not the case for real-world scenarios, where phenomena of interest are concentrated in a small number of spatial locations, such as interfaces, structural and topological defects, and multi-phase inclusions. One of the foundational problems is the discovery of nanometer- or atomic-scale structures having specific signatures in EELS spectra. Herein, we systematically explore the hyperparameters controlling deep kernel learning (DKL) discovery workflows for STEM-EELS and identify the role of the local structural descriptors and acquisition functions in experiment progression. In agreement with the actual experiment, we observe that for certain parameter combinations the experiment path can be trapped in the local minima. We demonstrate the approaches for monitoring the automated experiment in the real and feature space of the system and knowledge acquisition of the DKL model. Based on these, we construct intervention strategies defining the human-in-the-loop automated experiment (hAE). This approach can be further extended to other techniques including 4D STEM and other forms of spectroscopic imaging. The hAE library is available on Github at https://github.com/utkarshp1161/hAE/tree/main/hAE.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 5","pages":" 1323-1338"},"PeriodicalIF":6.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00033e?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944056","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":"Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials.","authors":"Maximilian X Tiefenbacher, Brigitta Bachmair, Cheng Giuseppe Chen, Julia Westermayr, Philipp Marquetand, Johannes C B Dietschreit, Leticia González","doi":"10.1039/d5dd00044k","DOIUrl":"https://doi.org/10.1039/d5dd00044k","url":null,"abstract":"<p><p>Excited-state nonadiabatic simulations with quantum mechanics/molecular mechanics (QM/MM) are essential to understand photoinduced processes in explicit environments. However, the high computational cost of the underlying quantum chemical calculations limits its application in combination with trajectory surface hopping methods. Here, we use FieldSchNet, a machine-learned interatomic potential capable of incorporating electric field effects into the electronic states, to replace traditional QM/MM electrostatic embedding with its ML/MM counterpart for nonadiabatic excited state trajectories. The developed method is applied to furan in water, including five coupled singlet states. Our results demonstrate that with sufficiently curated training data, the ML/MM model reproduces the electronic kinetics and structural rearrangements of QM/MM surface hopping reference simulations. Furthermore, we identify performance metrics that provide robust and interpretable validation of model accuracy.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" ","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12060776/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144059995","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":"The Citizen Data Science program at Dow†","authors":"Kyle Andrews, Steven Arturo, Matt Benedict, Birgit Braun, Brian Clark, Simon Cook, Jaime Curtis-Fisk, Fabio D’Ottaviano, Tim Licquia, Peter Margl, Jonathan Moore, Lynette Naler, Parth Singh, Alix Schmidt, Anatoliy Sokolov, John Talbert and James Wade","doi":"10.1039/D5DD00002E","DOIUrl":"https://doi.org/10.1039/D5DD00002E","url":null,"abstract":"<p >We present the Citizen Data Science (CDS) program, a data literacy program aimed at a Research and Development (R&amp;D)/Technical Service and Development (TS&amp;D) population from a heterogeneous background of traditional disciplines such as chemistry, materials science, engineering and others. The CDS program aims to facilitate the culture change required for maximizing researcher productivity and wellbeing by equipping every researcher with the skills to best manage, analyze, and communicate their data, enabling them to thrive in R&amp;D/TS&amp;D organizations that themselves are going through profound structural transformation induced by the pressures of digitalization. The Dow CDS program is going through its fourth year of implementation and improvement; we share the program and our learnings in the hope that they may be useful to other researchers in the materials development and adjacent spaces.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 5","pages":" 1124-1133"},"PeriodicalIF":6.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00002e?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943992","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":"High-throughput robotic colourimetric titrations using computer vision†","authors":"Yuan Li, Biplab Dutta, Qi Jie Yeow, Rob Clowes, Charlotte E. Boott and Andrew I. Cooper","doi":"10.1039/D4DD00334A","DOIUrl":"https://doi.org/10.1039/D4DD00334A","url":null,"abstract":"<p >A high-throughput (HTE) robotic colourimetric titration workstation was developed using a commercial liquid handling robot (Opentrons OT-2) and computer vision-based analysis. While designed for multiple titration applications, hydrogen peroxide (H<small>₂</small>O<small>₂</small>) determination serves as the most elaborate and well-characterized demonstration of its capabilities. Specifically, potassium permanganate (KMnO<small>₄</small>) redox titration was employed to quantify the hydrogen peroxide (H<small>₂</small>O<small>₂</small>) concentration, leveraging the distinct colourimetric transition from colourless to pale pink at the titration endpoint. To monitor this colour change, a webcam was installed on the OT-2 pipette mount, capturing real-time titration progress. Image analysis was enhanced through VGG-augmented UNet for segmentation and the CIELab colour model, ensuring robust and reproducible detection of subtle colour changes. The sensitivity test of the computer vision-aided colour analysis was strongly correlated to UV-vis spectroscopy (<em>R</em><small>²</small> = 0.9996), with a good linear dynamic range at low concentrations. The analytical accuracy of this workstation was ±11.9% in a 95% confidence interval and its corresponding absolute concentration difference was only 0.50 mM. To validate its real-world applicability, this workstation was first deployed to monitor the photoproduction of H<small>₂</small>O<small>₂</small> over a conjugated polymer photocatalyst, DE7. In addition to performing redox titrations, we demonstrated that this workstation can also be used for acid–base titration and complexometric titration, capturing a diverse range of colour changes.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 5","pages":" 1276-1283"},"PeriodicalIF":6.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d4dd00334a?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944052","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":"Active and transfer learning with partially Bayesian neural networks for materials and chemicals†","authors":"Sarah I. Allec and Maxim Ziatdinov","doi":"10.1039/D5DD00027K","DOIUrl":"https://doi.org/10.1039/D5DD00027K","url":null,"abstract":"<p >Active learning, an iterative process of selecting the most informative data points for exploration, is crucial for efficient characterization of materials and chemicals property space. Neural networks excel at predicting these properties but lack the uncertainty quantification needed for active learning-driven exploration. Fully Bayesian neural networks, in which weights are treated as probability distributions inferred <em>via</em> advanced Markov Chain Monte Carlo methods, offer robust uncertainty quantification but at high computational cost. Here, we show that partially Bayesian neural networks (PBNNs), where only selected layers have probabilistic weights while others remain deterministic, can achieve accuracy and uncertainty estimates on active learning tasks comparable to fully Bayesian networks at lower computational cost. Furthermore, by initializing prior distributions with weights pre-trained on theoretical calculations, we demonstrate that PBNNs can effectively leverage computational predictions to accelerate active learning of experimental data. We validate these approaches on both molecular property prediction and materials science tasks, establishing PBNNs as a practical tool for active learning with limited, complex datasets.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 5","pages":" 1284-1297"},"PeriodicalIF":6.2,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00027k?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944053","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":"Large language models for knowledge graph extraction from tables in materials science","authors":"Max Dreger, Kourosh Malek and Michael Eikerling","doi":"10.1039/D4DD00362D","DOIUrl":"https://doi.org/10.1039/D4DD00362D","url":null,"abstract":"<p >Research in materials science increasingly harnesses machine learning (ML) models. These models are trained with experimental or theoretical data, the quality of their output hinges on the data's quantity and quality. Improving data quality and accessibility necessitates advanced data management solutions. Today, data are often stored in non-standardized table formats that lack interoperability, accessibility and reusability. To address this issue, we present a semi-automated data ingestion pipeline that transforms R&amp;D tables into knowledge graphs. Utilizing large language models and rule-based feedback loops, our pipeline transforms tabular data into graph structures. The proposed process consists of entity recognition and relationship extraction. It facilitates better data interoperability and accessibility, by streamlining data integration from various sources. The pipeline is integrated into a platform harboring a graph database as well as semantic search capabilities.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 5","pages":" 1221-1231"},"PeriodicalIF":6.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d4dd00362d?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944048","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":"Predicting the excited-state properties of crystalline organic semiconductors using GW+BSE and machine learning†","authors":"Siyu Gao, Yiqun Luo, Xingyu Liu and Noa Marom","doi":"10.1039/D4DD00396A","DOIUrl":"https://doi.org/10.1039/D4DD00396A","url":null,"abstract":"<p >Excited-state properties of crystalline organic semiconductors are key to organic electronic device applications. Machine learning (ML) models capable of predicting these properties could significantly accelerate materials discovery. We use the sure-independence-screening-and-sparsifying-operator (SISSO) ML algorithm to generate models to predict the first singlet excitation energy, which corresponds to the optical gap, the first triplet excitation energy, the singlet–triplet gap, and the singlet exciton binding energy of organic molecular crystals. To train the models we use the “PAH101” dataset of many-body perturbation theory calculations within the GW approximation and Bethe–Salpeter equation (GW+BSE) for 101 crystals of polycyclic aromatic hydrocarbons (PAHs). The best performing SISSO models yield predictions within about 0.2 eV of the GW+BSE reference values. SISSO models are selected based on considerations of accuracy and computational cost to construct materials screening workflows for each property. The screening targets are chosen to demonstrate typical use-cases relevant for organic electronic devices. We show that the workflows based on SISSO models can effectively screen out most of the materials that are not of interest and significantly reduce the number of candidates selected for further evaluation using computationally expensive excited-state theory.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 5","pages":" 1306-1322"},"PeriodicalIF":6.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d4dd00396a?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944055","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}