{"id":11837,"date":"2023-10-03T12:48:03","date_gmt":"2023-10-03T10:48:03","guid":{"rendered":"https:\/\/www.qsarlab.com\/?page_id=11837"},"modified":"2026-04-22T08:22:58","modified_gmt":"2026-04-22T06:22:58","slug":"tools","status":"publish","type":"page","link":"https:\/\/www.qsarlab.com\/en\/tools\/","title":{"rendered":"Produkty IT"},"content":{"rendered":"\t\t
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\r\n \r\n \r\n IT tools\r\n \r\n \r\n <\/i>\r\n <\/i>\r\n <\/i>\r\n <\/i>\r\n <\/i>\r\n <\/i>\r\n <\/i>\r\n <\/span>\r\n <\/h3>\r\n <\/div>\r\n<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t <\/div>\n <\/div>\n
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QSAR Lab’s research, development, and implementation activity focuses on developing a set of computer tools that enable a comprehensive assessment of the risks posed by new materials (including nanomaterials) to the natural environment and human health and life. Try the tools developed by our company.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t <\/div>\n <\/div>\n <\/div>\n <\/section>\n

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\r\n \r\n \r\n NAMs Database – all methods for hazard characterization in one place\r\n \r\n <\/span>\r\n
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NAMs.network is the first database of regulatory-relevant NAMs for assessing the safety of nanomaterials and chemicals. It is a comprehensive, easily accessible repository that collects all of the NAMs under EU and US regulations in one place<\/strong>, allowing you to extract state-of-the-art knowledge on different NAMs.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\"\"\u00a0 \u00a0Recently proposed & promising NAMs <\/span><\/p>

\"\"\u00a0 NAMs currently under validation or adaptation<\/span><\/span><\/p>

\"\"\u00a0 Already accepted & legally approved NAMs<\/span><\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\r\n \r\n \r\n Platform for Predicting Properties and Assessing Chemical Safety\r\n \r\n <\/span>\r\n
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QL Chemicals is a web-based platform for predicting physicochemical, environmental, and toxicological properties of chemical compounds, with a particular focus on organic substances. It leverages validated QSAR models and covers a broad range of endpoints across different chemical classes.<\/span><\/p>


\u2714 Physicochemical, environmental, and toxicological endpoints<\/span>
\u2714 Models for organic compounds, PFAS, POPs, and ionic liquids<\/span>
\u2714 Structure input via molecular editor or SMILES, with 3D optimization<\/span>
\u2714 Broad applicability domain covering over 6,000 compounds<\/span>
\u2714 User-friendly interface, reporting features, QMRF documentation, and Wallet-based payments<\/span>
\u2714 Transparent model documentation and reports supporting safety assessment<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\r\n \r\n \r\n Platform for Predicting Toxicity and Assessing the Safety of Advanced Materials\r\n \r\n <\/span>\r\n
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QLmaterials is a web-based platform for predicting the toxicity of advanced materials (IAM), including engineered nanomaterials (ENMs). It uses machine learning models built on curated experimental data to support early-stage safety assessment. The platform helps compare material variants, optimize experimental planning, and supports the Safe and Sustainable by Design (SSbD) approach.<\/span><\/p>


\u2714 Toxicological endpoints relevant to human health<\/span>
\u2714 Applicable to Innovative Advanced Materials (IAM), including ENMs<\/span>
\u2714 Comparison of material variants and support for selecting safer alternatives<\/span>
\u2714 Material definition based on key physicochemical properties<\/span>
\u2714 Intuitive prediction workflow without requiring ML expertise<\/span>
\u2714 Reports supporting safety documentation, compliant with QPRF and OECD standards<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\r\n \r\n \r\n QSAR Models for Predicting Nanoparticle Toxicity Alerts and Behavior\r\n \r\n <\/span>\r\n
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This is a unique online application, supported by machine learning (ML) algorithms, that enables the prediction of genotoxicity and mutagenicity alerts for TiO2 and SiO2 nanoparticles<\/strong>. In addition to information on potential toxicity, the application also provides data on the level of uncertainty with which the results should be treated \u2013 an innovation in QSAR models.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\"\"\u00a0 Regulatory relevant endpoints & tests<\/span><\/span><\/p>

\"\"\u00a0 \u00a0Reliable & accurate data aligned with EFSA recommendations<\/span><\/span><\/p>

\"\"\u00a0 Reducing time-consuming and costly experiments<\/span><\/span><\/span><\/p>

\"\"\u00a0 Simple and intuitive user-friendly interface<\/span><\/span><\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\r\n \r\n \r\n Framework for predictive models tailored to your needs\r\n \r\n <\/span>\r\n
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An intuitive platform ready to integrate with any predictive models.<\/b><\/span>
No need to build everything from scratch \u2013 we implement only the components you need, ensuring ease of use and fast deployment.<\/span><\/p>

The platform can be fully tailored to your company’s needs.<\/b><\/span>
We design the interface, features, and structure exactly as required \u2013 from the look and feel to specific modules and integrations. It can be deployed locally within your organization or hosted by us, with full technical support and ongoing updates.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Provide your data<\/strong>, <\/span>and we\u2019ll develop high-performance predictive models tailored to solve your problem.<\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t <\/div>\n <\/div>\n <\/div>\n <\/section>\n

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Already have models? <\/span><\/strong>We can integrate, optimize, and adapt <\/span>them into an intuitive, dedicated framework.<\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t <\/div>\n <\/div>\n <\/div>\n <\/section>\n \t\t

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The platform also serves as a model repository.<\/b><\/span>
If you\u2019d like to use ready-to-go models provided by our team, you can run one-time predictions using a token-based system. The results are saved to your account \u2013 always accessible and ready for further analysis.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\r\n \r\n \r\n Bioinformatics service supporting the design of molecular diagnostic tests\r\n \r\n <\/span>\r\n
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A unique web-based bioinformatics tool and service, powered by machine learning (ML) and artificial intelligence (AI), to help design and develop new molecular diagnostic tests<\/strong> for emerging or established diseases such as COVID-19. An innovative, experimentally validated algorithm optimizes virtual high-throughput screening (vHTS) and molecular docking of virtual peptide libraries consisting of all possible combinations of amino acid (AA) sequences for a given peptide length (4-12 AA).<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\"\"\u00a0 \u00a0 Library of over 25 billions peptide structures<\/span><\/p>

\"\"\u00a0 Molecular docking using force field<\/span><\/span><\/p>

\"\"\u00a0 \u00a0Reduction of cost, time and computational power<\/span><\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Model predicting BMD(L) values of multi-wall carbon nanotubes (MWCNT) in the context of AOP 173<\/h3>\r\n\t <\/div>\r\n\t<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t <\/div>\n <\/div>\n
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Model predicting the toxicity of nanoparticles towards the CHO-K1 cell line using SAPNet<\/h3>\r\n\t <\/div>\r\n\t<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t <\/div>\n <\/div>\n <\/div>\n <\/section>\n
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IT tools QSAR Lab’s research, development, and implementation activity focuses on developing a set of computer tools that enable a comprehensive assessment of the risks posed by new materials (including nanomaterials) to the natural environment and human health and life. Try the tools developed by our company. TRY NOW! TRY NOW! NAMs Database – all […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-11837","page","type-page","status-publish","hentry"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.qsarlab.com\/en\/wp-json\/wp\/v2\/pages\/11837","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.qsarlab.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.qsarlab.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.qsarlab.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.qsarlab.com\/en\/wp-json\/wp\/v2\/comments?post=11837"}],"version-history":[{"count":63,"href":"https:\/\/www.qsarlab.com\/en\/wp-json\/wp\/v2\/pages\/11837\/revisions"}],"predecessor-version":[{"id":14699,"href":"https:\/\/www.qsarlab.com\/en\/wp-json\/wp\/v2\/pages\/11837\/revisions\/14699"}],"wp:attachment":[{"href":"https:\/\/www.qsarlab.com\/en\/wp-json\/wp\/v2\/media?parent=11837"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}