[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122203-en":3,"doc-seo-122203-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122203,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",6,"Technology","ROBERT - Bridging the Gap Between Machine Learning and Chemistry","Integration of machine learning into human society is advancing sustainability through digitalized protocols, yet a major adoption gap prevents widespread use of ML procedures in computational and experimental chemistry. Despite the availability of many toolkits, implementation remains difficult for chemists, especially those lacking cheminformatics experience. This work presents ROBERT, an accessible open-source software that yields expert-level results across supervised regression and classification benchmarks, supports workflows from SMILES, and enables discovery of luminescent Pd complexes using a small dataset.","Wiley Interdisciplinary Reviews: Computational Molecular Science  \nSOFTWARE FOCUS  OPEN ACCESS    \nROBERT: Bridging the Gap Between Machine Learning and Chemistry  \nDavid Dalmau  | Juan V. Alegre-Requena   \nDepartamento de Química Inorgánica, Instituto de Síntesis Química y Catálisis Homogénea (ISQCH), CSIC-Universidad de Zaragoza, Zaragoza, Spain Correspondence: Juan V. Alegre-Requena ([jv.alegre@csic.es](jv.alegre@csic.es))  \nReceived: 9 March 2024 | Revised: 10 June 2024 | Accepted: 19 June 2024  \nFunding: Juan V. Alegre-Requena and David Dalmau acknowledge Gobierno de Aragón-Fondo Social Europeo (Research Groups E07_23R and E17_23R) and the State Research Agency of Spain (MCIN/AEI/10.13039/501100011033/FEDER, UE) for financial support (IJC2020-044217-I, PID2022-140159NA-I00, and PID2019-106394GB-I00). Juan V. Alegre-Requena and David Dalmau acknowledge the computing resources at the Galicia Supercomputing Center, CESGA, including access to the FinisTerrae supercomputer and the Drago cluster facility ofSGAI-CSIC. David Dalmau thanks Gobierno de Aragón-FSE fora PhD fellowship (2021–2025) .  \nKeywords: automation | cheminformatics | machine learning | reproducibility | workflows  \nABSTRACT  \nBeyond addressing technological demands, the integration of machine learning (ML) into human societies has also promoted sustainability through the adoption of digitalized protocols. Despite these advantages and the abundance of available toolkits, a substantial implementation gap is preventing the widespread incorporation of ML protocols into the computational and experimental chemistry communities. In this work, we introduce ROBERT, a software carefully crafted to make ML more accessible to chemists of all programming skill levels, while achieving results comparable to those of field experts. We conducted benchmarking using six recent ML studies in chemistry containing from 18 to 4149 entries. Furthermore, we demonstrated the program's ability to initiate workflows directly from SMILES strings, which simplifies the generation of ML predictors for common chemistry problems. To assess ROBERT's practicality in real-life scenarios, we employed it to discover new luminescent Pd complexes with a modest dataset of 23 points, a frequently encountered scenario in experimental studies.  \n1 | Introduction  \nThe world is witnessing a growing interest in applying machine learning (ML) to everyday tasks, primarily driven by substantial savings in time, effort, and resources. Its integration not only fulfills technological needs but also fosters sustainability through the adoption of digitalized procedures, yielding important benefits for a more environmentally conscious future. In particular, the integration of ML in chemistry has opened up new avenues for exploring chemical space and predicting properties of molecules and reaction outcomes. This has led to significant advances in fields such as drug discovery [1–5], materials  \nscience [6–9], chemical synthesis [10–17], and catalyst discovery [18–24], among others.  \nAutomated ML workflows in chemistry have also become increasingly popular, enabling researchers to predict outcomes efficiently and accurately [25–28]. Numerous packages and tools are available, including molSimplify [29], pycaret [30], ChemML [31], Chemprop [32], DeepChem [33], Chainer Chemistry [34], Lazy Predict [35], and PREFER [36] . However, despite the increasing interest in this field, there is a vast community ofchemists that possess little experience in cheminformatics and find these ML tools impossible to include as part of their research.  \n\n| Edited by: Peter R. Schreiner, Editor-in-Chief |\n| --- |\n| This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.\u003Cbr>© 2024 The Author(s). WIREs Computational M","cbCaisH2CkThPc3U","https://ap.wps.com/l/cbCaisH2CkThPc3U","pdf",4527474,1,9,"English","en",105,"# Introduction\n## Applications of ML in chemistry\n## Challenges: tooling access and reproducibility\n# Overview of ROBERT\n## Software purpose and supported tasks\n## Example workflow inputs and use cases","[{\"question\":\"What problem does ROBERT address in chemical machine learning?\",\"answer\":\"It targets the implementation gap that limits adoption of ML protocols in computational and experimental chemistry, especially for chemists with little cheminformatics experience.\"},{\"question\":\"What kinds of ML tasks can ROBERT run?\",\"answer\":\"ROBERT supports supervised regression and classification workflows for chemistry-related prediction problems.\"},{\"question\":\"How does ROBERT help with workflow creation and practical usability?\",\"answer\":\"It can initiate workflows directly from SMILES strings and can generate ML predictors from common chemistry inputs with results comparable to field experts.\"}]","ROBERT - Bridging the Gap Between Machine Learning and Chemistry | PDF",1785809341,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"robert-bridging-the-gap-between-machine-learning-and-chemistry","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/robert-bridging-the-gap-between-machine-learning-and-chemistry/122203/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does ROBERT address in chemical machine learning?","Question",{"text":75,"@type":76},"It targets the implementation gap that limits adoption of ML protocols in computational and experimental chemistry, especially for chemists with little cheminformatics experience.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What kinds of ML tasks can ROBERT run?",{"text":80,"@type":76},"ROBERT supports supervised regression and classification workflows for chemistry-related prediction problems.",{"name":82,"@type":73,"acceptedAnswer":83},"How does ROBERT help with workflow creation and practical usability?",{"text":84,"@type":76},"It can initiate workflows directly from SMILES strings and can generate ML predictors from common chemistry inputs with results comparable to field experts.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]